Publics’ views on ethical challenges of artificial intelligence: a scoping review

Vol.:(0123456789) 1 3 AI and Ethics https://doi.org/10.1007/s43681-023-00387-1 REVIEW Publics’ views onethical challenges ofartificial intelligence: ascoping review HelenaMachado1 · SusanaSilva2 · LauraNeiva3 Received: 8 October 2023 / Accepted: 16 November 2023 © The Author(s) 2023 Abstract This scoping review examines the research landscape about publics’ views on the ethical challenges of AI. To elucidate how the concerns voiced by the publics are translated within the research domain, this study scrutinizes 64 publications sourced from PubMed® and Web of Science™. The central inquiry revolves around discerning the motivations, stakeholders, and ethical quandaries that emerge in research on this topic. The analysis reveals that innovation and legitimation stand out as the primary impetuses for engaging the public in deliberations concerning the ethical dilemmas associated with AI technologies. Supplementary motives are rooted in educational endeavors, democratization initiatives, and inspirational pursuits, whereas politicization emerges as a comparatively infrequent incentive. The study participants predominantly comprise the general public and professional groups, followed by AI system developers, industry and business managers, students, scholars, consumers, and policymakers. The ethical dimensions most commonly explored in the literature encompass human agency and oversight, followed by issues centered on privacy and data governance. Conversely, topics related to diversity, nondiscrimination, fairness, societal and environmental well-being, technical robustness, safety, transparency, and accountability receive comparatively less attention. This paper delineates the concrete operationalization of calls for public involvement in AI governance within the research sphere. It underscores the intricate interplay between ethical concerns, public involvement, and societal structures, including political and economic agendas, which serve to bolster technical proficiency and affirm the legitimacy of AI development in accordance with the institutional norms that underlie responsible research practices. Keywords Artificial intelligence· Ethics· Public involvement· Publics’ views· Responsible research 1 Introduction Current advances in the research, development, and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics that is accompanied by calls for AI technology to be democratically accountable and trustworthy from the publics’1 perspective [1–5]. Consequently, several ethics guidelines for AI have been released in recent years. As of early 2020, there were 167 AI ethics guidelines documents around the world [6]. Organizations such as the European Commission (EC), the Organization for Economic Co-operation and Development (OECD), * Helena Machado [email protected] 1 Department ofSociology, Institute forSocial Sciences, University ofMinho, Braga, Portugal 2 Department ofSociology andCentre forResearch inAnthropology (CRIA), Institute forSocial Sciences, University ofMinho, Braga, Portugal 3 Institute forSocial Sciences, Communication andSociety Research Centre (CECS), University ofMinho, Braga, Portugal 1 In this article, we will employ the term "publics" rather than the singular "public" to delineate our viewpoint concerning public participation in AI. Our option is meant to acknowledge that there are no uniform, monolithic viewpoints or interests. From our perspective, the term "publics" allows for a more nuanced understanding of the various groups, communities, and individuals who may have different attitudes, beliefs, and concerns regarding AI. This choice may differ from the terminology employed in the referenced literature. AI and Ethics 1 3 and the United Nations Educational, Scientific and Cultural Organization (UNESCO) recognize that public participation is crucial for ensuring the responsible development and deployment of AI technologies,2 emphasizing the importance of inclusivity, transparency, and democratic processes to effectively address the societal implications of AI [11, 12]. These efforts were publicly announced as aiming to create a common understanding of ethical AI development and foster responsible practices that address societal concerns while maximizing AI’s potential benefits [13, 14]. The concept of human-centric AI has emerged as a key principle in many of these regulatory initiatives, with the purposes of ensuring that human values are incorporated into the design of algorithms, that humans do not lose control over automated systems, and that AI is used in the service of humanity and the common good to improve human welfare and human rights [15]. Using the same rationale, the opacity and rapid diffusion of AI have prompted debate about how such technologies ought to be governed and which actors and values should be involved in shaping governance regimes [1, 2]. While industry and business have traditionally tended to be seen as having no or little incentive to engage with ethics or in dialogue, AI leaders currently sponsor AI ethics [6, 16, 17]. However, some concerns call for ethics, public participation, and human-centric approaches in areas such as AI with high economic and political importance to be used within an instrumental rationale by the AI industry. A growing corpus of critical literature has conceived the development of AI ethics as efforts to reduce ethics to another form of industrial capital or to coopt and capture researchers as part of efforts to control public narratives [12, 18]. According to some authors, one of the reasons why ethics is so appealing to many AI companies is to calm critical voices from the publics; therefore, AI ethics is seen as a way of gaining or restoring trust, credibility and support, as well as legitimation, while criticized practices are calmed down to maintain the agenda of industry and science [12, 17, 19, 20]. Critical approaches also point out that despite regulatory initiatives explicitly invoking the need to incorporate human values into AI systems, they have the main objective of setting rules and standards to enable AI-based products and services to circulate in markets [20–22] and might serve to avoid or delay binding regulation [12, 23]. Other critical studies argue that AI ethics fails to mitigate the racial, social, and environmental damage of AI technologies in any meaningful sense [24] and excludes alternative ethical practices [25, 26]. As explained by Su [13], in a paper that considers the promise and perils of international human rights in AI governance, while human rights can serve as an authoritative source for holding AI developers accountable, its application to AI governance in practice shows a lack of effectiveness, an inability to effect structural change, and the problem of cooptation. In a value analysis of AI national strategies, Wilson [5] concludes that the publics are primarily cast as recipients of AI’s abstract benefits, users of AI-driven services and products, a workforce in need of training and upskilling, or an important element for thriving democratic society that unlocks AI's potential. According to the author, when AI strategies articulate a governance role for the publics, it is more like an afterthought or rhetorical gesture than a clear commitment to putting “society-in-the-loop” into AI design and implementation [5, pp. 7–8]. Another study of how public participation is framed in AI policy documents [4] shows that high expectations are assigned to public participation as a solution to address concerns about the concentration of power, increases in inequality, lack of diversity, and bias. However, in practice, this framing thus far gives little consideration to some of the challenges well known for researchers and practitioners of public participation with science and technology, such as the difficulty of achieving consensus among diverse societal views, the high resource requirements for public participation exercises, and the risks of capture by vested interests [4, pp. 170–171]. These studies consistently reveal a noteworthy pattern: while references to public participation in AI governance are prevalent in the majority of AI national strategies, they tend to remain abstract and are often overshadowed by other roles, values, and policy concerns. Some authors thus contended that the increasing demand to involve multiple stakeholders in AI governance, including the publics, signifies a discernible transformation within the sphere of science and technology policy. This transformation frequently embraces the framework of “responsible innovation”,3 which emphasizes alignment with societal imperatives, responsiveness 3 “Responsible Innovation” (RI) and “Responsible Research and Innovation” (RRI) have emerged in parallel and are often used interchangeably, but they are not the same thing [27, 28]. RRI is a policydriven discourse that emerged from the EC in the early 2010s, while RI emerged largely from academic roots. For this paper, we will not consider the distinctive features of each discourse, but instead focus on the common features they share. 