Summary of Fair Multivariate Adaptive Regression Splines For Ensuring Equity and Transparency, by Parian Haghighat et al.
Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency
by Parian Haghighat, Denisa G’andara, Lulu Kang, Hadis Anahideh
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Predictive analytics is widely used across various domains, including education, to inform decision-making and improve outcomes. However, many proprietary models are inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, opaque and incomprehensible models reduce trust among officials who use them, while introducing or exacerbating bias and inequity is a concern. Therefore, there is a need for transparent, interpretable, and fair predictive models that can be easily adopted and adapted by different stakeholders. The proposed fairMARS model incorporates fairness measures in the learning process using multivariate adaptive regression splines (MARS). MARS performs feature selection, handles non-linear relationships, generates interpretable decision rules, and derives optimal splitting criteria on variables. The paper integrates fairness into the knot optimization algorithm and provides theoretical and empirical evidence of how it results in a fair knot placement. Our fairMARS model is applied to real-world data, demonstrating its effectiveness in terms of accuracy and equity. The proposed approach contributes to the advancement of responsible and ethical predictive analytics for social good, making it an important step towards developing more transparent and interpretable models that can be used in various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive analytics helps make decisions better. But many models are secret and hard to understand, which makes them unreliable. This is a problem because these models can also make things worse by being unfair or biased. To solve this issue, we need models that are clear, fair, and easy to use. Our new model, called fairMARS, does just that. fairMARS uses a special way of analyzing data called multivariate adaptive regression splines (MARS). This method is good at finding patterns in data and making decisions based on those patterns. We made sure our model was fair by adding special rules to make sure it didn’t favor one group over another. We tested our model with real-world data and showed that it works well. This new approach helps us make better predictions while being more transparent and fair, which is important for using models in many different areas of life. |
Keywords
* Artificial intelligence * Feature selection * Optimization * Regression
