Measuring and Mitigating Bias and Harm in Personalized Advertising

Abstract

Online personalized advertising is often very effective in identifying relevant audiences for each piece of content, which has led to its widespread adoption. In today’s internet, however, these advertising systems are used not only to market products, but also consequential life opportunities such as employment or housing, as well as socially important political messaging. This has led to increasing concerns about the presence of algorithmic bias and possible discrimination in these important domains — with results showing problematic biases along gender, race, and political affiliation, even when the advertiser might have targeted broadly. A growing body of work focuses on measuring and characterizing these biases, as well as finding ways to mitigate these effects and building responsible systems. However, these results often emerge from different scientific communities and are often disconnected in the literature. In this paper, I attempt at bridging the gap between isolated efforts to either measure these biases, or to mitigate them. I discuss how the need to measure bias in advertising, and the efforts to mitigate it, despite being distant in the literature, are complementary problems that need to center their methodolgy around user studies. This paper presents a research agenda that focuses on the need for user-centric measurements of bias, by collecting real ads from users, and using surveys to understand user perceptions for these ads. My approach also calls for incorporating user sentiments into the mitigation efforts, by constraining optimization on user values that emerge from surveys. Finally, I also emphasize the need for involving users in the evaluation of responsible advertising systems; efforts to mitigate bias eventually need to be contextualized in terms of benefits to users instead of simple performance tradeoffs. My focus on the users is motivated by the fact that they are stakeholders in personalized advertising, vulnerable at the hand of algorithmic bias and harm, and therefore crucial in both efforts to measure and mitigate these effects.