Sotabase

Career

· K&L Gates Career Development Assistant Professor in Ethics and Computational Technologies, Carnegie Mellon University2023–
· Assistant Professor, Machine Learning Department, Carnegie Mellon University2020–
· Post-doctoral Associate, Cornell University2019–2020
· Post-doctoral Fellow, ETH Zürich2017–2019
· Ph.D. in Computer and Information Science, University of Pennsylvania2017–
· M.Sc. in Statistics, Wharton School of Business2017–
· Intern, Google Research2015–
· Intern, Microsoft Research2014–
· B.Sc. in Software Engineering, Sharif University of Technology2011–

Publications (55)

Fairness in Criminal Justice Risk Assessments: The State of the Art
Sociological Methods & Research · 2017
1,084
cited
A Convex Framework for Fair Regression
arXiv.org · 2017
365
cited
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices
Knowledge Discovery and Data Mining · 2018
287
cited
Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
Knowledge Discovery and Data Mining · 2019
220
cited
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
Neural Information Processing Systems · 2018
141
cited
A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity
arXiv.org · 2018
125
cited
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
International Conference on Machine Learning · 2019
73
cited
A Moral Framework for Understanding Fair ML through Economic Models of Equality of Opportunity
FAT · 2019
61
cited
Tight Policy Regret Bounds for Improving and Decaying Bandits
International Joint Conference on Artificial Intelligence · 2016
55
cited
International AI Safety Report
arXiv.org · 2025
52
cited
Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects
Conference on Fairness, Accountability and Transparency · 2022
49
cited
A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms
2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · 2022
47
cited
Preventing Disparate Treatment in Sequential Decision Making
International Joint Conference on Artificial Intelligence · 2018
47
cited
The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder, Early-stage Deliberations Around Public Sector AI Proposals
International Conference on Human Factors in Computing Systems · 2024
39
cited
International Scientific Report on the Safety of Advanced AI (Interim Report)
arXiv.org · 2024
38
cited
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
International Conference on Machine Learning · 2021
37
cited
Stateful Strategic Regression
Neural Information Processing Systems · 2021
32
cited
Moral Machine or Tyranny of the Majority?
AAAI Conference on Artificial Intelligence · 2023
31
cited
From Preference Elicitation to Participatory ML: A Critical Survey & Guidelines for Future Research
AAAI/ACM Conference on AI, Ethics, and Society · 2023
30
cited
Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures Through AI Systems
Minds and Machines · 2023
29
cited
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