[EUGAIN] Presentations of the 1st International EUGAIN Summer Training School

1st International EUGAIN Summer Training School (EUGAIN-STS 2022) 6-8 June 2022 Lugano, Switzerland Nicolaas Pietersz. Berchem (1620 - 1683). Harvest, KMSKB, Brussel Diversity and Inclusion in Software Engineering Alexander Serebrenik @aserebrenik a.serebr[email protected] tenure gender tenure diversity gender diversity Individual Team Process turnover productivity comm. smell: black cloud Product code smell: long method @aserebrenik geography geographic diversity Diversity in Teams: Bad or Good? Bogdan Vasilescu, Daryl Posnett, Baishakhi Ray, Mark G. J. van den Brand, Alexander Serebrenik, Premkumar T. Devanbu, Vladimir Filkov: Gender and Tenure Diversity in GitHub Teams. CHI 2015: 3789-3798 Bogdan Vasilescu, Daryl Posnett, Baishakhi Ray, Mark G. J. van den Brand, Alexander Serebrenik, Premkumar T. Devanbu, Vladimir Filkov: Gender and Tenure Diversity in GitHub Teams. CHI 2015: 3789-3798 Catolino, Palomba, Tamburri, Serebrenik, Ferrucci. Gender Diversity and Women in Software Teams: How Do They Affect Community Smells? ICSE SEIS, 2019, pp. 11-20 Lambiase,!Catolino,!Tamburri, Serebrenik,!Palomba,!Ferrucci.!Good Fences Make Good Neighbours? On the Impact of Cultural and Geographical Dispersion on Community Smells, ICSE-SEIS, 2022 Lambiase,!Catolino,!Tamburri, Serebrenik,!Palomba,!Ferrucci.!Good Fences Make Good Neighbours? On the Impact of Cultural and Geographical Dispersion on Community Smells, ICSE-SEIS, 2022 Catolino, Palomba, Tamburri, Serebrenik, Ferrucci. Gender Diversity and Community Smells: Insights from the Trenches IEEE Software 2020 Catolino, Palomba, Tamburri, Serebrenik, Ferrucci. Gender Diversity and Community Smells: Insights from the Trenches IEEE Software 2020 “diversity is a strength of the team and fosters appropriate behaviours and communications” “people of different genders allow a different comparison within the team” “Gender should not matter” Qiu,!Nolte,!Brown, Serebrenik,!Vasilescu.!Going Farther Together: The Impact of Social Capital on Sustained Participation in Open Source.!ICSE 2019, pp. 688-699 Qiu,!Nolte,!Brown, Serebrenik,!Vasilescu.!Going Farther Together: The Impact of Social Capital on Sustained Participation in Open Source.!ICSE 2019, pp. 688-699 Arthurian Romances, French (ca. 1275-1300). Beinecke MS 229, Yale University Library, USA. Attraction How well do we extend a hand to newcomers? Retention How long do different people stay engaged? But… attraction and retention are not enough! http://todogroup.org/opencodeofconduct/ Parastou Tourani, Bram Adams, Alexander Serebrenik: Code of Conduct in Open Source Projects. SANER 2017 https://cdn.pixabay.com/photo/2017/04/15/18/00/chicks-2233080_1280.jpg Emerging results ⬆ % women ❌ duration of women’s engagement 36 Denae Ford,!Reed Milewicz, Alexander Serebrenik.!How Remote Work Can Foster a More Inclusive Environment for Transgender Developers!Workshop on Gender Equality in Software Engineering, 2019, pp. 9-12 With special thanks to Denae Ford (NCSU) 37 Control of Identity Disclosure: The desire to be seen as presented Denae Ford,!Reed Milewicz, Alexander Serebrenik.!How Remote Work Can Foster a More Inclusive Environment for Transgender Developers!Workshop on Gender Equality in Software Engineering, 2019, pp. 9-12 “Stack Overflow has constrained expressions of identity. It’s up to you what content you want to fill in. GitHub for a while it was required you expose your email address to the rest of the world.” Petruzalek: The obvious drawback of not being passable is that you become an instant target. So passability is not only an identity goal, its also a mean of self-preservation 38 Economically Stable Work: Distance technical merits from identity Denae Ford,!Reed Milewicz, Alexander Serebrenik.!How Remote Work Can Foster a More Inclusive Environment for Transgender Developers!Workshop on Gender Equality in Software Engineering, 2019, pp. 9-12 39 Economically Stable Work: Distance technical merits from identity Denae Ford,!Reed Milewicz, Alexander Serebrenik.!How Remote Work Can Foster a More Inclusive Environment for Transgender Developers!Workshop on Gender Equality in Software Engineering, 2019, pp. 9-12 You cannot tell from my technical profiles that I’m transgender. I don’t make a big deal that in professional context. It’s just not relevant Ross: “Technology has totally leveled the playing field for someone like me. I can get on the internet and watch tutorials. I have the drive to spend five hours a day to teach myself a skill.” 