2020-01-27 – ACM FAT*: Third Annual ACM Conference on Fairness, Accountability, and Transparency

Conference on January 27-30, 2020 in Barcelona, Spain. Brings together researchers and practitioners interested in fairness, accountability, and transparency in socio-technical systems. It builds on the success of the inaugural 2018 conference held in New York, and the 2019 conference held in Atlanta.

In this edition, our goal has been to sustain and further improve the high quality of computer science research in this domain, while simultaneously extending the focus to law and social sciences and humanities research. To this end, we have appointed dedicated selection committees and track chairs securing excellence in computer science (CS), social sciences and humanities (SSH), and legal scholarship (LAW), thus creating a rich program centered on computer science with strong and diverse cross-disciplinary components. We are also delighted to host this conference in Europe for the first time, and have done our best to reflect and emphasize issues and research in Europe on these topics.

Keynote Speakers

  • Ayanna Howard — Hacking the Human Bias in AI
  • Yochai Benkler — Productivity and Power: The Role of Technology in Political Economy
  • Nani Jansen Reventlow — Making Accountability Real: Strategic Litigation


ACM FAT* 2020 Accepted Papers

Tuesday, January 28th, 2020

Session 1: Accountability. Session chair: Michael Veale

  • What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability — M. Wieringa
  • Algorithmic Realism: Expanding the Boundaries of Algorithmic Thought — B. Green; S. Viljoen
  • Algorithmic Accountability in Public Administration: The GDPR Paradox — S. Kang
  • Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing — D. Raji; A. SMART; R. White; M. Mitchell; T. Gebru; B. Hutchinson; J. Smith-Loud; D. Theron; P. Barnes
  • Toward Situated Interventions for Algorithmic Equity: Lessons from the Field — M. Katell, M. Young, B. Herman, D. Dailey, V. Guetler, A. Tam, C. Binz, D. Raz, P. Krafft

Session 2: Explainability 1. Session chair: Jatinder Singh

  • Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches — K. Sokol; P. Flach
  • Multi-layered Explanation from Algorithmic Impact Assessments in the GDPR — G. Malgieri; M. Kaminski
  • The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons — S. Barocas; A. Selbst; M. Raghavan
  • Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting — A. Lucic; H. Haned; M. de Rijke
  • The Human Body is a Black Box: Supporting Clinical Decision-Making with Deep Learning — M. Sendak; M. Elish; M. Gao; W. Ratliff; M. Nichols; J. Futoma; A. Bedoya; S. Balu; C. O’Brien

Session 3: Auditing/Assessment 1. Session chair: Suresh Venkatasubramanian

  • Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination — N. Kallus; X. Mao; A. Zhou
  • FlipTest: Fairness Testing via Optimal Transport — E. Black; S. Yeom; M. Fredrikson
  • Implications of AI (Un-)Fairness in Higher Education Admissions: The Effects of Perceived AI (Un-)Fairness on Exit, Voice and Organizational Reputation — F. Marcinkowski, K. Kieslich, C. Starke, M. Lünich
  • Auditing Radicalization Pathways on YouTube — M. Ribeiro; R. Ottoni; R. West; V. Almeida; W. Meira Jr.
  • Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions — K. Rodolfa; E. Salomon; L. Haynes; I. Mendieta; J. Larson; R. Ghani

Session 4: Fairness 1. Session chair: Solon Barocas

  • The concept of fairness in the GDPR: a linguistic and contextual interpretation — G. Malgieri
  • Studying Up: Reorienting the study of algorithmic fairness around issues of power — C. Barabas; C. Doyle; J. Rubinovitz; K. Dinakar
  • POTs: Protective Optimization Technologies — R. Overdorf; B. Kulynych; E. Balsa; C. Troncoso; S. Gürses
  • Fair Decision Making using Privacy-Protected Data — S. Kuppam; R. McKenna; D. Pujol; M. Hay; A. Machanavajjhala; G. Miklau
  • Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data — D. Slack; S. Friedler; E. Givental

Session 5: Ethics and Policy. Session chair: Lilian Edwards

  • From Ethics Washing to Ethics Bashing: A View on Tech Ethics from Within Moral Philosophy — E. Bietti
  • Onward for the freedom of others: Marching beyond the AI Ethics — P. Terzis
  • Whose Side are Ethics Codes On? Power, Responsibility and the Social Good — A. Washington, R. Kuo
  • Algorithmic Targeting of Social Policies: Fairness, Accuracy, and Distributed Governance — A. Noriega-Campero, B. Bulle-Bueno, L. Cantu, M. Bakker, L. Tejerina, A. Pentland
  • Roles for Computing in Social Change — R. Abebe; S. Barocas; J. Kleinberg; K. Levy; M. Raghavan; D. Robinson

Session 6: Values. Session chair: Gabriela Zanfir-Fortuna

  • Regulating Transparency? Facebook, Twitter and the German Network Enforcement Act — B. Wagner, K. Rozgonyi, M. Sekwenz, J. Singh, J. Cobbe
  • The relationship between trust in AI and trustworthy machine learning technologies — E. Toreini; M. Aitken; A. van Moorsel; K. Elliott; K. Coopamootoo
  • The philosophical basis of algorithmic recourse — S. Venkatasubramanian; M. Alfano
  • Value-laden Disciplinary Shifts in Machine Learning — R. Dotan; S. Milli
  • Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making — Y. Zhang; Q. Liao; R. Bellamy

