Stop treating 'AGI' as the north-star goal of AI research - Borhane Blili-Hamelin; Christopher Graziul; Leif Hancox-Li; Hananel Hazan; El-Mahdi El-Mhamdi; Avijit Ghosh; Katherine A. Heller; Jacob Metcalf; Fabricio Murai; Eryk Salvaggio; Andrew Smart; Todd Snider; et al. (CoRR, 2025)

Introduction for the special issue of "Ideologies of AI and the consolidation of power": Naming power - Jenna Burrell; Jacob Metcalf (First Monday, 2024)

Participation versus scale: Tensions in the practical demands on participatory AI - Meg Young; Upol Ehsan; Ranjit Singh; Emnet Tafesse; Michele Gilman; Christina Harrington; Jacob Metcalf (First Monday, 2024)

Practicing Inclusivity in AI: Stakeholder Engagement Policy in Action - Tina M. Park; Adriana Alvarado Garcia; Juana Catalina Becerra Sandoval; Jiyoo Chang; Bill Curtis-Davidson; Remi Denton; Seeta Peña Gangadharan; Lara Groves; Jerremy Holland; Kenneth Holstein; Tamara Kneese; Jacob Metcalf; et al. (Companion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing, CSCW Companion 2024, San Jose, Costa Rica, November 9-13, 2024, 2024)

Auditing Work: Exploring the New York City algorithmic bias audit regime - Lara Groves; Jacob Metcalf; Alayna Kennedy; Briana Vecchione; Andrew Strait (The 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024, Rio de Janeiro, Brazil, June 3-6, 2024, 2024)

Null Compliance: NYC Local Law 144 and the challenges of algorithm accountability - Lucas Wright; Roxana Mika Muenster; Briana Vecchione; Tianyao Qu; Pika Senhuang Cai; Alan Smith; Jacob Metcalf; J. Nathan Matias; et al. (The 2024 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2024, Rio de Janeiro, Brazil, June 3-6, 2024, 2024)

Auditing Work: Exploring the New York City algorithmic bias audit regime - Lara Groves; Jacob Metcalf; Alayna Kennedy; Briana Vecchione; Andrew Strait (CoRR, 2024)

Null Compliance: NYC Local Law 144 and the Challenges of Algorithm Accountability - Lucas Wright; Roxana Mika Muenster; Briana Vecchione; Tianyao Qu; Pika Senhuang Cai; Jacob Metcalf; J. Nathan Matias (CoRR, 2024)

Taking Algorithms to Courts: A Relational Approach to Algorithmic Accountability - Jacob Metcalf; Ranjit Singh; Emanuel Moss; Emnet Tafesse; Elizabeth Anne Watkins (Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, 2023)

Obligations to assess: Recent trends in AI accountability regulations - Serena Oduro; Emanuel Moss; Jacob Metcalf (Patterns, 2022)

The Algorithmic Imprint - Upol Ehsan; Ranjit Singh; Jacob Metcalf; Mark O. Riedl (FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21 - 24, 2022, 2022)

A relationship and not a thing: A relational approach to algorithmic accountability and assessment documentation - Jacob Metcalf; Emanuel Moss; Ranjit Singh; Emnet Tafese; Elizabeth Anne Watkins (CoRR, 2022)

The Algorithmic Imprint - Upol Ehsan; Ranjit Singh; Jacob Metcalf; Mark O. Riedl (CoRR, 2022)

Excavating awareness and power in data science: A manifesto for trustworthy pervasive data research - Katie Shilton; Emanuel Moss; Sarah A. Gilbert; Matthew J. Bietz; Casey Fiesler; Jacob Metcalf; Jessica Vitak; Michael Zimmer (Big Data Soc., 2021)

Governing Algorithmic Systems with Impact Assessments: Six Observations - Elizabeth Anne Watkins; Emanuel Moss; Jacob Metcalf; Ranjit Singh; Madeleine Clare Elish (AIES '21: AAAI/ACM Conference on AI, Ethics, and Society, Virtual Event, USA, May 19-21, 2021, 2021)

Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts - Jacob Metcalf; Emanuel Moss; Elizabeth Anne Watkins; Ranjit Singh; Madeleine Clare Elish (FAccT '21: 2021 ACM Conference on Fairness, Accountability, and Transparency, Virtual Event / Toronto, Canada, March 3-10, 2021, 2021)

High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response - Emanuel Moss; Jacob Metcalf (Patterns, 2020)

Beyond Checklist Approaches to Ethics in Design - Richmond Y. Wong; Karen Boyd; Jake Metcalf; Katie Shilton (Companion Publication of the 2020 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2020, Virtual Event, USA, October, 2020, 2020)

Ten simple rules for responsible big data research - Matthew Zook; Solon Barocas; danah boyd; Kate Crawford; Emily Keller; Seeta Peña Gangadharan; Alyssa Goodman; Rachelle Hollander; Barbara König; Jacob Metcalf; Arvind Narayanan; Alondra Nelson; et al. (PLoS Comput. Biol., 2017)

Where are human subjects in Big Data research? The emerging ethics divide - Jacob Metcalf; Kate Crawford (Big Data Soc., 2016)

Big data analytics and revision of the common rule - Jacob Metcalf (Commun. ACM, 2016)