Promoting human-centred AI in the workplace. Trade unions and their strategies for regulating the use of AI in Germany

Krzywdzinski, Martin; Gerst, Detlef; Butollo, Florian Article — Published Version Promoting human-centred AI in the workplace. Trade unions and their strategies for regulating the use of AI in Germany Transfer: European Review of Labour and Research Provided in Cooperation with: WZB Berlin Social Science Center Suggested Citation: Krzywdzinski, Martin; Gerst, Detlef; Butollo, Florian (2022) : Promoting humancentred AI in the workplace. Trade unions and their strategies for regulating the use of AI in Germany, Transfer: European Review of Labour and Research, ISSN 1996-7284, Sage, Thousand Oaks, CA, Iss. OnlineFirst Articles, pp. --, https://doi.org/10.1177/10242589221142273 This Version is available at: https://hdl.handle.net/10419/267782 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1177/10242589221142273 Transfer 1 –17 © The Author(s) 2022 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/10242589221142273 journals.sagepub.com/home/trs Promoting human-centred AI in the workplace. Trade unions and their strategies for regulating the use of AI in Germany Martin Krzywdzinski WZB Berlin Social Science Center and Weizenbaum Institute for the Networked Society, Germany Helmut Schmidt University, Germany Detlef Gerst IG Metall, Frankfurt, Germany Florian Butollo Weizenbaum Institute for the Networked Society, Germany Summary The use of artificial intelligence (AI) is changing the world of work. For trade unions, the issue of how to regulate the use of AI is a central but difficult topic because the technology is still at an early stage and experience on its use limited. Focusing on Germany, this article addresses the following questions: (1) what areas of application and use cases for AI are relevant for trade unions and works councils?, (2) what role do trade union positions and demands play in the political discussion on regulating the use of AI?, (3) what strategies are trade unions using to influence the regulation and use of AI in the workplace?, and (4) what experiences are they gaining during this process? Reviewing trade union strategies, this article shows which concepts of human-centred AI the trade unions are trying to promote, how they try to ensure that works councils and trade unionists get appropriate training to understand the new technologies, and how dealing with AI is changing the way works councils work. The article also shows how the characteristics of the German system of industrial relations influence discussions on AI and the processes of implementing it in the workplace. Résumé L'utilisation de l'intelligence artificielle (IA) est en train de changer le monde du travail. Pour les syndicats, la question de savoir comment réglementer le recours à l'IA dans le monde du Corresponding author: Martin Krzywdzinski, WZB Berlin Social Science Center and Weizenbaum Institute for the Networked Society, Reichpietschufer 50, Berlin, 10785, Germany. Email: [email protected] 1142273TRS0010.1177/10242589221142273TransferKrzywdzinski et al. research-article2022 Main Article 2 Transfer 00(0) travail constitue un enjeu à la fois capital et difficile, dans la mesure où cette technologie en est encore à ses débuts et où l'expérience de son utilisation est limitée. En se concentrant sur l'Allemagne, cet article aborde les questions suivantes : (1) quels sont les domaines d'application et les cas d'utilisation de l'IA qui sont pertinents pour les syndicats et les comités d'entreprise ? (2) quel est le rôle des positions et des revendications des syndicats dans le débat politique sur la réglementation de l'utilisation de l'IA ? (3) quelles sont les stratégies utilisées par les syndicats pour influencer la réglementation et l'utilisation de l'IA sur le lieu de travail ? et (4) quelles sont les expériences qu'ils tirent de ce processus ? L'analyse des stratégies des syndicats permet de définir les concepts d'une IA centrée sur l'être humain que les syndicats tentent de promouvoir, la manière dont ils cherchent à s'assurer que les comités d'entreprise et les syndicalistes bénéficient d'une formation adéquate pour comprendre les nouvelles technologies, et l’impact de la gestion de l'IA sur le mode de fonctionnement des comités d'entreprise. L'article met également en évidence la manière dont les caractéristiques du système allemand des relations industrielles influencent les débats sur l'IA et les modalités de sa mise en œuvre sur le lieu de travail. Zusammenfassung Der Einsatz künstlicher Intelligenz (KI) verändert die Arbeitswelt. Für die Gewerkschaften ist die Frage, wie der Einsatz von KI am Arbeitsplatz reguliert werden soll, ein zentrales, aber schwieriges Thema, denn diese Technologie befindet sich noch in einem frühen Entwicklungsstadium, und die Erfahrungen mit ihrer Verwendung sind begrenzt. Der vorliegende Artikel befasst sich in erster Linie mit Deutschland und geht folgenden Fragen nach: (1) Welche Anwendungsbereiche und