Algorithmic Fairness in AI Surrogates for End-of-Life Decision-Making
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917023729057792 |
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| author | Ahmad, Muhammad Aurangzeb |
| author_facet | Ahmad, Muhammad Aurangzeb |
| contents | Artificial intelligence surrogates are systems designed to infer preferences when individuals lose decision-making capacity. Fairness in such systems is a domain that has been insufficiently explored. Traditional algorithmic fairness frameworks are insufficient for contexts where decisions are relational, existential, and culturally diverse. This paper explores an ethical framework for algorithmic fairness in AI surrogates by mapping major fairness notions onto potential real-world end-of-life scenarios. It then examines fairness across moral traditions. The authors argue that fairness in this domain extends beyond parity of outcomes to encompass moral representation, fidelity to the patient's values, relationships, and worldview. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_16056 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Algorithmic Fairness in AI Surrogates for End-of-Life Decision-Making Ahmad, Muhammad Aurangzeb Computers and Society Artificial Intelligence Machine Learning Artificial intelligence surrogates are systems designed to infer preferences when individuals lose decision-making capacity. Fairness in such systems is a domain that has been insufficiently explored. Traditional algorithmic fairness frameworks are insufficient for contexts where decisions are relational, existential, and culturally diverse. This paper explores an ethical framework for algorithmic fairness in AI surrogates by mapping major fairness notions onto potential real-world end-of-life scenarios. It then examines fairness across moral traditions. The authors argue that fairness in this domain extends beyond parity of outcomes to encompass moral representation, fidelity to the patient's values, relationships, and worldview. |
| title | Algorithmic Fairness in AI Surrogates for End-of-Life Decision-Making |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.16056 |