40 So what? So what? So what? So what? Together we can create everything! Alexander Serebrenik @aserebrenik a.serebr[email protected] Nicolaas Pietersz. Berchem (1620 - 1683). Harvest, KMSKB, Brussel Tools and measures in EDI @ SE Alexander Serebrenik @aserebrenik a.serebr[email protected] How do we do it? Damian A. Tamburri,!Fabio Palomba,!Rick Kazman: Exploring Community Smells in Open-Source: An Automated Approach.!IEEE Trans. Software Eng.!47(3):!630-652!(2021) Nuri Almarimi,!Ali Ouni,!Moataz Chouchen,!Mohamed Wiem Mkaouer: csDetector: an open source tool for community smells detection.!ESEC/SIGSOFT FSE!2021:!1560-1564 CodeFace4Smells csDetector Kaiaulu Damian A. Tamburri,!Fabio Palomba,!Rick Kazman: Exploring Community Smells in Open-Source: An Automated Approach.!IEEE Trans. Software Eng.!47(3):!630-652!(2021) Nuri Almarimi,!Ali Ouni,!Moataz Chouchen,!Mohamed Wiem Mkaouer: csDetector: an open source tool for community smells detection.!ESEC/SIGSOFT FSE!2021:!1560-1564 Fabio Palomba,!Damian Andrew Tamburri,!Francesca Arcelli Fontana,!Rocco Oliveto,!Andy Zaidman,!Alexander Serebrenik: Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?!IEEE Trans. Software Eng.!47(1):!108-129!(2021) CodeFace4Smells csDetector vignettes Kaiaulu Fabio Palomba,!Damian Andrew Tamburri,!Francesca Arcelli Fontana,!Rocco Oliveto,!Andy Zaidman,!Alexander Serebrenik: Beyond Technical Aspects: How Do Community Smells Influence the Intensity of Code Smells?!IEEE Trans. Software Eng.!47(1):!108-129!(2021) Suppose your development team is working on the definition of a web-based application for the scheduling of resources. ! During the development, you recognize the existence of independent sub-teams that do not communicate with each other except through one or two of their respective members. 1.42%+0.92% 12 4% Bauer GR. Making sure everyone counts: considerations for inclusion, identification, and analysis of transgender and transsexual participants in health surveys. In: Coen S, Banister E, editors. What a difference sex and gender make. Vancouver: Institute of Gender and Health, Canadian Institutes of Health Research; 2012. pp. 59–67. 13 Bauer GR. Making sure everyone counts: considerations for inclusion, identification, and analysis of transgender and transsexual participants in health surveys. In: Coen S, Banister E, editors. What a difference sex and gender make. Vancouver: Institute of Gender and Health, Canadian Institutes of Health Research; 2012. pp. 59–67. Bauer GR, Braimoh J, Scheim AI, Dharma C (2017) Transgender-inclusive measures of sex/gender for population surveys: Mixed-methods evaluation and recommendations. PLoS ONE 12(5): e0178043.# 0 25 50 75 100 Male Female Other Transfeminine (assigned male at birth, identify as women/non-binary) Transmasculine (assigned female at birth, identify as men/non-binary) 14 Do you consider yourself to be transgender?! () Yes! () No! () Questioning! Do you consider yourself to be gender non-conforming, gender diverse, gender variant, or gender expansive?! () Yes! () No! () Questioning! Are you intersex?! () Yes! () No! () I don't know! Where do you identify on the gender spectrum (check all that apply)?! [] Woman! [] Demi-girl! [] Man! [] Demi-boy! [] Non-binary! [] Demi-non-binary! [] Genderqueer! [] Genderflux! [] Genderfluid! [] Demi-fluid! [] Demi-gender! [] Bigender! [] Trigender! [] Two-Spirit! [] Multigender/polygender! [] Pangender/omnigender! [] Maxigender! [] Aporagender! [] Intergender! [] Maverique! [] Gender confusion/Gender f*ck! [] Gender indifferent! [] Graygender! [] Agender/genderless! [] Demi-agender! [] Genderless! [] Gender neutral! [] Neutrois! [] Androgynous! [] Androgyne! [] Prefer