Summary of Towards Human-ai Complementarity with Prediction Sets, by Giovanni De Toni et al.
Towards Human-AI Complementarity with Prediction Sets
by Giovanni De Toni, Nastaran Okati, Suhas Thejaswi, Eleni Straitouri, Manuel Gomez-Rodriguez
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to decision support systems uses prediction sets to help human experts solve classification tasks. Instead of providing single-label predictions, these systems provide a set of predicted labels and ask the expert to select the most accurate label. This paper investigates the effectiveness of these prediction sets by showing that they are often suboptimal in terms of accuracy. The authors then introduce an NP-hard problem: finding the optimal prediction sets that maximize human expert accuracy. However, they also propose a simple and efficient greedy algorithm that can find near-optimal prediction sets offering better performance than conformal prediction methods. This approach has been tested on both synthetic and real-world data and shows promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision support systems using prediction sets help experts solve classification tasks by providing multiple label predictions instead of single labels. In this study, researchers found that these prediction sets are not always the best choice for accuracy. They also discovered that finding the optimal prediction set is a difficult problem related to the famous P=NP question. However, they developed an easy-to-use algorithm that can find near-optimal prediction sets, which performed better than previous methods in practice. |
Keywords
» Artificial intelligence » Classification
