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1. Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences

   https://doi.org/10.1145/3313831.3376813  

   Wang, Ruotong; Harper, F. Maxwell; Zhu, Haiyi (April 2020, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   Algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect people's perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants' education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users' feedback must consider the possibility of "outcome favorability" bias. 

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2. Fairness and Abstraction in Sociotechnical Systems

   https://doi.org/10.1145/3287560.3287598  

   Selbst, Andrew D.; boyd, danah; Friedler, Sorelle A.; Venkatasubramanian, Suresh; Vertesi, Janet (January 2019, Proceedings of the Conference on Fairness, Accountability, and Transparency)

   A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science---such as abstraction and modular design---are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones. 

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3. Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints

   https://doi.org/10.1609/aaai.v38i14.29489  

   Wang, Shufan; Xiong, Guojun; Li, Jian (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Restless multi-armed bandits (RMAB) have been widely used to model sequential decision making problems with constraints. The decision maker (DM) aims to maximize the expected total reward over an infinite horizon under an “instantaneous activation constraint” that at most B arms can be activated at any decision epoch, where the state of each arm evolves stochastically according to a Markov decision process (MDP). However, this basic model fails to provide any fairness guarantee among arms. In this paper, we introduce RMAB-F, a new RMAB model with “long-term fairness constraints”, where the objective now is to maximize the longterm reward while a minimum long-term activation fraction for each arm must be satisfied. For the online RMAB-F setting (i.e., the underlying MDPs associated with each arm are unknown to the DM), we develop a novel reinforcement learning (RL) algorithm named Fair-UCRL. We prove that Fair-UCRL ensures probabilistic sublinear bounds on both the reward regret and the fairness violation regret. Compared with off-the-shelf RL methods, our Fair-UCRL is much more computationally efficient since it contains a novel exploitation that leverages a low-complexity index policy for making decisions. Experimental results further demonstrate the effectiveness of our Fair-UCRL. 

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4. Fairness and Friends

   Arif Khan, Falaah; Manis, Eleni; Stoyanovich, Julia (January 2021, Beyond static papers: Rethinking how we share scientific understanding in ML - ICLR 2021 workshop)

   Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithm to be “fair.” Most of these propositions are inspired by, but inadequately grounded in, scholarship from political philosophy. This comic aims to correct that deficit. We begin by setting up a working definition of an 'Automated Decision System' (ADS) and explaining 'bias' in outputs of an ADS. We then critically evaluate different definitions of fairness as Equality of Opportunity (EOP) by contrasting their conception in political philosophy (such as Rawls’s fair EOP and formal EOP) with the proposed codification in Fair-ML (such as statistical parity, equality of odds and accuracy) to provide a clearer lens with which to view existing results and to identify future research directions. We use this framing to reinterpret the impossibility results as the incompatibility between different EOP doctrines and demonstrate how political philosophy can provide normative guidance as to which notion of fairness is applicable in which context. We conclude by highlighting justice considerations that the fair-ML literature currently overlooks or underemphasizes, such as Rawls's broader theory of justice, which supplements his EOP principle with a principle guaranteeing equal rights and liberties to all citizens in a free and democratic society. 

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5. Algorithmic Fairness in Distribution of Resources and Tasks

   https://doi.org/10.24963/ijcai.2024/979  

   Hosseini, Hadi (August 2024, International Joint Conferences on Artificial Intelligence Organization)

   The widespread adoption of Artificial Intelligence (AI) systems has profoundly reshaped decision-making in social, political, and commercial contexts. This paper explores the critical issue of fairness in AI-driven decision-making, particularly in allocating resources and tasks. By examining recent advancements and key questions in computational social choice, I highlight challenges and prospects in designing fair systems in collective decision-making that are scalable, adaptable to intricate environments, and are aligned with complex and diverse human preferences. 

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