Session 7: Data Collection. Session chair: Brent Hecht

  • Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning — E. Jo; T. GebruData in New Delhi’s predictive policing system — V. Marda; S. Narayan
  • Garbage In, Garbage Out: Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From? — R. Geiger, K. Yu, Y. Yang, M. Dai, J. Qiu, R. Tang, J. Huang

Wesnesday, January 29th, 2020

Session 8: Fairness 2. Session chair: Sorelle Friedler

  • Bidding Strategies with Gender Nondiscrimination Constraints for Online Ad Auctions — M. Nasr, M. Tschantz
  • Multi-category Fairness in Sponsored Search Auctions — C. Ilvento; M. Jagadeesan; S. Chawla
  • Reducing Sentiment Polarity for Demographic Attributes in Word Embeddings using Adversarial Learning — C. Sweeney; M. Najafian
  • Interventions for Ranking in the Presence of Implicit Bias — A. Mehrotra; L. Celis; N. Vishnoi
  • The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally — L. Liu; A. Wilson; N. Haghtalab; A. Kalai; C. Borgs; J. Chayes

Session 9: Cognition and Education. Session chair: Elana Zeide

  • An Empirical Study on the Perceived Fairness of Realistic, Imperfect Machine Learning Models — G. Harrison, J. Hanson, C. Jacinto, J. Ramirez, B. Ur
  • Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models — L. Liang, D. Acuna
  • The Social Lives of Generative Adversarial Networks — M. Castelle
  • Towards a more representative politics in the ethics of computer science — J. Moore
  • Integrating FATE/Critical Data Studies into Data Science curricula: where are we going and how do we get there? — J. Bates; D. Cameron; A. Checco; P. Clough; F. Hopfgartner; S. Mazumdar; L. Sbaffi; P. Stordy; A. de León

Session 10: Auditing/Assessment 2. Session chair: Michael Ekstrand

  • Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information — S. Dean; S. Rich; B. Recht
  • Bias in word embeddings — O. Papakyriakopoulos; S. Hegelich; J. Serrano; F. Marco
  • What does it mean to ‘solve’ the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems — J. Sánchez-Monedero, L. Dencik, L. Edwards
  • Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices — M. Raghavan; S. Barocas; J. Kleinberg; K. Levy
  • The impact of overbooking on a pre-trial risk assessment tool — K. Lum; C. Boudin; M. Price

Session 11: Sensitive Attributes 2. Session chair: Maya Ganesh

  • Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination — A. Rieke; M. Bogen; S. Ahmed
  • Towards a Critical Race Methodology in Algorithmic Fairness — E. Denton; A. Hanna; J. Smith-Loud; A. Smart
  • What’s Sex Got to Do With Fair Machine Learning? — L. Hu, I. Kohler-Hausmann

Thursday, January 30th, 2020

Session 12: Fairness 3. Session chair: Kristian Lum

  • On the Apparent Conflict Between Individual and Group Fairness — R. Binns
  • Fairness Is Not Static: Deeper Understanding of Long Term Fairness via Agents and Environments — A. D’Amour; Y. Halpern; H. Srinivasan; P. Baljekar; J. Atwood; D. Sculley
  • Fair Classification and Social Welfare — L. Hu; Y. Chen
  • Preference-Informed Fairness — M. Kim; A. Korolova; G. Rothblum; G. Yona
  • Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy — K. Yang, K. Qinami, L. Fei-Fei, J. Deng, O. Russakovsky

Session 13: Auditing/Assessment 3. Session chair: Timnit Gebru

  • The Case for Voter-Centered Audits of Search Engines during Political Elections — E. Mustafaraj; E. Lurie; C. Devine
  • Whose Tweets are Surveilled for the Police: An Audit of a Social-Media Monitoring Tool via Log Files — G. Borradaile; B. Burkhardt; A. LeClerc
  • Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability — J. Mena Roldán; O. Pujol Vila; J. Vitrià Marca
  • Counterfactual Risk Assessments, Evaluation, and Fairness — A. Coston, A. Chouldechova, E. Kennedy, A. Mishler
  • The False Promise of Risk Assessments: Epistemic Reform and the Limits of Fairness — B. Green

Session 14: Explainability 2. Session chair: Anna Monreale

  • Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations — R. Mothilal; A. Sharma; C. Tan
  • Model Agnostic Interpretability of Text Rankers via Intent Modelling — J. Singh, A. Anand
  • Doctor XAI: An ontology-based approach to black-box sequential data classification explanations — C. Panigutti; A. Perotti; D. Pedreschi
  • Robustness in Machine Learning Explanations:Does it Matter? — L. Hancox-Li
  • Explainable Machine Learning in Deployment — U. Bhatt, A. Xiang, S. Sharma, A. Weller, A. Taly, Y. Jia, J. Ghosh, R. Puri, J. Moura, P. Eckersley

Session 15: Fairness 4. Session chair: Reuben Binns

  • Fairness and Utilization in Allocating Resources with Uncertain Demand — K. Donahue; J. Kleinberg
  • The Effects of Competition and Regulation on Error Inequality in Data-Driven Markets — H. Elzayn; B. Fish
  • Measuring Justice in Machine Learning — A. Lundgard

Historical Note: In 2018, the conference’s name was FAT* and the proceedings were published in the Journal of Machine Learning Research. The conference affiliated with ACM in 2019, and changed its name to ACM FAccT immediately following the 2020 conference.