Anwendungsfälle für KI sind relevant für Gewerkschaften und Betriebsräte? (2) Welche Rolle spielen Standpunkte und Forderungen der Gewerkschaften in der politischen Diskussion über die Regulierung des Einsatzes von KI? (3) Welche Strategien nutzen die Gewerkschaften, um Einfluss auf die Regulierung und den Einsatz von KI am Arbeitsplatz zu nehmen? und (4) Welche Erkenntnisse gewinnen sie im Rahmen dieses Prozesses? Der vorliegende Artikel stellt Gewerkschaftsstrategien für eine menschenzentrierte KI vor. Diese umfassen Qualifizierungsstrategien für Betriebsräte und Gewerkschafter:innen und Veränderungen in deren Arbeitsweise. Der Artikel zeigt zudem, wie die Eigenheiten des deutschen Systems der Arbeitsbeziehungen die Diskussionen über KI und die Prozesse der Implementierung dieser Systeme beeinflussen. Keywords Industrial relations, trade unions, co-determination, technological change, digitalisation, artificial intelligence Introduction The use of artificial intelligence (AI) and its effects on the world of work are controversial topics that have been the subject of heated discussion (e.g. Deutscher Bundestag, 2020). While job cuts, reductions in the human workforce’s capacity to act, discrimination and surveillance are potential dangers, AI also offers opportunities to develop new products, increase the efficiency of work processes and relieve human workers of the need to perform repetitive and stressful tasks. Some who wish to capitalise on the opportunities of the new technology have called for ‘ethical’ technology design: technology development, they say, should focus on AI that supports people, is transparent and understandable for its users and has also been tested against potential dangers and biases (Roberts et al., 2021a). Krzywdzinski et al. 3 For trade unions, the issue of how to regulate and ensure ‘ethical’ AI use in the world of work is a central but difficult topic because the technology is still at an early stage of development, experience on its use is limited and uncertainties about its further development are substantial (Krzywdzinski et al., 2022b; Matuschek and Kleemann, 2018). Trade unions are trying to use their previous experience with technological change to tackle AI, but at the same time there have been increasing calls for trade unions (and works councils) to fundamentally change the way they work as a prerequisite for successfully influencing the design, introduction and use of AI (Gerst, 2020b). Against this background, this article addresses the following questions: (1) What areas of application and use cases for AI are relevant for trade unions and works councils, and what problems arise in this context? (2) What role do trade union positions and demands play in the political discussion on regulating the use of AI in the workplace in Germany? (3) What strategies/approaches are trade unions and works councils in Germany using to influence the regulation and use of AI in the workplace? (4) What are the experiences of trade unions and works councils and what conclusions can be drawn for the future? The article shows that relevant AI applications in companies consist not only of the more farreaching use cases in the field of HR (personnel diagnostics in recruitment or career management) currently highlighted in public discussions. Indeed, key fields of workplace AI solutions are cognitive assistance systems, process monitoring and process optimisation, i.e., applications often less in the focus of the debate. As in the case of HR-related applications, these use cases provoke questions regarding data protection and data quality, but also raise the general question of what skills and forms of work organisation will be needed to use AI technologies to increase job quality and secure employment. Accordingly, trade union strategies should focus not only on establishing rules for data protection but also address how works councils and trade unionists can receive sufficient training to understand the new technologies and be able to oversee their introduction. This involves providing appropriate training and introducing changes in work organisation. The pace and the opacity of technological change, as well as the amount of social innovation involved, may overwhelm works councils. To counter this risk, a change in the way works councils work is necessary, as discussed in this article. The focus is on the German system of industrial relations, a system with some special features regarding the introduction of new technologies and the influence of labour representatives. In addition to regulation through industry-level collective agreements, German industrial regulations are characterised by extensive co-determination at workplace level, with the introduction of new technologies an important issue for negotiation and compromise-building between management and labour (Bosch and Schmitz-Kießler, 2020; Haipeter, 2020). The article reflects in particular on the experiences of IG Metall, the German metalworkers’ union. Due to the relatively high