More Like this

1. Individually-Fair Auctions for Multi-Slot Sponsored Search

   Chawla, Shuchi; Rezvan, Rojin; Sauerberg, Nathaniel (June 2022, Foundations of Responsible Computing)

   We design fair-sponsored search auctions that achieve a near-optimal tradeoff between fairness and quality. Our work builds upon the model and auction design of Chawla and Jagadeesan, who considered the special case of a single slot. We consider sponsored search settings with multiple slots and the standard model of click-through rates that are multiplicatively separable into an advertiser-specific component and a slot-specific component. When similar users have similar advertiser-specific click-through rates, our auctions achieve the same near-optimal tradeoff between fairness and quality. When similar users can have different advertiser-specific preferences, we show that a preference-based fairness guarantee holds. Finally, we provide a computationally efficient algorithm for computing payments for our auctions as well as those in previous work, resolving another open direction from Chawla and Jagadeesan. 

   more » « less
2. Individually-Fair Auctions for Multi-Slot Sponsored Search

   Chawla, Shuchi; Rezvan, Rojin; Sauerberg, Nathaniel (June 2022, Foundations of Responsible Computing)

   We design fair-sponsored search auctions that achieve a near-optimal tradeoff between fairness and quality. Our work builds upon the model and auction design of Chawla and Jagadeesan, who considered the special case of a single slot. We consider sponsored search settings with multiple slots and the standard model of click-through rates that are multiplicatively separable into an advertiser-specific component and a slot-specific component. When similar users have similar advertiser-specific click-through rates, our auctions achieve the same near-optimal tradeoff between fairness and quality. When similar users can have different advertiser-specific preferences, we show that a preference-based fairness guarantee holds. Finally, we provide a computationally efficient algorithm for computing payments for our auctions as well as those in previous work, resolving another open direction from Chawla and Jagadeesan. 

   more » « less
3. External Evaluation of Discrimination Mitigation Efforts in Meta's Ad Delivery

   https://doi.org/10.1145/3715275.3732170  

   Imana, Basileal; Shen, Zeyu; Heidemann, John; Korolova, Aleksandra (June 2025, ACM)

   The 2022 settlement between Meta and the U.S. Department of Justice to resolve allegations of discriminatory advertising resulted is a first-of-its-kind change to Meta's ad delivery system aimed to address algorithmic discrimination in its housing ad delivery. In this work, we explore direct and indirect effects of both the settlement's choice of terms and the Variance Reduction System (VRS) implemented by Meta on the actual reduction in discrimination. \newline We first show that the settlement terms allow for an implementation that does not meaningfully improve access to opportunities for individuals. The settlement measures impact of ad delivery in terms of impressions, instead of unique individuals reached by an ad; it allows the platform to level down access, reducing disparities by decreasing the overall access to opportunities; and it allows the platform to selectively apply VRS to only small advertisers. \newline We then conduct experiments to evaluate VRS with real-world ads, and show that while VRS does reduce variance, it also raises advertiser costs (measured per-individuals-reached), therefore decreasing user exposure to opportunity ads for a given ad budget. VRS thus \emph{passes the cost of decreasing variance to advertisers}. \newline Finally, we explore an alternative approach to achieve the settlement goals, that is significantly more intuitive and transparent than VRS. We show our approach outperforms VRS by both increasing ad exposure for users from \emph{all} groups and reducing cost to advertisers, thus demonstrating that the increase in cost to advertisers when implementing the settlement is not inevitable. \newline Our methodologies use a black-box approach that relies on capabilities available to any regular advertiser, rather than on privileged access to data, allowing others to reproduce or extend our work. 

   more » « less
4. Gender and Racial Discrimination in Hiring Before and During the COVID-19 Pandemic: Evidence from a Field Experiment of Accountants, 2018–2020

   https://doi.org/10.1177/07308884221094539  

   Chavez, Koji; Weisshaar, Katherine; Cabello-Hutt, Tania (August 2022, Work and Occupations)

   In this article, we ask whether macro-level changes during the first year of the COVID-19 pandemic relate to changes in the levels of discrimination against women and Black job-seekers at the point of hire. We develop three main hypotheses: that discrimination against women and Black job-seekers increases due to a reduction in labor demand; that discrimination against women decreases due to the reduced supply of women employees and applicants; and that discrimination against Black job-seekers decreases due to increased attention toward racial inequities associated with the Black Lives Matter protests during the summer of 2020. We test these hypotheses using a correspondence audit study collected over two periods, before and during the early COVID-19 pandemic, for one professional occupation: accountants. We find that White women experience a positive change in callbacks during the pandemic, being preferred over White men, and this change is concentrated in geographic areas that experienced relatively larger decreases in women's labor supply. Black women experience discrimination pre-pandemic but receive similar callbacks to White men during the pandemic. In contrast to both White and Black women, discrimination against Black men is persistent before and during the pandemic. Our findings are consistent with the prediction of gender-specific changes in labor supply being associated with gender-specific changes in hiring discrimination during the COVID-19 pandemic. More broadly, our study shows how hiring decision-making is related to macro-level labor market processes. 

   more » « less
5. Does Shared Gender Group Membership Mitigate the Effect of Implicit Bias Attributions on Accountability for Gender-Based Discrimination?

   https://doi.org/10.1177/0146167220965306  

   Daumeyer, Natalie_M; Onyeador, Ivuoma_N; Richeson, Jennifer_A (November 2020, Personality and Social Psychology Bulletin)

   Attributing gender discrimination to implicit bias has become increasingly common. However, research suggests that when discrimination is attributed to implicit rather than explicit bias, the perpetrators are held less accountable and deemed less worthy of punishment. The present work examines (a) whether this effect replicates in the domain of gender discrimination, and (b) whether sharing a group membership with the victim moderates the effect. Four studies revealed that both men and women hold perpetrators of gender discrimination less accountable if their behavior is attributed to implicit rather than explicit bias. Moreover, women held male (Studies 1–3), but not female (Study 4), perpetrators of gender discrimination more accountable than did men. Together, these findings suggest that while shared gender group membership may inform judgments of accountability for gender discrimination, it does not weaken the tendency to hold perpetrators less accountable for discrimination attributed to implicit, compared with explicit, bias. 

   more » « less