not to answer! [] Self Identify: _________________! Open demographics https://drnikki.github.io/open-demographics/questions/gender.html 15 https://www.morgan-klaus.com/gender-guidelines.html#Surveys 16 Where do you identify on the gender spectrum? Your answer: ____________________________ https://www.morgan-klaus.com/gender-guidelines.html#Surveys Where do you identify on the gender spectrum? [ ] woman [ ] man [ ] non-binary [ ] prefer not to disclose [ ] prefer to self-describe: ________________ 10-20% 18 19 Self-presentation + Artefacts created 󲰛 Gender 20 28 https://insights.stackoverflow.com/survey/2021 With special thanks to Huilian Sophie Qiu (CMU) Huilian Sophie Qiu,!Alexander Nolte,!Anita Brown, Alexander Serebrenik,!Bogdan Vasilescu.!Going Farther Together: The Impact of Social Capital on Sustained Participation in Open Source!41st International Conference on Software Engineering (ICSE 2019), 2019, pp. 688-699 29 With special thanks to Huilian Sophie Qiu (CMU) Huilian Sophie Qiu,!Alexander Nolte,!Anita Brown, Alexander Serebrenik,!Bogdan Vasilescu.!Going Farther Together: The Impact of Social Capital on Sustained Participation in Open Source!41st International Conference on Software Engineering (ICSE 2019), 2019, pp. 688-699 30 https://www.facelytics.io/en/ (Old) https://www.picpurify.com/demo-face-gender-age.html https://www.facelytics.io/en/ (New) https://visagetechnologies.com/face-analysis/ ~30% autogenerated profile images 32 Bin Lin, Alexander Serebrenik: Recognizing gender of stack overflow users. MSR 2016: 425-429 53% (479/900) 33 34 Stefan Krüger, Ben Hermann. Can an Online Service Predict Gender? - On the State-of-the-Art in Gender Identification from Texts. Gender Equality Workshop ICSE 2019 35 Stefan Krüger, Ben Hermann. Can an Online Service Predict Gender? - On the State-of-the-Art in Gender Identification from Texts. Gender Equality Workshop ICSE 2019 36 Fariha Naz,!Jacqueline E. Rice: Sociolinguistics and programming.!PACRIM!2015:!74-79 ≠ 45 Alexander Serebrenik @aserebrenik a.serebr[email protected] The GenderMag method as a foundation for empirical studies on gender Margaret Burnett Oregon State University @gendermag gendermag.org gendermag.method more genders? men women Gender & Software •Q: How do these relate? –A: Most software has gender bias “bugs”. •Q: You’re kidding! Where? –A: in the software you create. –A: in the tools you use to do it. •Q: How much does this really matter? –A: a lot! Like a “cognitive tax” you pay every time you hit one of the bugs. 2 more genders? men women Where do these bugs lurk? •Open Source projects •Programming environments •Spreadsheets •E-Learning •Robots •Mobile Apps •Help Systems •Web Sites •Digital Libraries •… 3 Suppose … •… you want to make sure the tech you’re building is gender-inclusive. •How: –Step 1: Do a GenderMag evaluation of your tech –Step 2: Fix the inclusivity bugs revealed –Step 3: Do an empirical study to compare Before-GenderMag vs. After-GenderMag –Step 4: Analyze as per CHI’19 [Vorvoreanu et al. 2019] 4 more genders? men women Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 What NOT to do •Shrink it and Pink it is not a strategy –Dell’s pink laptops (2009) –BIC for her •Nobody is “typical” –World doesn’t divide into “typical” F vs. M –So, needs to be about debugging, not dividing. 5 Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 Step 1: GenderMag Evaluation •1. Pick a persona. eg: Abi •2. Pick a use case/scenario in your IT, eg: –in Augmented (Physical) Bookstore –“Find science fiction books” 6 Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 Step 1: GenderMag Evaluation •1. Pick a persona. eg: Abi •2. Pick a use case/scenario in your IT, eg: –in Augmented (Physical) Bookstore –“Find science fiction books” •3. Walk thru scenario via “intended” subgoals & actions –Like this… 7 42% 26% 29% 25% 33% 38% 0% 10% 20% 30% 40% 50% Most tolerant answers by 1/3 of everyone (1-2) Middle 1/3 (2.5-3) Most averse answers by 1/3 of everyone (3.5up) Facet #1: Risk 14 12% 14% 22% 25% 25% 27% 29% 29% 33% 36% 38% 45% 50% 55% 56% 56% 100% 32% 0% 50% 100% Gender&inclusiveness-issuesAverage' ' % of gender bias bugs found by 17 teams in own software. Avg = 32% •How pervasive are these biases? What has happened when the real world uses GenderMag? 