2 The following examples are particularly illustrative of the multiplicity of organizations emphasizing the need for public participation in AI. The OECD Recommendations of the Council on AI specifically emphasizes the importance of empowering stakeholders considering essential their engagement to adoption of trustworthy [7, p. 6]. The UNESCO Recommendation on the Ethics of AI emphasizes that public awareness and understanding of AI technologies should be promoted (recommendation 44) and it encourages governments and other stakeholders to involve the publics in AI decision-making processes (recommendation 47) [8, p. 23]. The European Union (EU) White Paper on AI [9, p. 259] outlines the EU’s approach to AI, including the need for public consultation and engagement. The Ethics Guidelines for Trustworthy AI [10, pp. 19, 239], developed by the High-Level Expert Group on AI (HLEG) appointed by the EC, emphasize the importance of public participation and consultation in the design, development, and deployment of AI systems. AI and Ethics 1 3 to evolving ethical, social, and environmental considerations, and the participation of the publics as well as traditionally defined stakeholders [3, 28]. When investigating how the conception and promotion of public participation in European science and technology policies have evolved, Macq, Tancoine, and Strasser [29] distinguish between “participation in decision-making” (pertaining to science policy decisions or decisions on research topics) and “participation in knowledge and innovation-making”. They find that “while public participation had initially been conceived and promoted as a way to build legitimacy of research policy decisions by involving publics into decision-making processes, it is now also promoted as a way to produce better or more knowledge and innovation by involving publics into knowledge and innovation-making processes, and thus building legitimacy for science and technology as a whole” [29, p. 508]. Although this shift in science and technology research policies has been noted, there exists a noticeable void in the literature in regard to understanding how concrete research practices incorporate public perspectives and embrace multistakeholder approaches, inclusion, and dialogue. While several studies have delved into the framing of the publics’ role within AI governance in several instances (from Big Tech initiatives to hiring ethics teams and guidelines issued from multiple institutions to governments’ national policies related to AI development), discussing the underlying motivations driving the publics’ participation and the ethical considerations resulting from such involvement, there remains a notable scarcity of knowledge concerning how publicly voiced concerns are concretely translated into research efforts [30, pp. 3–4, 31, p. 8, 6]. To address this crucial gap, our scoping review endeavors to analyse the research landscape about the publics’ views on the ethical challenges of AI. Our primary objective is to uncover the motivations behind involving the publics in research initiatives, identify the segments of the publics that are considered in these studies, and illuminate the ethical concerns that warrant specific attention. Through this scoping review, we aim to enhance the understanding of the political and social backdrop within which debates and prior commitments regarding values and conditions for publics’ participation in matters related to science and technology are formulated and expressed [29, 32, 33] and which specific normative social commitments are projected and performed by institutional science [34, p. 108, [35, p. 856]. 2 Methods We followed the guidance for descriptive systematic scoping reviews by Levac etal. [36], based on the methodological framework developed by Arksey and O’Malley [37]. The steps of the review are listed below: 2.1 Stage 1: identifying theresearch question The central question guiding this scoping review is the following: What motivations, publics and ethical issues emerge in research addressing the publics’ views on the ethical challenges of AI? We ask: • What motivations for engaging the publics with AI technologies are articulated? • Who are the publics invited? • Which ethical issues concerning AI technologies are perceived as needing the participation of the publics? 2.2 Stage 2: identifying relevant studies A search of the publications on PubMed® and Web of Science™ was conducted on 19 May 2023, with no restriction set for language or time of publication, using the following search expression: (“AI” OR “artificial intelligence”) AND (“public” OR “citizen”) AND “ethics”. The search was followed by backwards reference tracking, examining the references of the selected publications based on full-text assessment. 2.3 Stage 3: study selection The inclusion criteria allowed only empirical, peer-reviewed, original full-length studies written in English to explore publics’ views on the ethical challenges of AI as their main outcome. The exclusion criteria disallowed studies focusing on media discourses and texts. The titles of 1612 records were retrieved. After the removal of duplicates, 1485 records were examined. Two authors (HM and SS) independently screened all the papers retrieved initially, based on the title and abstract, and afterward, based on the full text. This was crosschecked and discussed in both phases, and perfect agreement was achieved. The screening process is summarized in Fig.1. Based on title and abstract assessments, 1265 records were excluded because they were neither original full-length peer-reviewed empirical studies nor focused on the publics’ views on the ethical challenges of AI. Of the 220 fully read papers, 54 met the inclusion criteria. After backwards reference tracking, 10 papers were included, and the final review was composed of 64 papers. 2.4 Stage 4: charting thedata A standardized data extraction sheet was initially developed by two authors (HM and SS) and completed by two coders (SS and LN), including both quantitative and qualitative data (Supplemental Table “Data Extraction”). We used MS Excel to chart the data from the studies. AI and Ethics 1 3 The two coders independently charted the first 10 records, with any disagreements or uncertainties in abstractions being discussed and resolved by consensus. The forms were further refined and finalized upon consensus before completing the data charting process. Each of the remaining records was charted by one coder. Two meetings were held to ensure consistency in data charting and to verify accuracy. The first author (HM) reviewed the results. Descriptive data for the characterization of studies included information about the authors and publication year, the country where the study was developed, study aims, type of research (quantitative, qualitative, or other), assessment of the publics’ views, and sample. The types of research participants recruited as publics were coded into 11 categories: developers of AI systems; managers from industry and business; representatives of governance bodies; policymakers; academics and researchers; students; professional groups; general public; local communities; patients/consumers; and other (specify). Data on the main motivations for researching the publics’ views on the ethical challenges of AI were also gathered. Authors’ accounts of their motivations were synthesized into eight categories according to the coding framework proposed by Weingart and colleagues [33] concerning public engagement withscience and technology-related issues: education (to inform and educate the public about AI, improving public access to scientific knowledge); innovation (to promote innovation, the publics are considered to be a valuable source of knowledge and are called upon to contribute to knowledge production, bridge building and including knowledge outside ‘formal’ ethics); legitimation (to promote public trust in and acceptance of AI, as well as of policies Fig. 1 Flowchart showing the search results and screening process for the scoping review of publics’ views on ethical challenges of AI ScreeningIncluded Eligibility Idenficaon Records idenfied throughWeb of ScienceTM (n = 624) Recordsaer duplicates removed (n = 1485) Recordsscreened (n = 1485) Records excluded (n = 1265) Non-original full-length peerreview empirical arcles (n = 530) Not related to the research queson (n = 730) Not in English (n = 5) Full-text arcles assessed for eligibility ( n = 220 ) Full-text arcles excluded (n = 166) Non-original full-length peerreview empirical arcles (n = 95) Not related to the research queson (n = 69) Not in English (n =2) Studies included in the scoping review (n = 64) Recordsincluded based on reference tracking (n = 10) Records idenfied through Pubmed® (n = 988) AI and Ethics 1 3 supporting AI); inspiration (to inspire and raise interest in AI, to secure a STEM-educated labor force); politicization (to address past political injustices and historical exclusion); democratization (to empower citizens to participate competently in society and/or to participate in AI); other (specify); and not clearly evident. Based on the content analysis technique [38], ethical issues perceived as needing the participation of the publics were identified through quotations stated in the studies. These were then summarized in seven key ethical principles, according to the proposal outlined by the EC's Ethics Guidelines for Trustworthy AI [39]: human agency and oversight; technical robustness and safety; privacy and data governance; transparency; diversity, nondiscrimination and fairness; societal and environmental well-being; and accountability. 2.5 Stage 5: collating, summarizing, andreporting theresults The main characteristics of the 64 studies included can be found in Table1. Studies were grouped by type of research and ordered by the year of publication. The findings regarding the publics invited to participate are presented in Fig.2. The main motivations for engaging the publics with AI technologies and the ethical issues perceived as needing the participation of the publics are summarized in Tables2 and 3, respectively. The results are presented below in a narrative format, with complimentary tables and figures to provide a visual representation of key findings. There are some methodological limitations in this scoping review that should be taken into account when interpreting the results. The use of only two search engines may preclude the inclusion of relevant studies, although supplemented by scanning the reference list of eligible studies. An in-depth analysis of the topics explored within each of the seven key ethical principles outlined by the EC's Ethics Guidelines for Trustworthy AI was not conducted. This assessment would lead to a detailed understanding of the publics’ views on ethical challenges of AI. 3 Results 3.1 Study characteristics Most of the studies were in recent years, with 35 of the 64 studies being published in 2022 and 2023. Journals were listed either on the databases related to Science Citation Index Expanded (n = 25) or the Social Science Citation Index (n = 23), with fewer journals indexed in the Emerging Sources Citation Index (n = 7) and the Arts and Humanities Citation Index (n = 2). Works covered a wide range of fields, including health and medicine (services, policy, medical informatics, medical ethics, public and environmental health); education; business, management and public administration; computer science; information sciences; engineering; robotics; communication; psychology; political science; and transportation. Beyond the general assessment of publics’ attitudes toward, preferences for, and expectations and concerns about AI, the publics’ views on ethical challenges of AI technologies have been studied mainly concerning healthcare and public services and less frequently regarding autonomous vehicles (AV), education, robotic technologies, and smart homes. Most of the studies (n = 47) were funded by research agencies, with 7 papers reporting conflicts of interest. Quantitative research approaches have assessed the publics’ views on the ethical challenges of AI mainly through online or web-based surveys and experimental platforms, relying on Delphi studies, moral judgment studies, hypothetical vignettes, and choice-based/comparative conjoint surveys. The 25 qualitative studies collected data mainly by semistructured or in-depth interviews. Analysis of publicly available material reporting on AI-use cases, focus groups, a post hoc self-assessment, World Café, participatory research, and practice-based design research were used once or twice. Multi or mixed-methods studies relied on surveys with openended and closed questions, frequently combined with focus groups, in-depth interviews, literature reviews, expert opinions, examinations of relevant curriculum examples, tests, and reflexive writings. The studies were performed (where stated) in a wide variety of countries, including the USA and Australia. More than half of the studies (n = 38) were conducted in a single country. Almost all studies used nonprobability sampling techniques. In quantitative studies, sample sizes varied from 2.3M internet users in an online experimental platform study [40] to 20 participants in a Delphi study [41]. In qualitative studies, the samples varied from 123 participants in 21 focus groups [42] to six expert interviews [43]. In multi or mixed-methods studies, samples varied from 2036 participants [44] to 21 participants [45]. 