# Improving Fairness in Adaptive Social Exergames via Shapley Bandits

arXiv: https://arxiv.org/abs/2302.09298 · HTML: https://mlsanity.com/paper/2302.09298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 118 references that are in the map.
- First posted on arXiv 18 Feb 2023.

## Builds on (most cited first, 23 of 23)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [A Convex Framework for Fair Regression](https://mlsanity.com/paper/1706.02409/) (2017)
- [Neuron Shapley: Discovering the Responsible Neurons](https://mlsanity.com/paper/2002.09815/) (2020)
- [Fairness in Reinforcement Learning](https://mlsanity.com/paper/1611.03071/) (2016)
- [Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved](https://mlsanity.com/paper/1811.11154/) (2018)
- [Combinatorial Sleeping Bandits with Fairness Constraints](https://mlsanity.com/paper/1901.04891/) (2019)
- [Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making](https://mlsanity.com/paper/1802.01029/) (2018)
- [Calibrated Fairness in Bandits](https://mlsanity.com/paper/1707.01875/) (2017)
- [A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patients](https://mlsanity.com/paper/1605.04070/) (2016)
- [Predictive and Causal Implications of using Shapley Value for Model Interpretation](https://mlsanity.com/paper/2008.05052/) (2020)
- [Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda](https://mlsanity.com/paper/2002.09054/) (2020)
- [Fair Influence Maximization: A Welfare Optimization Approach](https://mlsanity.com/paper/2006.07906/) (2020)
- [Towards Involving End-users in Interactive Human-in-the-loop AI Fairness](https://mlsanity.com/paper/2204.10464/) (2022)
- [Player-AI Interaction: What Neural Network Games Reveal About AI as Play](https://mlsanity.com/paper/2101.06220/) (2021)
- [Experience Management in Multi-player Games](https://mlsanity.com/paper/1907.02349/) (2019)
- [Player Modeling via Multi-Armed Bandits](https://mlsanity.com/paper/2102.05264/) (2021)
- [Player-Centered AI for Automatic Game Personalization: Open Problems](https://mlsanity.com/paper/2102.07548/) (2021)
- [Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity](https://mlsanity.com/paper/2101.10020/) (2021)

## Built on by (most cited first, 1 of 1)

- [Meritocratic Fairness via K-Shapley Values in Budgeted Combinatorial Bandits with Full-Bandit Feedback](https://mlsanity.com/paper/2605.00762/) (2026)

## Abstract (arXiv)

Algorithmic fairness is an essential requirement as AI becomes integrated in society. In the case of social applications where AI distributes resources, algorithms often must make decisions that will benefit a subset of users, sometimes repeatedly or exclusively, while attempting to maximize specific outcomes. How should we design such systems to serve users more fairly? This paper explores this question in the case where a group of users works toward a shared goal in a social exergame called Step Heroes. We identify adverse outcomes in traditional multi-armed bandits (MABs) and formalize the Greedy Bandit Problem. We then propose a solution based on a new type of fairness-aware multi-armed bandit, Shapley Bandits. It uses the Shapley Value for increasing overall player participation and intervention adherence rather than the maximization of total group output, which is traditionally achieved by favoring only high-performing participants. We evaluate our approach via a user study (n=46). Our results indicate that our Shapley Bandits effectively mediates the Greedy Bandit Problem and achieves better user retention and motivation across the participants.