15 Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 16 Step 2: Fix the inclusivity bugs •Facets Drive Fixes Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 17 Step 2: Fix the inclusivity bugs Length: was 10, now 5. (← Info Proc. Style) Numbers: gone now. (← Motivations) •Facets Drive Fixes Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 Step 3: Empirical study •Before-GenderMag vs. After-GenderMag •RQ: –Is After-GenderMag better? –For which genders? –For which facets? •Data: –Performance/errors: Action failures, success rates, etc. –Participants’ genders –Participants’ facets 18 Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 more genders? men women Scheuerman et al. “HCI Guidelines for Gender Equity and Inclusivity” https://www.morgan-klaus.com/genderguidelines.html Step 3: But wait— How to get participants’ facets? •The GenderMag facet survey •https://gendermag.org –Click on “Facet Survey” 19 Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 O1 ● ● ● ● ● - - - - - G1 O2 ● ● ■ ● ● - - - - - G2 O3 - - - - - - - - - - G3 O4 ● ■ ● ● ■ - - - - - G4 O5 ● ■ ■ ● ● - - - - - G5 O6 - - - - - - - - - - G6 O7 - - - - - ■ ● ■ ● ■ G7 O8 ■ ● ■ ■ ■ - - - - - G8 O9 - - - - - - - - - - G9 O10 ■ ■ ■ ■ ■ ■ ■ ■ ■ ■ G10 M SE R IP L M SE R IP L Original postGenderMag Does Gen derMag improve usability & inclusiveness? ●- action failure Abi facet ■- action failure Tim facet 20 Step 4: Analyze like this Issue 4 Fix by Participant [chi’19] Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 Does GenderMag improve usability & inclusiveness? Facets Redesigned,For Effects Issues 1&2 Motivations ✓ ++ Self -Efficacy ✓ ++ Risk ✓ *+ Info -Process ✓✓ +* Learning ✓✓ ++ IssueG3 Motivations ✓ +– Risk ✓ –+ Learning ✓ == IssueG4 Risk ✓✓ ++ Info -Process ✓ ++ Learning ✓ ++ Issues 5&6 Self -Efficacy ✓✓ +* Risk ✓ *+ Info -Process ✓✓ +* Learning ✓ += 21 + : post-GM better (20) - : post-GM worse (2) = : no effect (3) * : no failures (5) Step 4: Fixes by Issue by Facet [chi’19] Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 22 What about Gender? men women 100% Abi 100% Tim Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 Effects by Gender 23 w 0.6 M 0.6 1.7 0.6 0.8 0.6 0 0.5 1 1.5 2 Original post-GenderMag Women Men W W MM Step 1: GenderMag. Step 2: Fix. Step 3: Empirical Before Vs. After. Step 4: Analyze like CHI’19 What is Computer Science? The first Big-Data crisis: Administrating the entire Mesopotamian empire Solution: The invention of script Today 3400 BC Mesopotamia Digitalization: Representing information as sequences of symbols The 3 roots of Computer Science Information-and data representation Mesopotamia 1500 BC Shannon, Hamming Security:Keep data safe from unauthorized readers Information density Minimize the length of a and compression: representation Self-verifying Make data representation Codes: resilient against errors 1950 500 BC Egypt Shannon Hamming The 3 roots of Computer Science What is Computer Science? Information-and data representation Today Automation is the source of human efficiency: Executing procedures does not require the high qualification of inventors 570 BC 𝑎𝑎2+𝑏𝑏2=𝑐𝑐2 Pythagoras of Samos Algorithmics and automation The 3 roots of Computer Science What is Computer Science? Information-and data representation Today Gain knowledge and use it to automate al-Khwarizmi Euclid † 850300 BC The 3 roots of Computer Science What is Computer Science? Information-and data representation Algorithmics and automation Today Automation signifies the source of human efficiency: Executing procedures does not require the high qualification of inventors Gain knowledge and use it to automate The invention of the first calculating machine boosted the broad importance of computer science: Automation is rapidly gaining importance. Analytical Engine † 1871 † 1716 Leibniz‘ Calculator Leibniz Babbage Lovelace The 3 roots of Computer Science What is Computer Science? Information-and data representation Algorithmics and automation Technology Today Even the roots of communication technology are much older than you might think: They developed over a span of at least two millenia. ?