Related papers: Ban The Box? Information, Incentives, and Statisti…
Interactions between bids to show ads online can lead to an advertiser's ad being shown to more men than women even when the advertiser does not target towards men. We design bidding strategies that advertisers can use to avoid such…
Algorithmic tools are increasingly used in hiring to improve fairness and diversity, often by enforcing constraints such as gender-balanced candidate shortlists. However, we show theoretically and empirically that enforcing equal…
Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias, resulting in biased datasets that unfairly harm specific groups and cause classifiers to inherit these biases. In this paper, we…
We study how a monopolist's use of consumer data for price discrimination affects welfare. To answer this question, we develop a model of market segmentation subject to residual uncertainty. We fully characterize when data usage…
People use web search engines to find information before forming opinions, which can lead to practical decisions with different levels of impact. The cognitive effort of search can leave opinionated users vulnerable to cognitive biases,…
This paper uses and recycles data from a third-party digital marketing firm, to explore how targeted ads contribute to larger systems of racial discrimination. Focusing on a case study of targeted ads for educational searches in New York…
Researchers and journalists have repeatedly shown that algorithms commonly used in domains such as credit, employment, healthcare, or criminal justice can have discriminatory effects. Some organizations have tried to mitigate these effects…
In recent studies of political decision-making, apparently anomalous behavior has been observed on the part of voters, in which negative information about a candidate strengthens, rather than weakens, a prior positive opinion about the…
A robust body of evidence demonstrates the adverse effects of implicit bias in various contexts--from hiring to health care. The Rooney Rule is an intervention developed to counter implicit bias and has been implemented in the private and…
In decision-making, individuals often rely on intuition, which can occasionally yield suboptimal outcomes. This study examines the impact of intuitive decision-making on individuals who are confronted with limited position information in…
The economics of smaller budgets and larger case numbers necessitates the use of AI in legal proceedings. We examine the concept of disparate impact and how biases in the training data lead to the search for fairer AI. This paper seeks to…
Many U.S. colleges now use test-optional admissions. A frequent claim is that by not seeing standardized test scores, a college can admit a student body it prefers, say with more diversity. But how can observing less information improve…
It is well known that women are underrepresented in the academic systems of many countries. Gender discrimination is one of the factors that could contribute to this phenomenon. This study considers a recent national academic recruitment…
In this work, we proposed a new $N$-person game in which the players can bet on two options, for example represented by two boxers. Some of the players have privileged information about the boxers and part of them can provide this…
This paper studies a preference evolution model in which a population of agents are matched to play a sequential prisoner's dilemma in an incomplete information environment. An institution can design an incentive-compatible screening…
An information broker incentivizes consumers to share their information, while designing an information structure to shape the market segmentation. The information broker is a metaphor for an Internet platform that matches consumers with…
Classification with abstention has gained a lot of attention in recent years as it allows to incorporate human decision-makers in the process. Yet, abstention can potentially amplify disparities and lead to discriminatory predictions. The…
Motivated by online platforms such as job markets, we study an agent choosing from a list of candidates, each with a hidden quality that determines match value. The agent observes only a noisy ranking of the candidates plus a binary signal…
Many internet ventures rely on advertising for their revenue. However, users feel discontent by the presence of ads on the websites they visit, as the data-size of ads is often comparable to that of the actual content. This has an impact…
We develop an Empirical Bayes grading scheme that balances the informativeness of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the racial biases…