unionisation rate in the metal sector, these experiences differ from some parts of the service sector or the new area of platform work, where trade unions are struggling to organise activities that remain scattered and precarious (Vandaele, 2018). Nevertheless, lessons for other countries and sectors can be drawn from the trade union experiences reflected in this article, as will be argued in the conclusions. The article is based on an evaluation of existing research literature and on the experiences gained by the authors in the course of their work. As head of IG Metall’s ‘Future of Work’ department, 4 Transfer 00(0) Detlef Gerst chairs strategic discussions in the union, oversees the training of works councils and represents the union in discussions on digitalisation with employers’ associations and policymakers. Florian Butollo conducts research on the digital transformation of enterprises and work and was an expert member of the German Bundestag’s Study Commission on Artificial Intelligence. Martin Krzywdzinski is a researcher with a special interest and long experience in technological change in the manufacturing sector. In Section 2, we review the state of research on the use and regulation of AI in the world of work. Section 3 looks at the debates on regulating AI in Germany and the positions of trade unions in these debates, while Section 4 focuses on trade union activities to influence the regulation and implementation of AI in workplaces. The article ends with overarching conclusions. State of research Fields of application and AI use cases In the discussion about the use of AI in the world of work, the numbers being bandied about are hardly small, with the McKinsey Global Institute (2018) predicting ‘160 billion euros additional GDP in Germany by 2030’ (Buxmann and Schmidt, 2019). However, these euphoric forecasts are thrown into sharp relief by the uncertainty in both public and scientific discussions: What can AI really do and how developed is the technology today? What applications already exist and what is the experience with them? The discourse on AI in the workplace is part of a broader discussion about new concepts of ‘algorithmic management’, i.e., new forms of technical direction, evaluation, and disciplining of workers (Aloisi and De Stefano, 2022; Kellogg et al., 2020). Although the notion of algorithmic management has several merits, its drawbacks are that it attempts to capture very different technologies and their uses by management in a single term and reflecting just one dimension of AI – the control of work performance (Krzywdzinski and Gerber, 2021). We follow up on discussions of algorithmic management but focus on the specific features of the use of AI technologies in the workplace. There are different definitions of AI (Marcus and Davis, 2019: 41 ff). Some distinguish between ‘strong’ AI, defined as resembling human intelligence, and ‘weak’ AI which basically covers pattern recognition, modelling and deduction algorithms for narrowly defined problems (The Federal Government, 2018). Definitions of ‘weak’ AI distinguish between two types: classical knowledge or expert systems – the ‘good old-fashioned AI’ – based on pre-programmed logical operations and deduction rules; and systems based on so-called ‘machine learning’ and used to identify patterns in huge data sets. This latter type of AI system has become increasingly important, although here, too, there are several different approaches. Knowledge systems and machine learning are frequently combined in applications. Key application areas of AI in the world of work include (FraunhoferAllianz Big Data, 2017): (1) Cognitive assistance systems. Used in a wide range of activities, these for example guide workers in assembly processes, in picking processes, or in handling administrative processes (Krzywdzinski et al., 2022b). They provide information about the work steps to be performed, possibly with additional information, and in some cases serve to control the execution of tasks. Increasingly, they are being equipped with AI-based speech and image recognition systems to enable intuitive interaction between humans and assistance systems. (2) Monitoring and controlling networked systems, for example, in manufacturing. In this case, sensor data are analysed to detect deviations from standard processes and thus Krzywdzinski et al. 5 promptly detect any malfunctions or identify causes of malfunctions through so-called ‘industrial analytics’. The classic example is predictive maintenance (Acatech, 2015), where the aim is to detect signs of wear in machines at an early stage and to plan and instruct maintenance activities. The system performs certain tasks previously undertaken by skilled workers, possibly leading, under certain circumstances, to them being replaced by semi-skilled workers. (3) Analysis of personnel data, in the form of so-called ‘people analytics’. Several software systems now exist that are purportedly able to automate recruitment processes. AI software can, for