≈0968 1837 1876 1897 1907 1969 18371969 The 3 roots of Computer Science What is Computer Science? Information-and data representation Algorithmics and automation Technology Today Computer science is as old as science and human civilization. It influenced the entire development of our species. Technology Today Computer science established itself as individual subject when: 1. Algorithms could be formulated well enough such that no improvisation (i.e. intellectual capability) was required for their execution 2. Technology was sufficiently developed such that the execution of algorithms could be delegated to machines The 3 roots of Computer Science What is Computer Science? Information-and data representation Algorithmics and automation Goals of teaching computer science at school 1. Understanding, steering and contibuting to the development of the world created by humans 2. Fostering key competencies in mathematics and languages 3. Introduce constructive thinking (core to all technical disciplines) to general school education „Let’s bring up inventors and creators of digital technologies, not only their mere consumers.“ “Life is not about having the right answer –or at least it should not be –it is about getting things to work.” Seymour Papert 1. Understanding, steering and contibuting to the development of the world created by humans Why not just leave it to the specialists? •Understanding our surroundings is one of the key responsibilities of education. •In almost all professions, automation of tasks increases. Without knowledge in computer science, children will struggle in their future jobs. In general education, computer science takes on a similar role as mathematics at the time of technical revolution. “This is not a decision about pedagogic theory but a decision about what citizens of the future need to know… The rapid and accelerating change that marks our times means that every individual will see bigger changes every few years than previous generations saw in a lifetime. So this is the choice we must make for ourselves, for our children, for our countries and for our planet: acquire the skills needed to participate with understanding in the construction what is new OR be resigned to a life of dependency.” Seymour Papert Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 2 Outline ▪How to use AI for sustainability ▪Prediction of spatial distributions (non-intrusive sensing): oConvolutional neural networks (CNNs). Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 3 Outline ▪How to use AI for sustainability ▪Prediction of spatial distributions (non-intrusive sensing): o Convolutional neural networks (CNNs). Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 4 Motivation: Success stories of machine learning -Image recongnition, connection with medical applications. ImageNet challenges, classificaiton errors of 2% (humans: 5%). -Predictive text, speech recognition, large-scale data analytics, finance... -IBM DeepBlue supercomputer defeated the worldchampion Kasparov in chess (1997) by bluntly evaluating a wide range of possible moves (bruteforce computing). -The game of Go (moves=250,turns=150) is much more complexthan chess (moves=35,turns=70) mt=10350 vs 10108 -It is not possible to bluntly compute all moves, some sort of intuition to select the best moves is needed. Reuters DeepMind Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 5 Motivation: Artificial neural networks and the game of Go -AlphaGo1is an algorithm based on artificial neural networksdeveloped by Google DeepMind. Based on supervised learning from human expertgames and reinforcement learning it was able to defeat the world-champion Lee Sedol by 4-1. Reuters AlphaGo -AlphaGo Zero2is also based on artificial neural networksand relies only on reinforcement learning playing with itself, without any human input. It defeated the previous version by 100-0. and reached super-human performance. Silver et al. (2017) 