3.2 Motivations forengaging thepublics The qualitative synthesis of the motivations for researching the publics’ views on the ethical challenges of AI is presented in Table2 and ordered by the number of studies referencing them in the scoping review. More than half of the studies (n = 37) addressed a single motivation. Innovation (n = 33) and legitimation (n = 29) proved to have the highest relevance as motivations for engaging the publics in the ethical challenges of AI technologies, as articulated in 15 studies. Additional motivations are rooted in education (n = 13), democratization (n = 11), and inspiration (n = 9). AI and Ethics 1 3 Table 1 Main characteristics of the empirical studies exploring the publics’ views on ethical challenges of AI (n = 64) Publication Country Aim Sample Assessment of public views Quantitative studies (n = 27) Awad etal. 2018 [40] 233 countries, dependencies, or territories To quantify societal expectations about the ethical principles that should guide machine behaviour, and how they varied between individuals and countries 2.3M internet users who chose to visit the website and contribute to the data Online experimental platform Kaur and Rampersad 2018 [62] Australia To investigate the key factors influencing the perceptions of benefits, concerns, trust and adoption of driverless cars 101 Flinders University’s staff and students based at Tonsley Online survey Kallioinen etal. 2019 [59] 24 countries (Germany, Armenia, Australia, Russia, and other) To further understand the ethical issues of introducing self-driving cars Virtual reality experiment: 184 participants. Simplified animationbased experiment: 368 participants Moral judgement studies Liyanage etal. 2019 [41]Australia, Belgium, Canada, Croatia, Ireland, Italy, New Zealand, Spain, UK, and USA To seek consensus on the perceptions, issues, and challenges of AI in primary care Health informatics experts and clinicians involved in primary health care (20 in round 1, 12 in round 2, 13 in round 3) Delphi study (two online surveys + an online panel discussion) Awad etal. 2020 [88]USA To indicate how blame should be allocated when considering hypothetical cases in which a pedestrian was killed by a car operated under shared control of a primary and a secondary driver Study 1: 786 participants. Study 2: 779 participants. Study 3: 973 participants. Study 4: 375 participants. Study 5 (representative sample): 2000 participants Hypothetical vignettes that describe a crash Esmaeilzadeh 2020 [52]USA To examine the perceived benefits and risks of AI medical devices with clinical decision support (CDS) features from consumers’ perspectives 427 participants Online survey Chen and Wen 2021 [69] Taiwan To explore how Taiwanese people’s perceptions of AI are affected by their institutional trust, attitudes toward the government and corporations 1009 participants Online survey Nichol etal. 2021 [87] African countries (Ethiopia, Ghana, Kenya, Nigeria, Rwanda, South Africa, Uganda, Zambia, Zimbabwe), and USA To understand experts’ views about the ethical implications of ongoing research funded by the National Institutes of Health that uses machine learning to predict HIV/AIDS risk in sub-Saharan Africa based on publicly available Demographic and Health Surveys data, and to inform an ethical framework and recommendations for researchers 22 experts in informatics, African public health and HIV/AIDS and bioethics Modified Delphi (three online scenario-based semi-structured surveys with closed and open-ended questions) AI and Ethics 1 3 Table 1 (continued) Publication Country Aim Sample Assessment of public views Ploug etal. 2021 [73] Denmark To elicite the public’s preferences for the performance and explainability of AI decision making in health care and determined whether these preferences depend on respondent characteristics, including trust in health and technology and fears and hopes regarding AI 1027 participants: representative sample of the adult Danish population Choice-based conjoint survey Tosoni etal. 2021 [95] Canada To acquire an in-depth understanding of the contemporary and specific consent needs of cancer patients at a large academic hospital to inform its own institutional consent policies 222 participants Survey Zheng etal. 2021 [74] Various cities and countries, mainly in Zhejiang Province To assess medical workers’ and other professional technicians’ familiarity with, attitudes toward, and concerns about AI in ophthalmology 562 health care workers or medical students, mainly members of the Zhejiang Society of Mathematical Medicine Electronic questionnaire Criado and de Zarate-Alcarazo 2022 [56] Spain To understand CIOs’ (Chief Information Officers) interpretation of AI in the public sector 73 participants Survey, with closed and open questions De Graaf etal. 2022 [91] USA, EU (e.g., Italy, Spain, Germany), and Asia (e.g. China, South Corea, Singapore) To investigate layman’s attitudes toward granting particular rights to robots, and the reasons for their willingness to grant them those rights 439 participants (USA = 200, EU = 97, Asia = 142) Online survey Ehret 2022 [51] Germany, UK, India, Chile, and China To what extent economic and especially the labor market consequences affect preferences for public policies governing AI: when the public demands imposing restrictions on (or even prohibiting) emerging AI technologies; to assess how the balance betweennormative and economic concerns varies across countries 932 participants Comparative conjoint survey experiment AI and Ethics 1 3 Table 1 (continued) Publication Country Aim Sample Assessment of public views Gray etal. 2022 [97] Australia To explore health care education experts’ ideas and plans for preparing the health workforce to work with AI and identify critical gaps in curriculum and educational resources across a national health care system 39 senior people from different health workforce subgroups Survey Guerouaou etal. 2022 [92] France To initiate the data-driven study of expressive deep-fakes ethics (specifically here, vocal deep-fakes) and to quantify societal expectations about the principles that should guide their deployment 303 participants Online questionnaire (24 text vignettes describing potential applications of vocal deep-fakes) Ikkatai etal. 2022 [81] Japan To investigate public attitudes toward AI ethics using four scenarios in Japan 1029 participants Online questionnaire Isbanner etal. 2022 [57] Australia To understand Australians’ normative judgments regarding the use of AI, especially in the fields of health care and social services, and determine the attributes of health care and social service AI systems that Australians consider most important 4448 participants: 1950 in a web-based panel; 2498 from an omnibus survey (for a subset of questions) Survey Kieslich etal. 2022 [48] Germany To investigate how ethical principles (explainability, fairness, security, accountability, accuracy, privacy, and machine autonomy) are weighted in comparison to each other, given the use of artificial intelligence in tax fraud detection 1099 participants (online access panel representative of the German population above 18years of age, which at least occasionally uses the Internet) Online survey (eight compositions of AI systems that varied only in the different fulfillment of the ethical principles) Kopecky etal. 2022 [100] Czech Republic To assess whether the public visibility of the choice of an AV type choice make this choice more altruistic, and which type of situation makes it more difficult to choose altruistically: when choosing for society as a whole, when choosing only for oneself, or when choosing only for one’s offspring 2769 participants Online questionnaire AI and Ethics 1 3 Table 1 (continued) Publication Country Aim Sample Assessment of public views Lam etal. 2022 [101] UK, rest of Europe, North America, and South America To define the term digital surgery and the ethical issues surrounding its clinical application, and to identify barriers and research goals for future practice Round 1: 52 international experts + 20 members of the public. Round 2: 44 participants. Round 3: 24 surgeons, 8 academics, 3 from healthcare industry, 3 from healthcare policy/digital law/ ethics Delphi exercise Ma etal. 2022 [75]USA To provide relevant government and industry bodies with a better understanding of the general public’s receptiveness towards sex robots for more informed policy making, and to raise awareness of the significant social and ethical implications should sex robots become widely accepted and adopted 497 participants Surveya Sartori and Bocca 2022 [68] Italy To investigate the perceptions and attitudes of the general public towards AI (levels of knowledge, awareness, trust, hopes and fears) 5391 students, professors and other employees affiliated to the University of Bologna Survey, with closed and open-ended questions Willems etal. 2022a [63] Austria To hypothesize and test the way citizens trade-off the perceived usefulness of AI-driven applications with overall privacy concerns to engage in AI-driven public services 1048 participants: representative sample of Austrian citizens for place of residence, and gender over different age categories Online vignette experiment Willems etal. 2022b [49] Austria and Germany To explore and test whether robot appearance influences citizens’ perceptions of the ethical tradeoffs made in robot-supported public services Study 1: 156 students at the University of Hamburg. Study 2: 1339 participants (representative sample of Austrian citizens for place of