Summary of Learning in Reverse Causal Strategic Environments with Ramifications on Two Sided Markets, by Seamus Somerstep and Yuekai Sun and Ya’acov Ritov
Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Markets
by Seamus Somerstep, Yuekai Sun, Ya’acov Ritov
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
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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 paper proposes a novel formulation of causal strategic classification, inspired by equilibrium models of labor markets. The authors show how employers can manipulate their outcomes through strategic hiring policies, leading to improved employer rewards, labor force skills, and equity in some cases. However, they also demonstrate that such performative employers can harm labor force utility and fail to prevent discrimination in other situations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how employers make decisions about who to hire, based on how workers will respond strategically to these decisions. The authors compare two approaches: one where employers take into account how their choices might affect the workers they’re hiring, and another where they don’t. They found that when employers consider the strategic responses of potential hires, it leads to better outcomes for everyone involved. |
Keywords
» Artificial intelligence » Classification
