Summary of Fairtargetsim: An Interactive Simulator For Understanding and Explaining the Fairness Effects Of Target Variable Definition, by Dalia Gala et al.
FairTargetSim: An Interactive Simulator for Understanding and Explaining the Fairness Effects of Target Variable Definition
by Dalia Gala, Milo Phillips-Brown, Naman Goel, Carinal Prunkl, Laura Alvarez Jubete, medb corcoran, Ray Eitel-Porter
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 abstract presents an interactive simulator, FairTargetSim (FTS), which demonstrates the impact of target variable definition on fairness in machine learning. The tool uses a case study of algorithmic hiring with real-world data and user-defined target variables. FTS is designed for algorithm developers, researchers, and non-technical stakeholders to illustrate how biases are often encoded in target variable definition itself, before any data collection or training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The simulator, FairTargetSim (FTS), shows how the way we define our target variable affects fairness in machine learning. This tool helps people understand that fairness issues can start even before collecting data or training a model. FTS uses a real-world example of hiring to show this effect and is available for everyone to use. |
Keywords
* Artificial intelligence * Machine learning