residence, and gender over different age categories) Laboratory experiment involving eye-tracking recording (study 1) + Online vignette experiment (study 2) Choung etal. 2023 [71]USA To investigate the importance assigned by the general population to ethical requirements of AI and the influence of these requirements on trust 525 current and potential consumers of AI products Online survey Hartwig etal. 2023 [72] Japan, and USA To investigate public attitudes toward AI research ethics in Japan and the US using a set of dilemma scenarios Japan: 1108 respondents. USA: 1063 respondents Online survey AI and Ethics 1 3 Table 1 (continued) Publication Country Aim Sample Assessment of public views Zhang etal. 2022 [83] Not specified To examine whether and to what extent the Developing AI Literacy (DAILy) curriculum helped middle school students develop three core domains of AI: technical concepts, processes, knowledge and skills; ideas about AI’s ethical and societal implications; attitudes toward AI and AI careers 25 middle school students in a summer STEM program (urban students who have a grade of C or lower in science/ technology courses, are from low-income families, and may become the first person to go to college in their family), 19 of them purposefully selected for interviewing after the workshop Three quantitative instruments investigate students’ learning of AI with the DAILy workshops implemented online: AI Concept inventory, Attitudes toward AI survey; and AI Career Futures survey. Students’ final presentations + observation notes + semi-structured interviews Couture etal. 2023 [45] Canada (and potential other countries not specified) To explore the perspectives and attitudes of citizens and experts regarding the ethics of AI in population health, the engagement of citizens in AI governance, and the potential of a digital app to foster citizen engagement 21 participants: 11 citizens and 10 experts (AI experts, scholars, policymakers) Web-based survey, with close ended and open questions Kong etal. 2023 [79] China To design, implement and evaluate an AI literacy programme based on a multi-dimensional conceptual framework, which developed participants’ conceptual understanding, literacy, empowerment and ethical awareness 36 university students with diverse academic backgrounds Tests, surveys, focus group, and reflective writings (self-reflections), preand post-course activities a This scoping review does not consider an additional study that examined Google search volume derived from the USA, from 2015 to 2019, related to search of items such as sex toys and prominent adult content websites b Findings from media reports were not considered in this scoping review c The exclusion criteria disallowed an initial study focusing on the analysis of 2330 tweets from 1530 twitter users AI and Ethics 1 3 Politicization was mentioned in five studies. Although they were not authors’ motivations, few studies were found to have educational [46, 47], democratization [48, 49], and legitimation or inspirations effects [50]. To consider the publics as a valuable source of knowledge that can add real value to innovation processes in both the private and public sectors was the most frequent motivation mentioned in the literature. The call for public participation is rooted in the aspiration to add knowledge outside “formal” ethics at three interrelated levels. First, at a societal level, by asking what kind of AI we want as a society based on novel experiments on public policy preferences [51] and on the study of public perceptions, values, and concerns regarding AI design, development, and implementation in domains such as health care [46, 52–55], public and social services [49, 56–58], AV [59, 60] and journalism [61]. Second, at a practical level, the literature provides insights into the perceived usefulness of AI applications [62, 63] and choicesbetween boosting developers’ voluntary adoption of ethical standards or imposing ethical standards via regulation and oversight [64], as well as suggesting specific guidance for the development and use of AI systems [65–67]. Finally, at a theoretical level, literature expandsthe socialtechnical perspective [68] and motivated-reasoning theory [69]. Legitimation was also a frequent motivation for engaging the publics. It was underpinned by the need for public trust in and social licences for implementing AI technologies. To ensure the long-term social acceptability of AI as a trustworthy technology [70, 71] was perceived as essential to support its use and to justify its implementation. In one study [72], the authors developed an AI ethics scale to quantify how AI research is accepted in society and which area of ethical, legal, and social issues (ELSI) people are most concerned with. Public trust in and acceptance of AI is claimed by social institutions such as governments, private sectors, industry bodies, and the science community, behaving in a trustworthy manner, respecting public concerns, aligning with societal values, and involving members of the publics in decision-making and publicpolicy [46, 48, 73–75], as well as in the responsible design and integration of AI technologies [52, 76, 77]. Education, democratization, and inspiration had a more modest presence as motivations to explore the publics’ views on the ethical challenges of AI. Considering the emergence of new roles and tasks related to AI, the literature has pointed to the public need to ensure the safe use of AI technologies by incorporating ethics and career futures into the education, preparation, and training of both middle school and university students and the current and future health workforce. Improvements in education and guidance for developers and older adults were also noticed. The views of the publics on what needs to be learned or how this learning may be supported or assessed were perceived as crucial. In one study [78], the authors developed strategies that promote learning related to AI through collaborative media production, connecting computational thinking to civic issues and creative expression. In another study [79], real-world scenarios were successfully used as a novel approach to teaching AI ethics. Rhim etal. [76] provided AV moral behavior design guidelines for policymakers, developers, and the publics by reducing the abstractness of AV morality. Studies motivated by democratization promoted broader public participation in AI, aiming to empower citizens both Fig. 2 Publics invited to engage with issues framed as ethical challenges of AI AI and Ethics 1 3 Table 2 Motivations for researching publics’ views on ethical challenges of AI a [40, 46, 48, 49, 51–70, 75, 86, 87, 91, 92, 94, 96, 99, 101] b [40, 41, 44, 46, 48, 52, 55, 58, 62, 63, 65, 69–77, 80, 82, 87, 88, 93, 95, 96, 100, 101] c [50, 76, 78, 79, 82, 83, 85, 89, 90, 93, 97, 99, 102] d [40, 43, 45, 78–83, 88, 96] e [46, 48, 61, 78, 79, 84, 85, 96, 98] f [78, 79, 86, 87, 103] Innovation (33 articles)a: illustrative quotes Public engagement presents valuable opportunities to incorporate diverse views and perspectives and to enable critical reflection on organisational practices and/or the direction of innovation. (…) Wider public engagement can play a valuable role in contesting framings and opening up discourses around data ethics and responsible AI to a wider range of perspectives and considerations. (…) This can add real value to innovation processes in both the private and public sector [70] Our analysis can contribute to underline the need to place the concerns, values, and opinions of citizens at the centre of the development and implementation processes of AI and robotics systems in health care, as the only way to ensure that these technologies seek to respond to individual and collective well-being and good living [54] We need to gauge social expectations about how autonomous vehicles should solve moral dilemmas [40] This paper complements existing normative guidelines and ethical frameworks by empirically probing stakeholders’ views on ethically relevant issues relating to the use of medical AI in a concrete clinical context, namely, stroke medicine [66] Understanding the scenarios when users are most willing to adopt driverless cars will assist in early implementation programs among adopting target groups and settings [62] Legitimation (29 articles)b: illustrative quotes Citizens must support the use of such technology to justify their implementation in public services [63] Attention to understanding and incorporating the values of older citizens will be important for the acceptance and effectiveness of smart technologies for supporting independent and full lives for older citizens [80] When people’s skepticism of the discipline and integrity of government, private sectors, and science community remains high and unaddressed, then public distrust of AI will not diminish, because the masses would be constantly agitated over potential abuse of it by those social institutions in power [69] The study has social implications in terms of ensuring that proper guidelines are developed for using AI technology for citizen services, thereby bridging the evercritical trust gap between citizens and city administration [58] A top-down design methodology (…) fails to engage users in the design process. This has frequently created significant mismatches between the needs and preferences of the users and the products that are developed to fulfill their needs. (…) These mismatches can hinder meaningful adoption and sustained usage, and risk leaving priority needs of end-users unmet [82] Education (13 articles)c: illustrative quotes To be ready for new roles and tasks, medical students and physicians will need to understand the (…) ethical and medico-legal issues [102] The rapid expansion of artificial intelligence (AI) necessitates promoting AI education at the K-12 level. (…) Students must learn three core domains of AI: technical concepts and processes, ethical and societal implications, and career futures in the AI era [83] At present, there is no obvious consensus among educators about what needs to be learned or how this learning may be supported or assessed [97] There is a need to increase technology literacy of older adults along with aging literacy of technologists [82] Regulatory approaches (…) might have limited success without education and guidance for ML developers about the extent of their responsibilities and how to implement them [85] Democratization (11 articles)d: illustrative quotes We brought together notions of an informed public, rich collaborative discussion in a hospitable space, and collective knowledge made visible. (…) We aimed for diversity but also purposely recruited participants from low socioeconomic