• Academic/Institutional Faculty (Faculty member from academic institutions): $200
• Academic Researchers (Non-faculty academic researchers such as current PhD, PostDoc, and Academic Fellows): $100
• Government & Nonprofit (Employees of the US or State Government or employees of a nonprofit organization): $200
• Industry Practitioners (Businessperson with interests in AI): $500

*Limited scholarship funding is available for those unable to cover the registration fee. Please contact Schotland McQuade at smcquade@wharton.upenn.edu.

Please note: This is a research conference open to participants from academia and industry, including faculty and current researchers. We are developing opportunities for the broader Penn community, including students. Penn undergraduate, MBA, and WEMBA students who are interested are encouraged to join our mailing list to be notified when these opportunities become available.

— Call for Papers —

The call for papers is now closed.

Thank you to all who submitted to the Accountable AI Conference. We received a wide range of work from across disciplines, reflecting the diverse perspectives shaping the future of responsible AI.

— Presenters —

• Agathe Balayn
  Postdoctoral Researcher, Microsoft Research
  “Responsible AI on the Ground: What Empirical Research Tells Us about Regulating AI”
• Felix Chen
  Emerging Scholar, Princeton University, Center for Information Technology Policy
  “Measuring the Impact of Google AI Overviews and WebGuide on User Search Behavior”
• Colleen Chien
  Professor, UC Berkeley Law
  “Surveillance Pricing: Inclusion or Exploitation?”
• Chee Hae Chung
  Postdoctoral Research Associate, Purdue University
  “From Ethics to Policy: Translating AI Ethical Guidelines into Governance Frameworks in Northeast Asia”
• Niva Elkin-Koren and Shlomi Hod
  Director, Shamgar Center for Digital Law and Innovation, Tel-Aviv University Faculty of Law; Researcher, Weizenbaum Institute
  “Transparency by Middleware: How to Address Blind Spots in AI Governance Caused by Self-Reporting”
• Neel Guha
  JD/PhD Candidate, Stanford Computer Science / Stanford Law School
  “Designing Application-Specific AI Regulation”
• Heonuk Ha
  Postdoctoral Research Associate, University of Michigan, Institute for Social Research
  “Competing (Policy Shifts) Algorithms of Governance: A Comparative Analysis of AI Executive Orders Under Presidents Trump and Biden”
• Amit Haim
  Assistant Professor, Tel Aviv University Faculty of Law
  “Untangling Hybrid Decision-making”
• Vivek Krishnamurthy
  Associate Professor, University of Colorado Law School
  “Against AI Sovereignty”
• Christina Lee
  Visiting Associate Professor of Law and Privacy and Technology Law Fellow, George Washington University Law School
  “Developers as AI Agents’ Shadow Principals”
• Anat Lior
  Assistant Professor of Law, Drexel University’s Thomas R. Kline School of Law
  “Fighting AI Harms Together: What Class Actions Can (and Can’t) Do”
• Oumou Ly
  Independent Researcher, UC Berkeley, Center for Long-Term Cybersecurity, AI Security Initiative
  “Strengthening Risk Governance: An AI Risk Management Framework for Investors”
• Artur Pericles L. Monteiro
  Resident Fellow & Schmidt Visiting Scholar on AI, Yale Law School & Jackson School
  “Scalable Oversight for Regulators”
• Paul Ohm
  Professor, Georgetown Law
  “Revealing AI’s Latent Rulebook”
• Nizan Geslevich Packin
  Professor, Zicklin School of Business, Baruch College, CUNY
  “Pretextual Privacy Theory”
• Daniel Schwarcz
  Professor, University of Minnesota Law School
  “The Limits of Regulating AI Safety Through Liability and Insurance: Lessons from Cybersecurity”
• Sepehr Shahshahani 
  Professor, Washington University Law School
  “Designing a Data Market for AI”
• Chinmayi Sharma
  Associate Professor, Fordham Law School
  “AI Dissidence by Design”
• Madhavi Singh
  Deputy Director, Thurman Arnold Project, Yale Law School
  “Preventing the Monopolies of Today from Monopolizing Tomorrow: Google, AI, and the Next Antitrust Frontier”
• Jiannan Xu
  PhD Candidate, Robert H. Smith School of Business, University of Maryland
  “AI Self-Preferencing in Algorithmic Hiring: Empirical Evidence and Insights”
• Jiawei Zhang
  Lloyd M. Robbins Doctor of Juridical Science (J.S.D.) Fellow at UC Berkeley Law School, UC Berkeley Law School
  “From Deepfake 1.0 to 2.0: Deepfaith in Information Market Dynamics & The First Amendment”