Description

This track welcomes scholars and practitioners presenting applied cases of tools or approaches to fairness, accountability and transparency, including the domains of government, the public sector and civil society. Insights from activism and advocacy are also welcomed. Operational insights from practice should be framed in relation to the overall agenda of conceptualizing ACM FAT* issues, and could overlap with the topics mentioned in other tracks. Submissions may focus on lessons from the deployment of systems, applications and software, on their auditing and evaluation, or on governance issues in relation to these. They may also offer insights from activism and advocacy, and the central role these play in conceptualizing what is fair and accountable, assessing real-world and deployed systems, and communicating with the broader public. 

Evaluation

Each paper will receive 3 peer-reviews and, possibly, by 1 cross-disciplinary review drawn from the program committees of Tracks 1-3. The evaluation criteria for the review will include: 

  • Relevance to the themes of the conference;
  • Quality of submission as measured by accuracy, clarity, comprehensiveness, and depth of exposition, including contextualizing the work in the relevant field(s); 
  • Novelty of the contributions and problem domain; and,
  • Potential for broader impact, including across other disciplines and real-world systems.

The selection of papers will follow the same high quality standards of the other tracks.

Areas of interest

4.1 Deployed applications, systems and software: covers large-scale deployed systems and components (software or open source libraries) that aim to become world-recognized tools for fairness, accountability or transparency aware computing systems. 

4.2 Algorithmic audits, evaluations, benchmarks of real-world systems: covers qualitative, quantitative, and experimental real-world experiences on auditing deployed computing systems, on comparing state-of-the-art methodologies, and on collecting gold standard datasets.

4.3 Experiences in governance: standardization, activism and communication. Papers cover real-world experiences in the governance of ACM FAT* issues in the development of standards, in political and civil rights activism, in journalism and community engagement.

Sub-disciplines

Authors should select one or more main discipline(s) and/or domain(s) for their paper, from the lists of Track T1-T3. Peer reviewers for a paper will be experts in the selected discipline(s)/domain(s), so please select them judiciously.

Program Committee (to be updated)

  • Virgilio Almeida, Universidade Federal de Minas Gerais
  • Meredith K. Broussard, New York University
  • Stefan Bucur, Google
  • Florian Cech, Vienna University of Technology / TU Wien
  • Rumman Chowdhury, Accenture
  • Moustapha Cisse, Facebook
  • Niva Elkin-Koren, Haifa Center for Law & Technology
  • Jamie Grace, Sheffield Hallam University
  • Sara Hajian, NTENT
  • Michael Hind, IBM Research
  • Soheil Human, Vienna University of Economics and Business
  • Malavika Jayram, Digital Asia Hub
  • Frederike Kaltheuner, Privacy International
  • Eren Kursun, Columbia University
  • Tomas Laurenzo, School of Creative Media, City University of Hong Kong
  • Brenda Leong, Future of Privacy Forum
  • Nishtha Madaan, IBM Research AI- India
  • Brent Mittelstadt, Oxford University
  • Laura Montoya, Accel.AI
  • Niels ten Oever, University of Amsterdam
  • David Powell, Hampshire Constabulary
  • Inioluwa Deborah Raji, University of Toronto
  • Christine Rinik, Winchester University
  • Michael Rovatsos, University of Edinburgh
  • Nithya Sambasivan, Google
  • Prasanna Sattigeri, IBM Research
  • Nishant Shah, Dutch Art Institute
  • Jatinder Singh, University of Cambridge
  • Steffen Staab, Institute for Web Science and Technologies, University of Koblenz-Landau and WAIS Research Group, University of Southampton
  • Sophie Stalla-Bourdillon, Southampton University
  • Anne Washington, New York University
  • Muhammad Bilal Zafar, Bosch Center for Artificial Intelligence
  • Kalapriya kannan, IBM Research

Track Chairs