Generative AI for anti-corruption and integrity in government: Taking stock of promise, perils and practice
Generative artificial intelligence (AI) presents myriad opportunities for integrity actors-anti-corruption agencies, supreme audit institutions, internal audit bodies and others-to enhance the impact of their work, particularly through the use of large language models (LLMS). As this type of AI beco...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Paris
OECD Publishing
2024
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| Schriftenreihe: | OECD Artificial Intelligence Papers
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Generative artificial intelligence (AI) presents myriad opportunities for integrity actors-anti-corruption agencies, supreme audit institutions, internal audit bodies and others-to enhance the impact of their work, particularly through the use of large language models (LLMS). As this type of AI becomes increasingly mainstream, it is critical for integrity actors to understand both where generative AI and LLMs can add the most value and the risks they pose. To advance this understanding, this paper draws on input from the OECD integrity and anti-corruption communities and provides a snapshot of the ways these bodies are using generative AI and LLMs, the challenges they face, and the insights these experiences offer to similar bodies in other countries. The paper also explores key considerations for integrity actors to ensure trustworthy AI systems and responsible use of AI as their capacities in this area develop |
| Beschreibung: | 1 Online-Ressource (50 Seiten) 21 x 28cm |
| DOI: | 10.1787/657a185a-en |
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| spelling | Ugale, Gavin Verfasser aut Generative AI for anti-corruption and integrity in government Taking stock of promise, perils and practice Gavin, Ugale and Cameron, Hall Paris OECD Publishing 2024 1 Online-Ressource (50 Seiten) 21 x 28cm txt rdacontent c rdamedia cr rdacarrier OECD Artificial Intelligence Papers Generative artificial intelligence (AI) presents myriad opportunities for integrity actors-anti-corruption agencies, supreme audit institutions, internal audit bodies and others-to enhance the impact of their work, particularly through the use of large language models (LLMS). As this type of AI becomes increasingly mainstream, it is critical for integrity actors to understand both where generative AI and LLMs can add the most value and the risks they pose. To advance this understanding, this paper draws on input from the OECD integrity and anti-corruption communities and provides a snapshot of the ways these bodies are using generative AI and LLMs, the challenges they face, and the insights these experiences offer to similar bodies in other countries. The paper also explores key considerations for integrity actors to ensure trustworthy AI systems and responsible use of AI as their capacities in this area develop Education Employment Governance Social Issues/Migration/Health Science and Technology Hall, Cameron ctb https://doi.org/10.1787/657a185a-en Verlag kostenfrei Volltext |
| spellingShingle | Ugale, Gavin Generative AI for anti-corruption and integrity in government Taking stock of promise, perils and practice Education Employment Governance Social Issues/Migration/Health Science and Technology |
| title | Generative AI for anti-corruption and integrity in government Taking stock of promise, perils and practice |
| title_auth | Generative AI for anti-corruption and integrity in government Taking stock of promise, perils and practice |
| title_exact_search | Generative AI for anti-corruption and integrity in government Taking stock of promise, perils and practice |
| title_full | Generative AI for anti-corruption and integrity in government Taking stock of promise, perils and practice Gavin, Ugale and Cameron, Hall |
| title_fullStr | Generative AI for anti-corruption and integrity in government Taking stock of promise, perils and practice Gavin, Ugale and Cameron, Hall |
| title_full_unstemmed | Generative AI for anti-corruption and integrity in government Taking stock of promise, perils and practice Gavin, Ugale and Cameron, Hall |
| title_short | Generative AI for anti-corruption and integrity in government |
| title_sort | generative ai for anti corruption and integrity in government taking stock of promise perils and practice |
| title_sub | Taking stock of promise, perils and practice |
| topic | Education Employment Governance Social Issues/Migration/Health Science and Technology |
| topic_facet | Education Employment Governance Social Issues/Migration/Health Science and Technology |
| url | https://doi.org/10.1787/657a185a-en |
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