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Jurity: State of the Art Open Source Software for AI Fairnessevaluation - Melinda Thielbar
Learn how to detect and prevent biases in AI models using Juridy, an open-source software, and statistical parity metric, ensuring fairness in industries like finance and hiring practices with expert Melinda Thielbar.
- It’s essential to have a range of surrogate classes, at least 100, to effectively evaluate AI fairness.
- Juridy, an open-source software, is designed to detect biases in AI models and can be used in various industries, including finance and hiring practices.
- Fairness testing is necessary to ensure that AI models do not inadvertently discriminate against certain groups based on demographics.
- Statistical parity is a metric used to measure fairness, and it looks at the difference in the prediction rates between different demographic groups.
- The Juridy library provides a way to calculate statistical parity and other fairness metrics, such as equal opportunity and predictive parity.
- A well-designed model should perform equally well for everyone, regardless of demographic group.
- Surrogate classes can be used to group people by demographics, and model performance metrics should not change when demographic groups are used.
- Demographic profiles can be calculated based on changes in fairness metrics, allowing for a deeper understanding of model biases.
- Measurement error can occur when surrogate classes are not well-designed, which can lead to biased statistics.
- Small probabilities and small counts can affect the accuracy of fairness metrics, but the Juridy software is designed to account for these issues.
- The speaker, Melinda Thielbar, recommends using zip code as a demographic factor to detect bias in AI models.
- Biases in AI models can be unintentional, but fairness testing can help to catch and address these biases before they become a problem.
- The Juridy software is currently available and can be used by anyone who wants to test the fairness of their AI models.
- Prevention is key when it comes to avoiding biases in AI models, as it is easier to detect and address biases before they become a problem.