Summary of Accounting For Ai and Users Shaping One Another: the Role Of Mathematical Models, by Sarah Dean et al.
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
by Sarah Dean, Evan Dong, Meena Jagadeesan, Liu Leqi
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This position paper argues that AI systems’ interactions with users should be formally modeled to account for the reciprocal shaping of AI and user preferences. The proposed formal interaction models can specify, monitor, anticipate, and control societal impacts by considering factors like style, granularity, mathematical complexity, and measurability. A case study on content recommender systems examines the existing literature on formal interaction models, highlighting design axes and use-cases. The authors urge the AI community to adopt formal interaction models when designing, evaluating, or auditing AI systems that interact with users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how AI systems affect people and vice versa. Right now, AI systems are used in many areas of life, but we don’t always think about how they influence each other. The authors suggest creating a new way to understand these interactions using “formal interaction models.” These models can help us design, test, predict, and even control the impact of AI on society. They use an example from content recommendation systems to show how this approach could work. |
