Aggregated Individual Reporting for Post-Deployment Evaluation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915354905673728 |
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| author | Dai, Jessica Raji, Inioluwa Deborah Recht, Benjamin Chen, Irene Y. |
| author_facet | Dai, Jessica Raji, Inioluwa Deborah Recht, Benjamin Chen, Irene Y. |
| contents | The need for developing model evaluations beyond static benchmarking, especially in the post-deployment phase, is now well-understood. At the same time, concerns about the concentration of power in deployed AI systems have sparked a keen interest in 'democratic' or 'public' AI. In this work, we bring these two ideas together by proposing mechanisms for aggregated individual reporting (AIR), a framework for post-deployment evaluation that relies on individual reports from the public. An AIR mechanism allows those who interact with a specific, deployed (AI) system to report when they feel that they may have experienced something problematic; these reports are then aggregated over time, with the goal of evaluating the relevant system in a fine-grained manner. This position paper argues that individual experiences should be understood as an integral part of post-deployment evaluation, and that the scope of our proposed aggregated individual reporting mechanism is a practical path to that end. On the one hand, individual reporting can identify substantively novel insights about safety and performance; on the other, aggregation can be uniquely useful for informing action. From a normative perspective, the post-deployment phase completes a missing piece in the conversation about 'democratic' AI. As a pathway to implementation, we provide a workflow of concrete design decisions and pointers to areas requiring further research and methodological development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18133 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aggregated Individual Reporting for Post-Deployment Evaluation Dai, Jessica Raji, Inioluwa Deborah Recht, Benjamin Chen, Irene Y. Computers and Society The need for developing model evaluations beyond static benchmarking, especially in the post-deployment phase, is now well-understood. At the same time, concerns about the concentration of power in deployed AI systems have sparked a keen interest in 'democratic' or 'public' AI. In this work, we bring these two ideas together by proposing mechanisms for aggregated individual reporting (AIR), a framework for post-deployment evaluation that relies on individual reports from the public. An AIR mechanism allows those who interact with a specific, deployed (AI) system to report when they feel that they may have experienced something problematic; these reports are then aggregated over time, with the goal of evaluating the relevant system in a fine-grained manner. This position paper argues that individual experiences should be understood as an integral part of post-deployment evaluation, and that the scope of our proposed aggregated individual reporting mechanism is a practical path to that end. On the one hand, individual reporting can identify substantively novel insights about safety and performance; on the other, aggregation can be uniquely useful for informing action. From a normative perspective, the post-deployment phase completes a missing piece in the conversation about 'democratic' AI. As a pathway to implementation, we provide a workflow of concrete design decisions and pointers to areas requiring further research and methodological development. |
| title | Aggregated Individual Reporting for Post-Deployment Evaluation |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2506.18133 |