This work tackles the issue of fairness in the context of generative procedures, such as
image super-resolution, which entail different definitions from the standard classification …

This article examines the growth of algorithmic credit scoring and its implications for the
regulation of consumer credit markets in the UK. It constructs a frame of analysis for the …

The rapid advances in Generative AI tools have produced both excitement and worry about
how AI will impact academic writing. However, little is known about what norms are …

Strategic classification studies the design of a classifier robust to the manipulation of input by
strategic individuals. However, the existing literature does not consider the effect of …

This paper studies algorithmic decision-making in the presence of strategic individual
behaviors, where an ML model is used to make decisions about human agents and the latter …

Machine Learning models have been deployed across many different aspects of society,
often in situations that affect social welfare. Although these models offer streamlined …

We address an inherent difficulty in welfare-theoretic fair machine learning (ML), by
proposing an equivalently-axiomatically justified alternative setting, and studying the …

In an attempt to make algorithms fair, the machine learning literature has largely focused on
equalizing decisions, outcomes, or error rates across race or gender groups. To illustrate …

Welfare measures overall utility across a population, whereas malfare measures overall
disutility, and the social planner's problem can be cast either as maximizing the former or …

Personalized pricing is a business strategy to charge different prices to individual
consumers based on their characteristics and behaviors. It has become common practice in …