# Understanding Decision Subjects' Engagement with and Perceived Fairness of AI Models When Opportunities of Qualification Improvement Exist

arXiv: https://arxiv.org/abs/2410.03126 · HTML: https://mlsanity.com/paper/2410.03126/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [How Do Fair Decisions Fare in Long-term Qualification?](https://mlsanity.com/paper/2010.11300/) (2020)
- [Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning](https://mlsanity.com/paper/1902.04783/) (2019)
- [On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning](https://mlsanity.com/paper/1903.01209/) (2019)
- [How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness](https://mlsanity.com/paper/1811.03654/) (2018)
- [A Short-term Intervention for Long-term Fairness in the Labor Market](https://mlsanity.com/paper/1712.00064/) (2017)
- [The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally](https://mlsanity.com/paper/1910.04123/) (2019)
- [Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness](https://mlsanity.com/paper/1905.00569/) (2019)
- [From Fair Decision Making to Social Equality](https://mlsanity.com/paper/1812.02952/) (2018)
- [Fairness in Learning-Based Sequential Decision Algorithms: A Survey](https://mlsanity.com/paper/2001.04861/) (2020)
- [Towards Involving End-users in Interactive Human-in-the-loop AI Fairness](https://mlsanity.com/paper/2204.10464/) (2022)
- [A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness](https://mlsanity.com/paper/1911.03020/) (2019)

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

- [How Strategic Agents Respond: Comparing Analytical Models with LLM-Generated Responses in Strategic Classification](https://mlsanity.com/paper/2501.16355/) (2025)

## Abstract (arXiv)

We explore how an AI model's decision fairness affects people's engagement with and perceived fairness of the model if they are subject to its decisions, but could repeatedly and strategically respond to these decisions. Two types of strategic responses are considered -- people could determine whether to continue interacting with the model, and whether to invest in themselves to improve their chance of future favorable decisions from the model. Via three human-subject experiments, we found that in decision subjects' strategic, repeated interactions with an AI model, the model's decision fairness does not change their willingness to interact with the model or to improve themselves, even when the model exhibits unfairness on salient protected attributes. However, decision subjects still perceive the AI model to be less fair when it systematically biases against their group, especially if the difficulty of improving one's qualification for the favorable decision is larger for the lowly-qualified people.