Summary of Specification Overfitting in Artificial Intelligence, by Benjamin Roth et al.
Specification Overfitting in Artificial Intelligence
by Benjamin Roth, Pedro Henrique Luz de Araujo, Yuxi Xia, Saskia Kaltenbrunner, Christoph Korab
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper explores the challenges of using machine learning and artificial intelligence approaches, highlighting concerns about bias, lack of control, accountability, and transparency. To address these issues, regulatory bodies need concrete specifications that formalize high-level requirements like fairness and robustness. However, integrating specification metrics into system development processes is complex due to possible trade-offs between different metrics and over-optimization. The authors define specification overfitting as a scenario where systems prioritize specified metrics over high-level requirements and task performance. They conduct a literature survey on 74 papers that propose or optimize specification metrics in AI fields like natural language processing, computer vision, and reinforcement learning. The results show that most papers implicitly address specification overfitting by reporting multiple metrics, but rarely discuss the role of specification metrics in system development or define their scope and assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence can be improved to make it fairer and more reliable. Right now, AI systems are not very good at being fair because they can learn from biased data or algorithms. This means that AI systems might make decisions based on things like race or gender, which is not right. The authors want to find a way to make sure AI systems are more transparent and accountable for their actions. They think that one way to do this is by creating specific rules or metrics that AI systems have to follow. However, they also know that this can be tricky because different people might have different ideas about what fairness means. |
Keywords
» Artificial intelligence » Machine learning » Natural language processing » Optimization » Overfitting » Reinforcement learning