backgrounds, since these individuals, in general, have less input into public policy and fewer choices than individuals from high socioeconomic groups [80] This (…) study (…) aims to broaden participation in AI (…), and to prepare students to investigate and address ethical issues in AI as critical consumers and potential future creators of AI technologies [83] A user-centered design approach was used to identify older adults’ perspectives regarding AAL and AI technologies and gauge interest in participating in a co-design process [82] Before we allow our cars to make ethical decisions, we need to have a global conversation to express our preferences to the companies that will design moral algorithms, and to the policymakers that will regulate them [40] We (…) explore the ways in which citizens can be supported to participate in AI governance through a digital app. (…) The goal was to estimate whether a digital app would be able to contribute to four variables: (1) to inform, (2) to raise public awareness, (3) to rigorously measure citizen attitudes on the issues of AI, and (4) to support collective decision-making [45] Inspiration (9 articles)e: illustrative quotes The process of embedding values into ANRs can be addressed only through close collaboration between all those involved in the decision to implement, use, and design the technology [61] To guide and inspire future empirical and design research on fostering AI literacy among educated citizens of diverse backgrounds [79] To promote developers’ moral awareness—the appreciation that there is an ethical aspect to the decisions that they make [85] Politicization (5 articles)f: illustrative quotes To strengthen collective wisdom in promoting STEM engagement, particularly amongst historically marginalized populations [78] To serve social equity and sustainable development goals [79] To address (…) the region’s [SSA countries] history of human rights abuses… [87] AI and Ethics 1 3 Table 3 Ethical issues perceived as needing the participation of the publics Human agency and oversight (55 articles)a: illustrative quotes Attributing the same kind of agency humans have to robots and AI systems is the source of a distorted portrayal of future technical possibilities [68] Few of these developers, however, described taking action to prevent or mitigate harms, possibly because of lack of knowledge about how to do so, or perceiving lack of agency [85] While our interviewees saw CP technology as an expert tool (a reliable and competent tool) they did not advocate that these tools should replace trusted human experts [55] Even though patients intensely value their face-to-face relationships with hospital staff, from their experience of the oversaturation of the healthcare system and the lived experience of lack of care resources, robots in healthcare are accepted as a possible solution [54] So the technology would have to be sophisticated enough to judge situations properly but people need to have some kind of control over it to say, “This is not what I need.” [70] They emphasized that AI cannot replace human judgment. (…) Four types of citizen involvement were proposed (…): (1) information, (2) consultation, (3) decision-making, and (4) involvement in AI design. (…) In other words, participants expect that citizens play an engaged normative role in AI governance [45] Community and stakeholder engagement that includes Africans, ideally in relevant countries, were seen as key to minimising risks at several stages of the research process, including data access, protocol oversight and dissemination and implementation of findings. (…) When asked which specific stakeholders would be critically important to include in stakeholder engagement, over half of participants believed African data scientists, African ethicists, representatives from a national Ministry of Health and representatives from African universities were necessary to include. Interestingly, responses were split (roughly in half) on whether African religious leaders and healers, African health workers and African patients and families were critically important to include or not important to include in stakeholder engagement. There were divergent opinions as to the necessity of representatives from local communities as well [87] Privacy and data governance (43 articles)b: illustrative quotes It was clear that participants were aware of risks relating to data sharing and/or data misuse. The extent to which participants were interested in using new services underpinned by Open Banking depended on how confident they felt that they would remain in control of who had access to their data. (…) There was concern that while current regulations might limit data sharing or data reuse to purposes which were perceived to be acceptable and legitimate, in the future such regulation might change to allow further possibilities which are less acceptable (including potential government access of personal data). Moreover, there was concern that data might be used, or reused inappropriately or shared with third parties [70] Given the number of data breaches and the extensive use of personal data by corporations, it is not surprising that privacy rose to the top three in perceived importance [71] The term ‘regulation’ (…) appears roughly in two types of instances: mundanely, when it comes to mentioning/boasting that the company achieved clearance by a regulatory body; and in order to criticize/complain about the burden of regulation [64] Panellists agreed that determining appropriate access to data was an important issue and that governance processes at present were overcomplicated and obstructive. (…) Panellists agreed that there are currently no guidelines concerning data ownership, and the international legal requirements concerning data sharing are unclear. (…) Moreover, data sharing across international boundaries is problematic and there is no consensus on data sharing formats. (…) Preservation of privacy and confidentiality is essential, not only to safeguard patient autonomy but also to ensure patient trust [101] There must be regulations that mandate data protection such as the right to be forgotten, the right to privacy, and seeking unambiguous consent, which is very important, for data collection. So explicit consent should be obtained from citizens for each AI solution before using their data [58] Diversity, non-discrimination and fairness (39 articles)c: illustrative quotes There are several ways through which we may introduce bias, which means that decisions might penalize or reward one group over another. (…) These classes could be based on ethnicity, culture, physical disability, or socioeconomic status. (…) It is possible that sample or training data collected for AI solution only considers data from certain classes and does not represent the entire population (…). Also, it should not propagate a wrong message, which negatively impacts the peace and harmony within the society. (…) Using AI algorithms that are developed in some other countries with different social norms could act as a deterrent for citizens who are used to the local social norms and moral values [58] The participants were concerned about potential biases in the design and the use of AI, which could lead to increased social inequality. For instance, they referred to the risks that AI exacerbates unequal access to health care, contributes to the discrimination of subpopulations, and enhances social inequalities as well as economic equity issues. Some participants underlined the digital divide (disparity in access to technologies) and the issues of fairness and diversity of data. (…) One member of the panel mentioned the necessity of collecting data on more vulnerable populations to make sound decisions regarding population health [45] Discriminatory bias substantially impacts prohibition preferences in Germany (…) and Chile AI and Ethics 1 3 Table 3 (continued) (…), but less so in China (…) or India (…) [51] Panellists agreed the following important issues affect public trust: (…) the fear of AI reinforcing biases in datasets. (…) They highlighted issues surrounding inequality of power and differing motives between hospitals and commercial companies [101] Open-ended responses (…) reflected issues of re-identification, stigma, discrimination against individuals, families or geographically defined and/or socially defined groups (…). Another theme that emerged was concern about data on Africans being used by non-African researchers [87] Societal and environmental well-being (39 articles)d: illustrative quotes I believe that AI will lead to (…) unemployment; loss of control to machines; increased data collection and mass surveillance; more jobs; longer lives; more quality of life; peace and political stability. I believe that AI will cause unintentional harm to humans [73] Some also considered that overreliance may lead to a loss of expertise and competences in future generations of clinicians and, consequently, dependence [66] Many of the participants demonstrated cautious and conditional acceptance of smart technologies, while identifying concerns about social isolation (…). The principles of beneficence and non-maleficence obligate those providing care (here, arguably, including technology developers) to support the wellbeing of others (…). Other themes related to concerns about the impact of smart technologies on social norms and how we related to each other in community [80] Indeed, they made references to the ecological burden and greenhouse gas emissions because of the power demand for overarchiving and electronic medical devices. (…) Another societal issue raised by some respondents is related to the transformation of the health sector. Participants mentioned the risk that the introduction of AI could affect the “access to healthcare and caregivers.” Another response highlighted the participation of such technologies in the “commercialization of healthcare.” From the perspective of HCPs, respondents noted that “AI encourages the bureaucratization of work” and that the technology will contribute to the “transformation of healthcare professions.” [45] Technical robustness and safety (38 articles)e: illustrative quotes The primary care informatics community needs to be proactive and to guide the ethical and rigorous development of AI applications so that they will be safe and effective [41] Safety was the main concern for participants in all of the four moral dilemma vignettes [76] We need to be reassured that AI tools are subject to stringent reliability and quality assurance checks [55] Panellists agreed that institutions are not equipped and under resourced to perform appropriate cybersecurity [101] The poorer the performance of the AI, and the less