1Silver et al., Nature (2016) 2Silver et al., Nature (2017) Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 6 The Sustainable Development Goals (SDGs) •2030 Agenda for Sustainable Development adopted by all United Nations Member States in 2015 •Shared blueprint for peace and prosperity for people and the planet •Recognize that ending poverty and other deprivations must go hand-in-hand with strategies that improve health and education, reduce inequality, and spur economic growth –all while tackling climate change and working to preserve our oceans and forests •17 different Sustainable Development Goals (SDGs); 169 targets Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 7 Motivation ▪We want to answer the question: “Is there published evidence of AI acting as an enabler or an inhibitor for each of the SDG targets?” Vinuesa et al., Nature Communications 11, 233 (2020) Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 8 Motivation ▪We want to answer the question: “Is there published evidence of AI acting as an enabler or an inhibitor for each of the SDG targets?” ▪We needed to assemble a multi-disciplinary team spanning the wide range of required areas of knowledge. Vinuesa et al., Nature Communications 11, 233 (2020) Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 9 The team Vinuesa et al., Nature Communications 11, 233 (2020) R. Vinuesa Fluid mechanics, Applied AI H. Azizpour AI fundamentals I. Leite AI and social interaction M. Balaam Interaction design V. Dignnum AI ethics S. Domisch Biodiversity A. Felländer AI ethics S. D. Langhans Freshwater ecology M. Tegmark Cosmology, Applied AI F. F. Nerini Energy systems, sustainability Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 10 Dividing the 17 SDGs into 3 main pillars -We divided the 17 SDGs into 3 main categories (Stockholm Resilience Center, 2017; United Nations, 2019): Society, Economy, and Environment. Vinuesa et al., Nature Communications 11, 233 (2020) Ricardo Vinuesa: [email protected], www.vinuesalab.com, @ricardovinuesa 11 Consensus-based expert-elicitation process AI ENABLER AI INHIBITOR 1.1 By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day 11 We identified in the literature studies suggesting that AI may be an inhibitor for this target, due to the potential increase in inequalities which would hinder the achievement of this goal (1). Other references however identify AI as an enabler for this goal, in the context of using satellite data analysis to track areas of poverty and to foster international collaboration (2). (1) Nagano, A. Economic growth and automation risks in developing countries due to the transition toward digital modernity. In: Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance, Galway (Ireland), 42-50 (2018). (C) (2) Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B. & Ermon, S. Combining satellite imagery and machine learning to predict poverty. Science 353, 790-794 (2016). (A) 1.2 By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions 11 Based on the literature, AI may act as an inhibitor towards the achievement of this target, since it may lead to an increase of inequalities (1). Nevertheless, alternative views reflect that AI can enable this goal, through the use of satellite data to track areas of poverty and foster international collaboration (2); through the analysis of data from phone usage in order to predict income levels, with the aim of developing better plans of action (3,4); or using machine learning to predict drought (5). (1) Nagano, A. Economic growth and automation risks in developing countries due to the transition toward digital modernity. In: Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance, Galway (Ireland), 42-50 (2018). (C) (2) Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B. & Ermon, S. Combining satellite imagery and machine learning to predict poverty. Science 353, 790-794 (2016). (A) (3) Sundsoy, P., Bjelland, J., Reme, B.