The Science of the Predicted Human Talk Series: Professor Jon Kleinberg

Title

The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

Abstract

Online platforms have a wealth of data, run countless experiments and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is that incentives are misaligned: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken foundational assumption. To understand what users want, platforms look at what users do. This is a kind of revealed-preference assumption that is ubiquitous in user models. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want: we can choose mindlessly or myopically, behaviors that feel entirely familiar on online platforms.

In this work, we develop a model of media consumption where users have inconsistent preferences. We consider what happens when a platform that simply wants to maximize user utility is only able to observe behavioral data in the form of user engagement. Our framework is based on a stochastic model of user behavior, in which users are guided by two conflicting sets of preferences — one that operates impulsively in the moment, and the other of which makes plans over longer time-scales. By linking the behavior of this model to abstractions of platform design choices, we can develop a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media.

About Jon Kleinberg

Jon Kleinberg is the Tisch University Professor in the Computer Science Department at Cornell University. Broadly, his research focuses on issues at the interface of networks and information, with an emphasis on the social and information networks that underpin the Web and other online media. Kleinberg has made deep, creative and insightful contributions to many areas, including the HITS algorithm for web search, theoretical results on navigation in networks, and most recently fundamental results in algorithmic fairness. His work has been recognized with MacArthur, Packard, and Sloan Foundation Fellowships, as well as awards including the Nevanlinna Prize, the Lanchester Prize, and the ACM-Infosys Foundation Award in the Computing Sciences. He is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences, and serves on the Computer and Information Science and Engineering (CISE) Advisory Committee of the National Science Foundation, and the Computer Science and Telecommunications Board (CSTB) of the National Research Council.

The Predicted Human

Being human in 2023 implies being the target of a vast number of predictive infrastructures. In healthcare, algorithms predict not only potential pharmacological cures to disease but also their possible future incidence of those diseases. In governance, citizens are exposed to algorithms that predict – not only their day-to-day behaviors to craft better policy – but also to algorithms that attempt to predict, shape and manipulate their political attitudes and behaviors. In education, children’s emotional and intellectual development is increasingly the product of at-home and at-school interventions shaped around personalized algorithms. And humans worldwide are increasingly subject to advertising and marketing algorithms whose goal is to target them with specific products and ideas they will find palatable. Algorithms are everywhere – as are their intended as well as unintended consequences. The series is arranged with generous support by the Villum Foundation and the Pioneer Center for Artificial Intelligence.