mature the systems, the more harmful this could be to patients. However even for high-performing AI it remained a problem: we tend to believe machines more than we would humans and we tend to follow their advice even if it is wrong, so that’s a tendency that humans have, and so even if a model is 99% accurate, how are you going to deal with the 1% of cases where it’s not going to perform? [67] Transparency (35 articles)f: illustrative quotes The process of data collection (…) should be transparent. This will build more trust in the system if citizens know that their representation is properly done in the training dataset [58] While highly educated, most participants lacked understanding of the granularity of data that can be captured with pervasive sensing technology and the associated analytics used by digital platforms to identify patterns. The mystery of AI, including what it is and how it works, contributed to fears of data loss or being harmed from decisions made from their personal data [82] Machine learning, an essential part of modern AI, has been criticized widely in the media for its “black box” approach to solutions, which may have contributed to concerns about transparency. Further, transparency is closely associated with accountability because most users are willing to share some data if corporations are more transparent of potential risks and rewards [71] We find that citizens experience transparency as burdensome; experts hope transparency ensures acceptance, while citizens are mostly indifferent to AI; and with absent means of control, citizens question transparency’s relevance. The tensions we identify suggest transparency cannot be reduced to a product feature, but should be seen as a mediator of debate between experts and citizens. (…) We can also see that not only decisions, but motivations for them must be made transparent [98] Accountability (31 articles)g: illustrative quotes Some participants indicated media as a key mechanism for accountability. Some participants indicated skepticism that institutions and companies could be held accountable [93] The potential psychological impact of AI is shown (…) when young people begin to question whether they may be the problem or cause for not getting the results they expect or want. (…) Donald makes the critical move to hold accountable the companies and designers who fail to factor the full range of human experiences into the technology they create, developing systems that reflect and reify inequalities [78] Thus, if AI tools become crucial in medical decisions, physicians stated that they were not prepared (would not agree?) to be held criminally responsible if a medical error was made by an AI tool. (…) Paradoxically, they [healthcare industrial partners] said that the question concerning responsibility in case of injury was not yet relevant. (…) In addition, those in industry were quite clear about their not being ready to be held responsible for their AI tools if such a tool induced harm to a patient because of an unpredictable evolution of the tool due to a “black box” phenomenon. (…) Members of regulatory agencies are beginning to take an interest in the subject but appear to be currently overwhelmed [53] AI and Ethics 1 3 to express their understandings, apprehensions, and concerns about AI [43, 78, 80, 81] and to address ethical issues in AI as critical consumers, (potential future) developers of AI technologies or would-be participants in codesign processes [40, 43, 45, 78, 82, 83]. Understanding the publics’ views on the ethical challenges of AI is expected to influence companies and policymakers [40]. In one study [45], the authors explored how a digital app might support citizens’ engagement in AI governance by informing them, raising public awareness, measuring publics’ attitudes and supporting collective decision-making. Inspiration revolved around three main motivations: to raise public interest in AI [46, 48]; to guide future empirical and design studies [79]; and to promote developers’ moral awareness through close collaboration between all those involved in the implementation, use, and design of AI technologies [46, 61, 78, 84, 85]. Politicization was the less frequent motivation reported in the literature for engaging the publics. Recognizing the need for mitigation of social biases [86], public participation to address historically marginalized populations [78, 87], and promoting social equity [79] were the highlighted motives. 3.3 The invitedpublics Study participants were mostly the general public and professional groups, followed by developers of AI systems, managers from industry and business, students, academics and researchers, patients/consumers, and policymakers (Fig.2). The views of local communities and representatives of governance bodies were rarely assessed. Representative samples of the general public were used in five papers related to studies conducted in the USA [88], Denmark [73], Germany [48], and Austria [49, 63]. The remaining random or purposive samples from the general public comprised mainly adults and current and potential users of AI products and services, with few studies involving informal caregivers or family members of patients (n = 3), older people (n = 2), and university staff (n = 2). Samples of professional groups included mainly healthcare professionals (19 out of 24 studies). Educators, law enforcement, media practitioners, and GLAM professionals (galleries, libraries, archives, and museums) were invited once. 3.4 Ethical issues The ethical issues concerning AI technologies perceived as needing the participation of the publics are depicted in Table3. They were mapped by measuring the number of studies referencing them in the scoping review. Human agency and oversight (n = 55) was the most frequent ethical aspect that was studied in the literature, followed by those centered on privacy and data governance (n = 43). Diversity, nondiscrimination and fairness (n = 39), societal and environmental well-being (n = 39), technical robustness and safety (n = 38), transparency (n = 35), and accountability (n = 31) were less frequently discussed. The concerns regarding human agency and oversight were the replacement of human beings by AI technologies and deskilling [47, 55, 67, 74, 75, 89, 90]; the loss of autonomy, critical thinking, and innovative capacities [50, 58, 61, 77, 78, 83, 85, 90]; the erosion of human judgment and oversight [41, 70, 91]; and the potential for (over)dependence on technology and “oversimplified” decisions [90] due to the lack of publics’ expertise in judging and controlling AI technologies [68]. Beyond these ethical challenges, the following contributions of AI systems to empower human beings were noted: more fruitful and empathetic social relationships [47, 68, 90]; enhancing human capabilities and Table 3 (continued) It is currently unclear who holds responsibility for data integrity under law. (…) Panellists agreed that there is a lack of regulation concerning litigation and liability, both for failing digital surgical systems and for surgeons who elect to not follow systems such as AI decision support tools. Additionally, if a surgeon were to follow AI decision support, which resulted in a negative outcome, it is unclear how liability would be adjudicated [101] As with other IT systems, someone must take responsibility for the end-to-end AI process deployed for a specific purpose. This also means setting up clear boundaries for the AI based application from conceptualization to deployment. The data collection process and how the algorithms are selected before moving the system into production deployment should be supervised. A monitoring mechanism is required so that no bias enters, and the models continue to work as intended. Hence, someone should be responsible for AI processes, policies, and protocols. Somebody must be responsible for determining if the output and performance are as per the given framework [58] a [40–48, 50, 53–62, 64–68, 70, 71, 73–78, 80–87, 89–101, 103] b [41–48, 50–53, 55–58, 60–66, 70–72, 78–82, 85–87, 90, 91, 93–97, 101, 102] c [40, 41, 43, 45, 47, 48, 50–52, 55–61, 64–67, 71–73, 78–83, 85–87, 90, 93, 94, 96–98, 101] d [41, 42, 44–47, 49–53, 55–57, 59, 60, 62, 65, 66, 68, 70–76, 80, 81, 83, 85, 87, 89, 90, 94, 96, 97, 99, 100] e [41, 44, 45, 47, 48, 50–52, 55–58, 60, 62, 64–67, 70, 71, 73, 76, 79–81, 83–90, 93, 94, 96, 99, 101] f [41, 43, 45, 46, 48, 51, 53, 55–58, 61, 64–66, 69, 71–73, 78, 79, 81, 82, 84, 87, 92–98, 101–103] g [41, 45, 48, 52, 53, 55–58, 60, 61, 65, 66, 68, 71, 76–79, 81, 85–89, 91, 93, 94, 96, 101, 103] AI and Ethics 1 3 quality of life [68, 70, 74, 83, 92]; improving efficiency and productivity at work [50, 53, 62, 65, 83] by reducing errors [77], relieving the burden of professionals and/or increasing accuracy in decisions [47, 55, 90]; and facilitating and expanding access to safe and fair healthcare [42, 53, 54] through earlier diagnosis, increased screening and monitoring, and personalized prescriptions [47, 90]. To foster human rights, allowing people to make informed decisions, the last say was up to the person themselves [42, 43, 46, 55, 64, 67, 73, 76]. People should determine where and when to use automated functions and which functions to use [44, 54], developing “job sharing” arrangements with machines and humans complementing and enriching each other [56, 65, 90]. The literature highlights the need to build AI systems that are under human control [48, 70] whether to confirm or to correct the AI system’s outputs and recommendations [66, 90]. Proper oversight mechanisms were seen as crucial to ensure accuracy and completeness, with divergent views about who should be involved in public participation approaches [86, 87]. Data sharing and/or data misuse were considered the major roadblocks regarding privacy and data governance, with some studies pointing out distrust of participants related to commercial interests in health data [55, 90, 93–95] and concerns regarding risks of information getting into the hands of hackers, banks, employers, insurance companies, or governments [66]. As data are the backbone of AI, secure methods of data storage and protection are understood as needing to be provided from the input to the output data. Recognizing that in contemporary societies, people are aware of the consequences of smartphone use resulting in the minimization of privacy concerns [93], some studies have focused on the impacts of data breaches and loss of privacy and confidentiality [43, 45, 46, 60, 62, 80] in relation to health-sensitive personal data [46, 93], potentially affecting more vulnerable populations, such as senior citizens and mentally ill patients [82, 90] as well as those at young ages [50], and when