instance, analyse a video in terms of speech, facial expressions and gestures to select candidates to be invited for a face-to-face interview, thereby reducing the time needed to process applications and increase applicant diversity (Daugherty and Wilson, 2018). Some studies, however, have come to rather critical conclusions, questioning the way such applications work (for example, whether analysing speech, facial expressions and gestures is really suitable for making HR decisions), or the lack of data protection (Spielkamp and Gießler, 2020; Todolí-Signes, 2019). (4) Autonomous vehicles, transportation systems, robots. The term ‘autonomous’ refers to the ability of a machine to function without human guidance, for example moving to a given destination or performing a processing operation (gripping, moving, shifting, assembling, welding), with sensor data analysed to avoid collisions or move the vehicle or robot arm more precisely. Overall, this is an automation technology making some previous activities redundant (Groshen et al., 2019; Leonard et al., 2020). While the deployment of autonomous vehicles for individual mobility is still relatively far off despite all predictions, automated production lines and automated guided vehicles for transportation within factories have been used on a larger scale for some time. It should however be noted that many of these current applications have been solely introduced in an experimental mode and that only a small proportion of companies have implemented AI applications at all. For example, the German IT industry association Bitkom (2021) reported that only 8 per cent of German companies (mainly large ones) were using AI in 2021. Similar findings are reported by other studies (BMWI, 2020; PWC, 2019). While Krzywdzinski et al. (2022a) noted accelerated digitalisation and increasing use of AI in some companies during the COVID-19 pandemic, this acceleration remained limited to certain industries and a minority of companies. For trade unions, however, the topic of AI remains very important, as its significance in the workplace is set to increase. Challenges of implementing AI The introduction of AI systems in the workplace requires mutual adjustments to both the technological core and the organisational environment. Machine learning algorithms need to process data that are meaningful in terms of the desired functions, while delivering results that can be understood and used by humans. In computer science and information systems research, one focus relevant to the applicability of AI systems is on ‘explainable AI’ (XAI), i.e., designing AI systems to meet transparency and interpretability requirements (Adadi and Berrada, 2018). As Meske et al. (2022) have noted, there are now various technical approaches that can be implemented depending on the nature of the data and AI approaches used. ‘Feature attribution’ approaches show how certain data characteristics contribute to model results. Other approaches use selected data to illustrate how the model works. Some approaches attempt to produce an accurate, formal representation of the model, while others work with ‘surrogate models’, i.e., interpretable models intended to 6 Transfer 00(0) resemble the true black-box model. In the following, we shall not focus on this technically oriented discussion. In organisational research, the main discussion regarding the implementation of AI systems is about the criteria for achieving transparent and understandable AI in organisations. This research comes to several conclusions: The first key point concerns the careful definition of the objectives of the AI model and the appropriate selection of the specific AI variant. In an analysis of an AI system used by the Danish Business Authority to check registration documents and annual reports by companies for potential inaccuracies, Asatiani et al. (2020) showed that model development required a close exchange between developers and users. Case studies in the sociology of labour have also suggested that the introduction of AI-based assistance systems would not be possible without employee participation (Research Centre for Education and the Labour Market (ROA) and INPUT Consulting gGmbH, 2020; Van den Broek et al., 2021). This is because the processes of configuring machine learning algorithms (e.g. selecting data, defining the learning model) and building knowledge bases require a sound understanding of relevant causes and effects and thus human expertise (Baethge-Kinsky et al., 2018; Butollo et al., 2019; Walker, 2017). The second issue relates to the quality of the data with which AI systems are trained and later used. In a feasibility study on the implementation of preventive maintenance at a railway company, Marsh et al. (2016) showed that appropriate data sets can often only be produced with intensive user involvement. Asatiani et al. (2020) and Van den Broek et al. (2021) argued that the data must also be continuously reviewed by experts. The third issue pertains to the interpretation