Emergent Bias and Fairness in Multi-Agent Decision Systems
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909968783900672 |
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| author | Madigan, Maeve Kamalaruban, Parameswaran Moynihan, Glenn Kempton, Tom Sutton, David Burrell, Stuart |
| author_facet | Madigan, Maeve Kamalaruban, Parameswaran Moynihan, Glenn Kempton, Tom Sutton, David Burrell, Stuart |
| contents | Multi-agent systems have demonstrated the ability to improve performance on a variety of predictive tasks by leveraging collaborative decision making. However, the lack of effective evaluation methodologies has made it difficult to estimate the risk of bias, making deployment of such systems unsafe in high stakes domains such as consumer finance, where biased decisions can translate directly into regulatory breaches and financial loss. To address this challenge, we need to develop fairness evaluation methodologies for multi-agent predictive systems and measure the fairness characteristics of these systems in the financial tabular domain. Examining fairness metrics using large-scale simulations across diverse multi-agent configurations, with varying communication and collaboration mechanisms, we reveal patterns of emergent bias in financial decision-making that cannot be traced to individual agent components, indicating that multi-agent systems may exhibit genuinely collective behaviors. Our findings highlight that fairness risks in financial multi-agent systems represent a significant component of model risk, with tangible impacts on tasks such as credit scoring and income estimation. We advocate that multi-agent decision systems must be evaluated as holistic entities rather than through reductionist analyses of their constituent components. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16433 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emergent Bias and Fairness in Multi-Agent Decision Systems Madigan, Maeve Kamalaruban, Parameswaran Moynihan, Glenn Kempton, Tom Sutton, David Burrell, Stuart Machine Learning Artificial Intelligence Multi-agent systems have demonstrated the ability to improve performance on a variety of predictive tasks by leveraging collaborative decision making. However, the lack of effective evaluation methodologies has made it difficult to estimate the risk of bias, making deployment of such systems unsafe in high stakes domains such as consumer finance, where biased decisions can translate directly into regulatory breaches and financial loss. To address this challenge, we need to develop fairness evaluation methodologies for multi-agent predictive systems and measure the fairness characteristics of these systems in the financial tabular domain. Examining fairness metrics using large-scale simulations across diverse multi-agent configurations, with varying communication and collaboration mechanisms, we reveal patterns of emergent bias in financial decision-making that cannot be traced to individual agent components, indicating that multi-agent systems may exhibit genuinely collective behaviors. Our findings highlight that fairness risks in financial multi-agent systems represent a significant component of model risk, with tangible impacts on tasks such as credit scoring and income estimation. We advocate that multi-agent decision systems must be evaluated as holistic entities rather than through reductionist analyses of their constituent components. |
| title | Emergent Bias and Fairness in Multi-Agent Decision Systems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.16433 |