Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessment
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915115162402816 |
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| author | Lee, Sung Une Perera, Harsha Liu, Yue Xia, Boming Lu, Qinghua Zhu, Liming Salvado, Olivier Whittle, Jon |
| author_facet | Lee, Sung Une Perera, Harsha Liu, Yue Xia, Boming Lu, Qinghua Zhu, Liming Salvado, Olivier Whittle, Jon |
| contents | The rapid growth of Artificial Intelligence (AI) has underscored the urgent need for responsible AI practices. Despite increasing interest, a comprehensive AI risk assessment toolkit remains lacking. This study introduces our Responsible AI (RAI) Question Bank, a comprehensive framework and tool designed to support diverse AI initiatives. By integrating AI ethics principles such as fairness, transparency, and accountability into a structured question format, the RAI Question Bank aids in identifying potential risks, aligning with emerging regulations like the EU AI Act, and enhancing overall AI governance. A key benefit of the RAI Question Bank is its systematic approach to linking lower-level risk questions to higher-level ones and related themes, preventing siloed assessments and ensuring a cohesive evaluation process. Case studies illustrate the practical application of the RAI Question Bank in assessing AI projects, from evaluating risk factors to informing decision-making processes. The study also demonstrates how the RAI Question Bank can be used to ensure compliance with standards, mitigate risks, and promote the development of trustworthy AI systems. This work advances RAI by providing organizations with a valuable tool to navigate the complexities of ethical AI development and deployment while ensuring comprehensive risk management. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_11820 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessment Lee, Sung Une Perera, Harsha Liu, Yue Xia, Boming Lu, Qinghua Zhu, Liming Salvado, Olivier Whittle, Jon Computers and Society Artificial Intelligence The rapid growth of Artificial Intelligence (AI) has underscored the urgent need for responsible AI practices. Despite increasing interest, a comprehensive AI risk assessment toolkit remains lacking. This study introduces our Responsible AI (RAI) Question Bank, a comprehensive framework and tool designed to support diverse AI initiatives. By integrating AI ethics principles such as fairness, transparency, and accountability into a structured question format, the RAI Question Bank aids in identifying potential risks, aligning with emerging regulations like the EU AI Act, and enhancing overall AI governance. A key benefit of the RAI Question Bank is its systematic approach to linking lower-level risk questions to higher-level ones and related themes, preventing siloed assessments and ensuring a cohesive evaluation process. Case studies illustrate the practical application of the RAI Question Bank in assessing AI projects, from evaluating risk factors to informing decision-making processes. The study also demonstrates how the RAI Question Bank can be used to ensure compliance with standards, mitigate risks, and promote the development of trustworthy AI systems. This work advances RAI by providing organizations with a valuable tool to navigate the complexities of ethical AI development and deployment while ensuring comprehensive risk management. |
| title | Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessment |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2408.11820 |