Achieving Fairness in the Stochastic Multi-armed Bandit Problem

We study an interesting variant of the stochastic multi-armed bandit problem, called the Fair-SMAB problem, where each arm is required to be pulled for at least a given fraction of the total available rounds. We investigate the interplay between learning and fairness in terms of a pre-specified vector denoting the fractions of guaranteed pulls. We define a fairness-aware regret, called $r$-Regret, that takes into account the above fairness constraints and naturally extends the conventional notion of regret. Our primary contribution is characterizing a class of Fair-SMAB algorithms by two parameters: the unfairness tolerance and the learning algorithm used as a black-box. We provide a fairness guarantee for this class that holds uniformly over time irrespective of the choice of the learning algorithm. In particular, when the learning algorithm is UCB1, we show that our algorithm achieves $O(\ln T)$ $r$-Regret. Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.Comment: arXiv admin note: substantial text overlap with arXiv:1905.1126

Towards Algorithmic Fairness in Space-Time: Filling in Black Holes

New technologies and the availability of geospatial data have drawn attention to spatio-temporal biases present in society. For example: the COVID-19 pandemic highlighted disparities in the availability of broadband service and its role in the digital divide; the environmental justice movement in the United States has raised awareness to health implications for minority populations stemming from historical redlining practices; and studies have found varying quality and coverage in the collection and sharing of open-source geospatial data. Despite the extensive literature on machine learning (ML) fairness, few algorithmic strategies have been proposed to mitigate such biases. In this paper we highlight the unique challenges for quantifying and addressing spatio-temporal biases, through the lens of use cases presented in the scientific literature and media. We envision a roadmap of ML strategies that need to be developed or adapted to quantify and overcome these challenges -- including transfer learning, active learning, and reinforcement learning techniques. Further, we discuss the potential role of ML in providing guidance to policy makers on issues related to spatial fairness

Learning with Exposure Constraints in Recommendation Systems

Recommendation systems are dynamic economic systems that balance the needs of multiple stakeholders. A recent line of work studies incentives from the content providers' point of view. Content providers, e.g., vloggers and bloggers, contribute fresh content and rely on user engagement to create revenue and finance their operations. In this work, we propose a contextual multi-armed bandit setting to model the dependency of content providers on exposure. In our model, the system receives a user context in every round and has to select one of the arms. Every arm is a content provider who must receive a minimum number of pulls every fixed time period (e.g., a month) to remain viable in later rounds; otherwise, the arm departs and is no longer available. The system aims to maximize the users' (content consumers) welfare. To that end, it should learn which arms are vital and ensure they remain viable by subsidizing arm pulls if needed. We develop algorithms with sub-linear regret, as well as a lower bound that demonstrates that our algorithms are optimal up to logarithmic factors.Comment: Published in The Web Conference 2023 (WWW 23

Achieving Fairness in the Stochastic Multi-Armed Bandit Problem

We study an interesting variant of the stochastic multi-armed bandit problem, which we call the Fair-MAB problem, where, in addition to the objective of maximizing the sum of expected rewards, the algorithm also needs to ensure that at any time, each arm is pulled at least a pre-specified fraction of times. We investigate the interplay between learning and fairness in terms of a pre-specified vector denoting the fractions of guaranteed pulls. We define a fairness-aware regret, which we call r-Regret, that takes into account the above fairness constraints and extends the conventional notion of regret in a natural way. Our primary contribution is to obtain a complete characterization of a class of Fair-MAB algorithms via two parameters: the unfairness tolerance and the learning algorithm used as a black-box. For this class of algorithms, we provide a fairness guarantee that holds uniformly over time, irrespective of the choice of the learning algorithm. Further, when the learning algorithm is UCB1, we show that our algorithm achieves constant r-Regret for a large enough time horizon. Finally, we analyze the cost of fairness in terms of the conventional notion of regret. We conclude by experimentally validating our theoretical results

Socially Responsible Machine Learning: On the Preservation of Individual Privacy and Fairness

Machine learning (ML) techniques have seen significant advances over the last decade and are playing an increasingly critical role in people's lives. While their potential societal benefits are enormous, they can also inflict great harm if not developed or used with care. In this thesis, we focus on two critical ethical issues in ML systems, the violation of privacy and fairness, and explore mitigating approaches in various scenarios. On the privacy front, when ML systems are developed with private data from individuals, it is critical to prevent privacy violation. Differential privacy (DP), a widely used notion of privacy, ensures that no one by observing the computational outcome can infer a particular individual’s data with high confidence. However, DP is typically achieved by randomizing algorithms (e.g., adding noise), which inevitably leads to a trade-off between individual privacy and outcome accuracy. This trade-off can be difficult to balance, especially in settings where the same or correlated data is repeatedly used/exposed during the computation. In the first part of the thesis, we illustrate two key ideas that can be used to balance an algorithm's privacy-accuracy tradeoff: (1) the reuse of intermediate computational results to reduce information leakage; and (2) improving algorithmic robustness to accommodate more randomness. We introduce a number of randomized, privacy-preserving algorithms that leverage these ideas in various contexts such as distributed optimization and sequential computation. It is shown that our algorithms can significantly improve the privacy-accuracy tradeoff over existing solutions. On the fairness front, ML systems trained with real-world data can inherit biases and exhibit discrimination against already-disadvantaged or marginalized social groups. Recent works have proposed many fairness notions to measure and remedy such biases. However, their effectiveness is mostly studied in a static framework without accounting for the interactions between individuals and ML systems. Since individuals inevitably react to the algorithmic decisions they are subjected to, understanding the downstream impacts of ML decisions is critical to ensure that these decisions are socially responsible. In the second part of the thesis, we present our research on evaluating the long-term impacts of (fair) ML decisions. Specifically, we establish a number of theoretically rigorous frameworks to model the interactions and feedback between ML systems and individuals, and conduct equilibrium analysis to evaluate the impact they each have on the other. We will illustrate how ML decisions and individual behavior evolve in such a system, and how imposing common fairness criteria intended to promote fairness may nevertheless lead to undesirable pernicious effects. Aided with such understanding, mitigation approaches are also discussed.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169960/1/xueru_1.pd