Usage Governance Advisor: From Intent to AI Governance
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929684939276288 |
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| author | Daly, Elizabeth M. Rooney, Sean Tirupathi, Seshu Garces-Erice, Luis Vejsbjerg, Inge Bagehorn, Frank Salwala, Dhaval Giblin, Christopher Wolf-Bauwens, Mira L. Giurgiu, Ioana Hind, Michael Urbanetz, Peter |
| author_facet | Daly, Elizabeth M. Rooney, Sean Tirupathi, Seshu Garces-Erice, Luis Vejsbjerg, Inge Bagehorn, Frank Salwala, Dhaval Giblin, Christopher Wolf-Bauwens, Mira L. Giurgiu, Ioana Hind, Michael Urbanetz, Peter |
| contents | Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the reputational damage incurred by the use of models that are unsafe. Safety covers both what a model does; e.g., can it be used to reveal personal information from its training set, and how a model was built; e.g., was it only trained on licensed data sets. Determining the safety of an AI system requires gathering information from a wide set of heterogeneous sources including safety benchmarks and technical documentation for the set of models used in that system. In addition, responsible use is encouraged through mechanisms that advise and help the user to take mitigating actions where safety risks are detected. We present Usage Governance Advisor which creates semi-structured governance information, identifies and prioritizes risks according to the intended use case, recommends appropriate benchmarks and risk assessments and importantly proposes mitigation strategies and actions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Usage Governance Advisor: From Intent to AI Governance Daly, Elizabeth M. Rooney, Sean Tirupathi, Seshu Garces-Erice, Luis Vejsbjerg, Inge Bagehorn, Frank Salwala, Dhaval Giblin, Christopher Wolf-Bauwens, Mira L. Giurgiu, Ioana Hind, Michael Urbanetz, Peter Artificial Intelligence Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the reputational damage incurred by the use of models that are unsafe. Safety covers both what a model does; e.g., can it be used to reveal personal information from its training set, and how a model was built; e.g., was it only trained on licensed data sets. Determining the safety of an AI system requires gathering information from a wide set of heterogeneous sources including safety benchmarks and technical documentation for the set of models used in that system. In addition, responsible use is encouraged through mechanisms that advise and help the user to take mitigating actions where safety risks are detected. We present Usage Governance Advisor which creates semi-structured governance information, identifies and prioritizes risks according to the intended use case, recommends appropriate benchmarks and risk assessments and importantly proposes mitigation strategies and actions. |
| title | Usage Governance Advisor: From Intent to AI Governance |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.01957 |