Summary of From Fairness to Infinity: Outcome-indistinguishable (omni)prediction in Evolving Graphs, by Cynthia Dwork and Chris Hays and Nicole Immorlica and Juan C. Perdomo and Pranay Tankala
From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs
by Cynthia Dwork, Chris Hays, Nicole Immorlica, Juan C. Perdomo, Pranay Tankala
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Social and Information Networks (cs.SI)
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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 explores the potential for beneficial structural change in professional networks through hiring platforms that can nudge link formation. By estimating the likelihood of edge formation in an evolving graph, the authors aim to reduce privilege and disadvantage. The study proposes simple and efficient online algorithms that satisfy outcome indistinguishability and omniprediction, with guarantees that improve upon current knowledge. These techniques are applied to evolving graphs, enabling multicalibrated predictions of edge formation and simultaneous optimization of loss measured by various social welfare functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Professional networks can either help or hinder opportunities through referrals and introductions. But what if we could change the way these networks work? This paper talks about how hiring platforms can help make things fairer. It uses special math to predict when people will connect with each other, which can help reduce inequalities. The researchers show that their methods are good at making predictions and can even help optimize social welfare functions. |
Keywords
* Artificial intelligence * Likelihood * Optimization