and verification of results by humans. In the cases analysed by Asatiani et al. (2020) and Van den Broek et al. (2021), all critical cases indicated by the model were checked and validated by humans. Organisational research thus emphasises that, when seeking to achieve meaningful and usable outcomes, it is important to ensure AI users’ participation in developing the models, selecting and maintaining data, and interpreting and verifying the results. What is rarely addressed in organisational research, however, is the role of employee representatives. To date, the influence of employee representatives on AI development and implementation in the workplace has mainly been discussed from a legal (and occasionally a sociology of work) perspective. Research has focused primarily on issues of data protection and discrimination. AI systems (especially in the form of machine learning systems) are very ‘data hungry’. They require large amounts of data, the production of which implies the collection of personal data in operational work processes and thus risks of surveillance or at least a violation of data protection rules (Bales and Stone, 2020; Kim and Bodie, 2021; Moore, 2019; Todolí-Signes, 2019). The risk of discrimination is clearest in the use of AI in personnel selection, recruitment and workforce development (Asatiani et al., 2020; Daugherty and Wilson, 2018). Specific challenges arise with respect to ethical issues: how good is the quality of the data and to what extent does it reflect a discriminatory status quo? How transparent are the system’s decision-making criteria? How can discrimination be ruled out? Is it acceptable to base selection decisions solely on such systems or should human review be required? Overall, research to date shows that implementing AI technologies presents a number of challenges set to grow in importance in the future. These concern the changes in skill requirements and work organisation that accompany AI and the dangers of surveillance and discrimination. As shown above, organisational research shows that only intensive user involvement in model development, data verification and interpreting model results leads to meaningful and understandable AI systems. To reap the benefits of these technologies for employees and to avoid their dangers, works councils and trade unions need to exert influence on the technology implementation Krzywdzinski et al. 7 processes. Yet we have very little experience and research on how employee representatives can best influence technology implementation. This is the subject of our following analysis. The regulatory discussion in Germany In Germany, trade union action strategies are developing in the context of a multi-faceted discussion. On the one hand, there is the perceived global race for leadership in AI technology. In contrast to Industry 4.0 applications where German companies and developers have a strong position, in the field of AI applications technological leadership tends to lie with tech companies and researchintensive universities in the USA (Zhang et al., 2022). Significant momentum has also come from China, where substantial private investment in the field of AI research has gone hand in hand with strong government support (Lee, 2018; Roberts et al., 2021b). German policy-makers have identified weaknesses in transferring research results, such as low levels of AI-related patent applications (Deutscher Bundestag, 2020). Accordingly, increasing Germany’s (and Europe’s) competitiveness in the field of AI is a central goal of the 2018 AI strategy developed by the German federal government (The Federal Government, 2018) and the federal states or Länder (Jobin et al., 2021). On the other hand, there has been a resurgence of corporatist coordination between the state and associations. Discussions on digitalisation and Industry 4.0 (Haipeter, 2020; Pardi et al., 2020) and in other fields (e.g. Busemeyer et al., 2022) in the context of the COVID-19 crisis (Fuchs and Sack, 2021; Lechowski et al., 2021) have featured intensified coordination between state and corporate players. In line with this development, the German government’s AI strategy includes an explicit commitment to the ‘responsible development and use of AI that serves the good of society’ and to a ‘broad societal dialogue’ on its use (The Federal Government, 2018: 7). Key steps in achieving these goals include the development of a dialogue on the ‘human-centred’ use of AI in the world of work, strengthening co-determination in this area, and the development of a dialogue on the development and use of AI systems complying with data protection law. Human-centred AI is defined as ‘primarily focused on the well-being and dignity of people’, ‘bringing social benefit’, and ‘preserving the self-determination of people as agents and their freedom to make decisions’ (Deutscher Bundestag, 2020). This is also consistent with the European-level discussion on ‘ethical’ AI (Roberts et al., 2021a), as the debates on the EU regulation of AI provide