-A., Iqbal, A. M. & Jahani, E. Deep learning applied to mobile phone data for individual income classification. In: Proceedings of the International Conference on Artificial Intelligence: Technologies and Applications (ICAITA), 96-99 (2016). (A) (4) Brynjolfsson, E. & McAfee, A., The second machine age: Work, progress, and prosperity in a time of brilliant technologies, WW Norton & Company (2014). (C) (5) Mossad, A. & Alazba, A. A. Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems. Arabian Journal of Geosciences 11, 132 (2018). (A) 1.3 Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerable 11 AI may benefit the achievement of this target through the analysis of satellite data to track areas of poverty (1), or assessing data from phone usage to predict income levels and develop plans of action to avoid poverty (2). Some authors, however, claim that the advent of AI will increase economical inequalities, leaving the poor with even less resources (3). Although there is some preliminary research addressing the implementation of policies related to AI (4), this gives raise to a number of derived challenges (5). (1) Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B. & Ermon, S. Combining satellite imagery and machine learning to predict poverty. Science 353, 790-794 (2016). (A) (2) Sundsoy, P., Bjelland, J., Reme, B.-A., Iqbal, A. M. & Jahani, E. Deep learning applied to mobile phone data for individual income classification. In: Proceedings of the International Conference on Artificial Intelligence: Technologies and Applications (ICAITA), 96-99 (2016). (A) (3) Mokyr, J. Secular Stagnation? Not in Your Life. In: Secular Stagnation: Facts, Causes and Cures, Eds. Teulings, C. and Baldwin, R. 83. London: Centre for Economic Policy Research, CEPR (2014). (C) (4) Wang, W. & Siau, K. Artificial Intelligence: a study on governance, policies, and regulations. In: Proceedings of the 13th Midwest Association for Information Systems Conference, Saint Louis, US (2018). (C) (5) Gasser, U. & Almeida, V. A. F. A layered model for AI governance. IEEE Internet Computing 21, 58–62 (2017). (C) GOAL OR TARGET IN THE 2030 AGENDA FOR SUSTAINABLE DEVELOPMENT Is there published evidence of AI acting REFERENCES FOUND REASONING Goal 1: End poverty in all its forms everywhere. Main contributors: RV. Reviewers: MB. 1 reference (inhibitor) 1 reference (enabler) No evidence - Sharing of the presentation only after consultation with the author. - AI and Legitimacy 27.06.2022 Seite 22 Machine learning algorithms need to be (perceived) legitimate for realising acceptance and support AI allows throughput transparency and equality –AI legitimacy assessment predominantly depends on Outputs If the results of an algorithm are desirable, beneficial and in line with social norms and values, people will appreciate the advantages of machine learning algorithms Input Influence on process Throughput Rational decision making Output Achievement/success A suggestion how to operationalize „legitimacy“ © Fraunhofer IAO - Sharing of the presentation only after consultation with the author. - Contakt — Dr. Clemens Striebing Center for Responsible Research and Innovation at Fraunhofer IAO Phone +49 30 6807969 – 15 Mobile +49 151 16327676 [email protected]nhofer.de Center for Responsible Research and Innovation CeRRI Hardenbergstraße 20 10623 Berlin www.cerri.iao.fraunhofer.de Sources Baye, A., & Monseur, C. (2016). Gender differences in variability and extreme scores in an international context. Large-scale Assessments in Education, 4(1), 1-16. Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspective, 19(4), 25-42. http://dx.doi.org/10.1257/089533005775196732 Jiang, R., Calhoun, V. D., Fan, L., Zuo, N., Jung, R., Qi, S., ... & Sui, J. (2020). Gender differences in connectome-based predictions of individualized intelligence quotient and sub-domain scores. Cerebral Cortex, 30(3), 888-900. Murphy, K. R., Cronin, B. E., & Tam, A. P. (2003). Controversy and consensus regarding the use of cognitive ability testing in organizations. Journal of Applied Psychology, 88(4), 660. Zhang, D. C., Highhouse, S., & Rada, T. B. (2016). Explaining sex differences on the Cognitive Reflection Test. Personality and Individual Differences, 101, 425-427. https://doi.org/10.1016/j.paid.2016.06.034