journalistic organizations collect user data to provide personalized news suggestions [61]. The need to find a balance between widening access to data and ensuring confidentiality and respect for privacy [53] was often expressed in three interrelated terms: first, the ability of data subjects to be fully informed about how data will be used and given the option of providing informed consent [46, 58, 78] and controlling personal information about oneself [57]; second, the need for regulation [52, 65, 87], with one study reporting that AI developers complain about the complexity, slowness, and obstacles created by regulation [64]; and last, the testing and certification of AI-enabled products and services [71]. The study by De Graaf etal. [91] discussed the robots’ right to store and process the data they collect, while Jenkins and Draper [42] explored less intrusive ways in which the robot could use information to report back to carers about the patient’s adherence to healthcare. Studies discussing diversity, nondiscrimination, and fairness have pointed to the development of AI systems that reflect and reify social inequalities [45, 78] through nonrepresentative datasets [55, 58, 96, 97] and algorithmic bias [41, 45, 85, 98] that might benefit some more than others. This could have multiple negative consequences for different groups based on ethnicity, disease, physical disability, age, gender, culture, or socioeconomic status [43, 55, 58, 78, 82, 87], from the dissemination of hate speech [79] to the exacerbation of discrimination, which negatively impacts peace and harmony within society [58]. As there were crosscountry differences and issue variations in the publics’ views of discriminatory bias [51, 72, 73], fostering diversity, inclusiveness, and cultural plurality [61] was perceived as crucial to ensure the transferability/effectiveness of AI systems in all social groups [60, 94]. Diversity, nondiscrimination, and fairness were also discussed as a means to help reduce health inequalities [41, 67, 90], to compensate for human preconceptions about certain individuals [66], and to promote equitable distribution of benefits and burdens [57, 71, 80, 93], namely, supporting access by all to the same updated and high-quality AI systems [50]. In one study [83], students provided constructive solutions to build an unbiased AI system, such as using a dataset that includes a diverse dataset engaging people of different ages, genders, ethnicities, and cultures. In another study [86], participants recommended diverse approaches to mitigate algorithmic bias, from open disclosure of limitations to consumer and patient engagement, representation of marginalized groups, incorporation of equity considerations into sampling methods and legal recourse, and identification of a wide range of stakeholders who may be responsible for addressing AI bias: developers, healthcare workers, manufacturers and vendors, policymakers and regulators, AI researchers and consumers. Impacts on employment and social relationships were considered two major ethical challenges regarding societal and environmental well-being. The literature has discussed tensions between job creation [51] and job displacement [42, 90], efficiency [90], and deskilling [57]. The concerns regarding future social relationships were the loss of empathy, humanity, and/or sensitivity [52, 66, 90, 99]; isolation and fewer social connections [42, 47, 90]; laziness [50, 83]; anxious counterreactions [83, 99]; communication problems [90]; technology dependence [60]; plagiarism and cheating in education [50]; and becoming too emotionally attached to a robot [65]. To overcome social unawareness [56] and lack of acceptance [65] due to financial costs [56, 90], ecological burden [45], fear of the unknown [65, 83] and/or moral issues [44, 59, 100], AI systems need to provide public benefit sharing [55], consider discrepancies between public discourse about AI and the utility of the tools in real-world AI and Ethics 1 3 settings and practices [53], conform to the best standards of sustainability and address climate change and environmental justice [60, 71]. Successful strategies in promoting the acceptability of robots across contexts included an approachable and friendly looking as possible, but not too human-like [49, 65], and working with, rather than in competition, with humans [42]. The publics were invited to participate in the following ethical issues related to technical robustness and safety: usability, reliability, liability, and quality assurance checks of AI tools [44, 45, 55, 62, 99]; validity of big data analytic tools [87]; the degree to which an AI system can perform tasks without errors or mistakes [50, 57, 66, 84, 90, 93]; and needed resources to perform appropriate (cyber)security [62, 101]. Other studies approached the need to consider both material and normative concerns of AI applications [51], namely, assuring that AI systems are developed responsibly with proper consideration of risks [71] and sufficient proof of benefits [96]. One study [64] highlighted that AI developers tend to be reluctant to recognize safety issues, bias, errors, and failures, and when they do so, they do so in a selective manner and in their terms by adopting positivesounding professional jargon as AI robustness. Some studies recognized the need for greater transparency that reduces the mystery and opaqueness of AI systems [71, 82, 101] and opens its “black box” [64, 71, 98]. Clear insights about “what AI is/is not” and “how AI technology works” (definition, applications, implications, consequences, risks, limitations, weaknesses, threats, rewards, strengths, opportunities) were considered as needed to debunk the myth about AI as an independent entity [53] and for providing sufficient information and understandable explanations of “what’s happening” to society and individuals [43, 48, 72, 73, 78, 102]. Other studies considered that people, when using AI tools, should be made fully aware that these AI devices are capturing and using their data [46] and how data are collected [58] and used [41, 46, 93]. Other transparency issues reported in the literature included the need for more information about the composition of data training sets [55], how algorithms work [51, 55, 84, 94, 97], how AI makes a decision [57] and the motivations for that decision [98]. Transparency requirements were also addressed as needing the involvement of multiple stakeholders: one study reported that transparency requirements should be seen as a mediator of debate between experts, citizens, communities, and stakeholders [87] and cannot be reduced to a product feature, avoiding experiences where people feel overwhelmed by explanations [98] or “too much information” [66]. Accountability was perceived by the publics as an important ethical issue [48], while developers expressed mixed attitudes, from moral disengagement to a sense of responsibility and moral conflict and uncertainty [85]. The literature has revealed public skepticism regarding accountability mechanisms [93] and criticism about the shift of responsibility away from tech industries that develop and own AI technologies [53, 68], as it opens space for users to assume their own individual responsibility [78]. This was the case in studies that explored accountability concerns regarding the assignment of fault and responsibility for car accidents using self-driving technology [60, 76, 77, 88]. Other studies considered that more attention is needed to scrutinize each application across the AI life cycle [41, 71, 94], to explainability of AI algorithms that provide to the publics the cause of AI outcomes [58], and to regulations that assign clear responsibility concerning litigation and liability [52, 89, 101, 103]. 4 Discussion Within the realm of research studies encompassed in the scoping review, the contemporary impetus for engaging the publics in ethical considerations related to AI predominantly revolves around two key motivations: innovation and legitimation. This might be explained by the current emphasis on responsible innovation, which values the publics’ participation in knowledge and innovation-making [29] within a prioritization of the instrumental role of science for innovation and economic return [33]. Considering the publics as a valuable source of knowledge that should be called upon to contribute to knowledge innovation production is underpinned by the desire for legitimacy, specifically centered around securing the publics’ endorsement of scientific and technological advancements [33, 104]. Approaching the publics’ views on the ethical challenges of AI can also be used as a form of risk prevention to reduce conflict and close vital debates in contention areas [5, 34, 105]. A second aspect that stood out in this finding is a shift in the motivations frequently reported as central for engaging the publics with AI technologies. Previous studies analysing AI national policies and international guidelines addressing AI governance [3–5] and a study analysing science communication journals [33] highlighted education, inspiration and democratization as the most prominent motivations. Our scoping review did not yield similar findings, which might signal a departure, in science policy related to public participation, from the past emphasis on education associated with the deficit model of public understanding of science and democratization of the model of public engagement with science [106, 107]. The underlying motives for the publics’ engagement raise the question of the kinds of publics it addresses, i.e., who are the publics that are supposed to be recruited as research participants [32]. Our findings show a prevalence of the general public followed by professional groups and developers of AI systems. The wider presence of the general public indicates AI and Ethics 1 3 not only what Hagendijk and Irwin [32, p. 167] describe as a fashionable tendency in policy circles since the late 1990s, and especially in Europe, focused on engaging 'the public' in scientific and technological change but also the avoidance of the issues of democratic representation [12, 18]. Additionally, the unspecificity of the “public” does not stipulate any particular action [24] that allows for securing legitimacy for and protecting the interests of a wide range of stakeholders [19, 108] while bringing the risk of silencing the voices of the very publics with whom engagement is sought [33]. The focus on approaching the publics’ views on the ethical challenges of AI through the general public also demonstrates how seeking to “lay” people’s opinions may be driven by a desire to promote public trust and acceptance of AI developments, showing how science negotiates challenges