194 citations · 232 across the 6 of their papers we have counts for
11 papers
Addressing the Long-term Impact of ML Decisions via Policy Regret
David Lindner, Hoda Heidari, Andreas Krause
Machine Learning (ML) increasingly informs the allocation of opportunities to individuals and communities in areas such as lending, education, employment, and beyond. Such decision…
Stateful Strategic Regression
Keegan Harris, Hoda Heidari, Zhiwei Steven Wu
Automated decision-making tools increasingly assess individuals to determine if they qualify for high-stakes opportunities. A recent line of research investigates how strategic age…
On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes
Hoda Heidari, Solon Barocas, Jon Kleinberg +1
Many policies allocate harms or benefits that are uncertain in nature: they produce distributions over the population in which individuals have different probabilities of incurring…
Allocating Opportunities in a Dynamic Model of Intergenerational Mobility
Hoda Heidari, Jon Kleinberg
Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. We develop…
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Hoda Heidari, Vedant Nanda, Krishna P. Gummadi
Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact o…
Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
Megha Srivastava, Hoda Heidari, Andreas Krause
Fairness for Machine Learning has received considerable attention, recently. Various mathematical formulations of fairness have been proposed, and it has been shown that it is impo…