A3 · Proposed ICLR 2027 Workshop

Auditing
AI Agents

Evidence, Evaluation,
and Accountability

What conclusions does the evidence support, and how can others check or challenge them?

Explore the proposed program
01

From Agent Behavior to Checkable Claims

We bring together researchers who detect, diagnose, and test agent failures, and assess reliability and safety. Across benchmarks, monitors, and audits, we ask what the available evidence can establish.

Evidence

What Was Recorded?

Execution traces, tool permissions, environment state, reasoning traces, and the consequences of missing or selected information.

Evaluation

What Does It Show?

Failure diagnosis, adversarial testing, measurement validity, uncertainty, and reliability when tasks, tools, or environments change.

Accountability

Who Can Check It?

Independent assessment, access limits, conflicting conclusions, contestation, and evidence that a failure has been fixed.

02

A Day of Talks, Questions, and Shared Evidence

Proposed Program
Invited Talks

Six Complementary Perspectives

Deployed agent systems; evaluating agent workflows; ML evidence and monitoring; robustness auditing; security and adversarial testing; independent auditing and incident evidence.

6 talks
Contributed Work

Research Talks and Poster Discussions

Methods, evaluations, negative results, re-analyses, and case studies from the community.

Talks + posters
Interactive Session

One Claim, Different Evidence

Assess a coding agent's claimed repair and revise your judgment as the audience chooses which evidence to reveal.

60 minutes
Closing Panel

Which Claims Survive Independent Assessment?

Invited participants compare what can be concluded under restricted access and identify the next research questions.

40 minutes

The workshop day, detailed schedule, and talk durations will be finalized after acceptance.

The Evidence-Comparison Session

Would the Next Piece
Change Your Conclusion?

A planned exercise puts the audience in the evaluator's seat. A coding agent reports that it fixed a CSV parser. What would you need to believe the claim?

  1. 01 / Assess

    Start with the Claim

    Read the task, completion report, and reported tests. Record your verdict and confidence before the expert assessments.

  2. 02 / Reveal

    Ask for More Evidence

    Choose from code and test changes, execution traces, and permissions. Update your judgment as the record expands.

  3. 03 / Replay

    Compare with an Independent Check

    See a replay with unchanged tests and held-out cases. Discuss what remains unresolved, even when the tests pass.

Voluntary audience judgments make changes in verdict and confidence visible. Agreement alone does not establish correctness.

03

Invited Speakers and Panelists

The following guests have confirmed tentative participation, subject to workshop acceptance and final scheduling. Sean McGregor's tentative participation is for an invited talk, subject to travel availability. Other talk and panel roles, topics, and durations are to be agreed.

Listed alphabetically by family name. The proposed program includes six invited talks and one panel.

Emilio Ferrara

Emilio Ferrara

University of Southern California

Professor AAAI Fellow

Ben Goldhaber

Ben Goldhaber

Coefficient Giving

Program Officer AI Governance & International Policy

Heng Ji

Heng Ji

University of Illinois Urbana-Champaign

Professor ACL Fellow

Chen-Yu Lee

Chen-Yu Lee

Google

Principal Scientist and Director Google Cloud AI Research

Sharon Li

Sharon Li

University of Wisconsin-Madison

Associate Professor Sloan Research Fellow (2025)

Yunyao Li

Yunyao Li

Adobe

Director of Machine Learning ACM Distinguished Member

Sean McGregor

Sean McGregor

AVERI

Lead Research Engineer

Linsey Pang

Linsey Pang

PayPal

Distinguished AI Research Scientist

David Rein

David Rein

METR

Member of Technical Staff

Yu Su

Yu Su

The Ohio State University

Associate Professor Sloan Research Fellow (2025)

04

Organizing Committee

Yue Zhao

Yue Zhao

University of Southern California

Manling Li

Manling Li

Northwestern University

Hao Dong

Hao Dong

Tampere University

Chaowei Xiao

Chaowei Xiao

Johns Hopkins University

Kaize Ding

Kaize Ding

Northwestern University

Xiyang Hu

Xiyang Hu

Arizona State University

Yan Liu

Yan Liu

University of Southern California

Leman Akoglu

Leman Akoglu

Carnegie Mellon University

05

Contribute to the Conversation

Methods, empirical studies, and well-supported arguments about agent failures, reliability, safety, and accountability are welcome.

Contributions may address an individual benchmark, detector, monitor, or diagnostic method. Ongoing work, negative results, and re-analyses are in scope.