an important frame for developments in Germany (Justo-Hanani, 2022). The corporatist orientation of the German AI strategy can be observed at several points. First, company and employee representatives were involved in the consultation and expert bodies that played a role in developing the German AI strategy. One example is the German Bundestag’s Commission ‘Artificial Intelligence – Social Responsibility and Economic, Social and Ecological Potentials’ established in 2018 and including corporate and trade union experts. It presented a comprehensive report in 2020 (Deutscher Bundestag, 2020), the basic consensus of which was that AI had enormous economic and social potential that should be specifically promoted and shaped. The Commission’s guiding principles were the goal of attaining human-centred AI and the idea that AI development in Europe should fulfil ethical maxims of individual self-determination and freedom from discrimination. However, the focus on compromise also entailed limitations. For example, the Commission ultimately failed to formulate a joint recommendation on a system of risk classes (so-called ‘criticality’) for AI that would subject high-risk applications to stricter political regulation. There were also differing views on innovation policy, with some participants advancing neoliberal views while others argued for an increase in technology funding to enable a strategic economic and industrial policy able to address future societal issues (‘missionoriented innovation’). 8 Transfer 00(0) Looking at the use of AI in the workplace, the Commission’s recommendations were largely consistent with trade union positions, emphasising the need to systematically monitor how AI use impacted employment. Fears of a far-reaching substitution of labour were met with scepticism. The Commission argued that the autonomy of human work should not be restricted when AI and human systems work together, stating that the ability of workers to understand and judge automated decisions should be promoted. It suggested conducting research on which new skills will be required and correspondingly expanding education and training efforts. It also called for a modernisation of co-determination, to tackle the new challenges arising from the dynamics of AI systems and their lack of transparency. Works councils should therefore be able to participate ‘just as effectively in the definition of the objectives and configuration of AI systems as in the evaluation, operation and further development of the socio-technical conditions of use’ (Deutscher Bundestag, 2020: 321, authors’ translation). A second indicator of the corporatist nature of the AI discussion in Germany is the involvement of trade unions in AI standardisation processes. Standardisation is part of the German government’s AI strategy and is coordinated by the German Institute for Standardisation (DIN) and the German Commission for Electrical, Electronic & Information Technologies (DKE) (Wahlster and Winterhalter, 2020). In line with the AI strategy, standardisation involves societal stakeholders, including trade unions. The goal is to develop standards for AI data models, security, criticality and quality criteria. Finally, the third indicator of the corporatist orientation in the AI debate is the reform of co-determination. Even before the discussion on AI started, the German Works Constitution Act (BetrVG) contained relatively favourable rules for employee representatives. Generally speaking, when technical innovations are introduced, German works councils have the right to be informed and consulted over the plans in good time (§90 BetrVG). In addition, all technical systems that can be used for controlling performance and behaviour are subject to co-determination under §87. Nonetheless, there were discussions on the extent to which co-determination needs to be further specified with respect to AI systems and, in particular, whether works council resources need to be significantly strengthened. The Works Council Modernisation Act, which came into force in June 2021, represents an initial and still very cautious response to this debate, providing three innovations with regard to AI: (1) Works council consultation rights: When AI systems are introduced, the works council can request the involvement of an expert, to be financed by the company, to assist the works council in assessing the mode of operation and consequences of the technology. (2) Works council information rights when technical innovations and changes to work processes explicitly include the introduction of AI applications (§90). (3) Works council co-determination rights in the case of recruitment, transfers, regrouping and dismissals also apply when AI applications are used in these HR processes (§95). This implies that the introduction and use of AI in these areas requires works council consent and that all decisions made when working with these systems must be verifiable by the works council. 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