MARIA: A Framework for Marginal Risk Assessment without Ground Truth in AI Systems
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912679224934400 |
|---|---|
| author | Chen, Jieshan Ma, Suyu Lu, Qinghua Lee, Sung Une Zhu, Liming |
| author_facet | Chen, Jieshan Ma, Suyu Lu, Qinghua Lee, Sung Une Zhu, Liming |
| contents | Before deploying an AI system to replace an existing process, it must be compared with the incumbent to ensure improvement without added risk. Traditional evaluation relies on ground truth for both systems, but this is often unavailable due to delayed or unknowable outcomes, high costs, or incomplete data, especially for long-standing systems deemed safe by convention. The more practical solution is not to compute absolute risk but the difference between systems. We therefore propose a marginal risk assessment framework, that avoids dependence on ground truth or absolute risk. It emphasizes three kinds of relative evaluation methodology, including predictability, capability and interaction dominance. By shifting focus from absolute to relative evaluation, our approach equips software teams with actionable guidance: identifying where AI enhances outcomes, where it introduces new risks, and how to adopt such systems responsibly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_27163 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MARIA: A Framework for Marginal Risk Assessment without Ground Truth in AI Systems Chen, Jieshan Ma, Suyu Lu, Qinghua Lee, Sung Une Zhu, Liming Software Engineering Artificial Intelligence Human-Computer Interaction D.2.8; D.2.9.m; I.2 Before deploying an AI system to replace an existing process, it must be compared with the incumbent to ensure improvement without added risk. Traditional evaluation relies on ground truth for both systems, but this is often unavailable due to delayed or unknowable outcomes, high costs, or incomplete data, especially for long-standing systems deemed safe by convention. The more practical solution is not to compute absolute risk but the difference between systems. We therefore propose a marginal risk assessment framework, that avoids dependence on ground truth or absolute risk. It emphasizes three kinds of relative evaluation methodology, including predictability, capability and interaction dominance. By shifting focus from absolute to relative evaluation, our approach equips software teams with actionable guidance: identifying where AI enhances outcomes, where it introduces new risks, and how to adopt such systems responsibly. |
| title | MARIA: A Framework for Marginal Risk Assessment without Ground Truth in AI Systems |
| topic | Software Engineering Artificial Intelligence Human-Computer Interaction D.2.8; D.2.9.m; I.2 |
| url | https://arxiv.org/abs/2510.27163 |