and reinstates its authority [109]. While this strategy is based on nonscientific audiences or individuals who are not associated with any scientific discipline or area of inquiry as part of their professional activities, the converse strategy—i.e., involving professional groups and AI developers—is also noticeable in our findings. This suggests that technocratic expert-dominated approaches coexist with a call for more inclusive multistakeholder approaches [3]. This coexistence is reinforced by the normative principles of the “responsible innovation” framework, in particular the prescription that innovation should include the publics as well as traditionally defined stakeholders [3, 110], whose input has become so commonplace that seeking the input of laypeople on emerging technologies is sometimes described as a “standard procedure” [111, p. 153]. In the body of literature included in the scoping review, human agency and oversight emerged as the predominant ethical dimension under investigation. This finding underscores the pervasive significance attributed to human centricity, which is progressively integrated into public discourses concerning AI, innovation initiatives, and market-driven endeavours [15, 112]. In our perspective, the importance given to human-centric AI is emblematic of the “techno-regulatory imaginary” suggested by Rommetveit and van Dijk [35] in their study about privacy engineering applied in the European Union’s General Data Protection Regulation. This term encapsulates the evolving collective vision and conceptualization of the role of technology in regulatory and oversight contexts. At least two aspects stand out in the techno-regulatory imaginary, as they are meant to embed technoscience in societally acceptable ways. First, it reinstates pivotal demarcations between humans and nonhumans while concurrently producing intensified blurring between these two realms. Second, the potential resolutions offered relate to embedding fundamental rights within the structural underpinnings of technological architectures [35]. Following human agency and oversight, the most frequent ethical issue discussed in the studies contained in our scoping review was privacy and data governance. Our findings evidence additional central aspects of the “techno-regulatory imaginary” in the sense that instead of the traditional regulatory sites, modes of protecting privacy and data are increasingly located within more privatized and business-oriented institutions [6, 35] and crafted according to a human-centric view of rights. The focus on secure ways of data storage and protection as in need to be provided from the input to the output data, the testing and certification of AI-enabled products and services, the risks of data breaches, and calls for finding a balance between widening access to data and ensuring confidentiality and respect for privacy, exhibited by many studies in this scoping review, portray an increasing framing of privacy and data protection within technological and standardization sites. This tendency shows how forms of expertise for privacy and data protection are shifting away from traditional regulatory and legal professionals towards privacy engineers and risk assessors in information security and software development. Another salient element to highlight pertains to the distribution of responsibility for privacy and data governance [6, 113] within the realm of AI development through engagement with external stakeholders, including users, governmental bodies, and regulatory authorities. It extends from an emphasis on issues derived from data sharing and data misuse to facilitating individuals to exercise control over their data and privacy preferences and to advocating for regulatory frameworks that do not impede the pace of innovation. This distribution of responsibility shared among the contributions and expectations of different actors is usually convoked when the operationalization of ethics principles conflicts with AI deployment [6]. In this sense, privacy and data governance are reconstituted as a “normative transversal” [113, p. 20], both of which work to stabilize or close controversies, while their operationalization does not modify any underlying operations in AI development. Diversity, nondiscrimination and fairness, societal and environmental well-being, technical robustness and safety, transparency, and accountability were the ethical issues less frequently discussed in the studies included in this scoping review. In contrast, ethical issues of technical robustness and safety, transparency, and accountability “are those for which technical fixes can be or have already been developed” and “implemented in terms of technical solutions” [12, p. 103]. The recognition of issues related to technical robustness and safety expresses explicit admissions of expert ignorance, error, or lack of control, which opens space for politics of “optimization of algorithms” [114, p. 17] while reinforcing “strategic ignorance” [114, p. 89]. In the words of the sociologist Linsey McGoey, strategic ignorance refers to “any actions which mobilize, manufacture or exploit unknowns AI and Ethics 1 3 in a wider environment to avoid liability for earlier actions” [115, p. 3]. According to the analysis of Jobin etal. [11] of the global landscape of existing ethics guidelines for AI, transparency comprising efforts to increase explainability, interpretability, or other acts of communication and disclosure is the most prevalent principle in the current literature. Transparency gains high relevance in ethics guidelines because this principle has become a pro-ethical condition “enabling or impairing other ethical practices or principles” [Turilli and Floridi 2009, [11], p. 14]. Our findings highlight transparency as a crucial ethical concern for explainability and disclosure. However, as emphasized by Ananny and Crawford [116, p. 973], there are serious limitations to the transparency ideal in making black boxes visible (i.e., disclosing and explaining algorithms), since “being able to see a system is sometimes equated with being able to know how it works and governs it—a pattern that recurs in recent work about transparency and computational systems”. The emphasis on transparency mirrors Aradau and Blanke’s [114] observation that Big Tech firms are creating their version of transparency. They are prompting discussions about their data usage, whether it is for “explaining algorithms” or addressing bias and discrimination openly. The framing of ethical issues related to accountability, as elucidated by the studies within this scoping review, manifests as a commitment to ethical conduct and the transparent allocation of responsibility and legal obligations in instances where the publics encounters algorithmic deficiencies, glitches, or other imperfections. Within this framework, accountability becomes intricately intertwined with the notion of distributed responsibility, as expounded upon in our examination of how the literature addresses challenges in privacy and data governance. Simultaneously, it converges with our discussion on optimizing algorithms concerning ethical concerns on technical robustness and safety by which AI systems are portrayed as fallible yet eternally evolving towards optimization. As astutely observed by Aradau and Blanke [114, p. 171], “forms of accountability through error enact algorithmic systems as fallible but ultimately correctable and therefore always desirable. Errors become temporary malfunctions, while the future of algorithms is that of indefinite optimization”. 5 Conclusion This scoping review of how publics' views on ethical challenges of AI are framed, articulated, and concretely operationalized in the research sector shows that ethical issues and publics formation are closely entangled with symbolic and social orders, including political and economic agendas and visions. While Steinhoff [6] highlights the subordinated nature of AI ethics within an innovation network, drawing on insights from diverse sources beyond Big Tech, we assert that this network is dynamically evolving towards greater hybridity and boundary fusion. In this regard, we extend Steinhoff's argument by emphasizing the imperative for a more nuanced understanding of how this network operates within diverse contexts. Specifically, within the research sector, it operates through a convergence of boundaries, engaging human and nonhuman entities and various disciplines and stakeholders. Concurrently, the advocacy for diversity and inclusivity, along with the acknowledgement of errors and flaws, serves to bolster technical expertise and reaffirm the establishment of order and legitimacy in alignment with the institutional norms underpinning responsible research practices. Our analysis underscores the growing importance of involving the publics in AI knowledge creation and innovation, both to secure public endorsement and as a tool for risk prevention and conflict mitigation. We observe two distinct approaches: one engaging nonscientific audiences and the other involving professional groups and AI developers, emphasizing the need for inclusivity while safeguarding expert knowledge. Human-centred approaches are gaining prominence, emphasizing the distinction and blending of human and nonhuman entities and embedding fundamental rights in technological systems. Privacy and data governance emerge as the second most prevalent ethical concern, shifting expertise away from traditional regulatory experts to privacy engineers and risk assessors. The distribution of responsibility for privacy and data governance is a recurring theme, especially in cases of ethical conflicts with AI deployment. However, there is a notable imbalance in attention, with less focus on diversity, nondiscrimination, fairness, societal, and environmental wellbeing, compared to human-centric AI, privacy, and data governance being managed through technical fixes. Last, acknowledging technical robustness and safety, transparency, and accountability as foundational ethics principles reveals an openness to expert limitations, allowing room for the politics of algorithm optimization, framing AI systems as correctable and perpetually evolving. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s4368102300387-1. Acknowledgements The authors would like to express their gratitude to Rafaela Granja (CECS, University of Minho) for her insightful support in an early stage of preparation of this manuscript, and to the AIDA research netwrok for the inspiring debates. Author contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by HM, SS, and LN. The first draft of the manuscript was written by HM and SS. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.