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Nov 11, 2022 · Fairness defintions identify historical systematic disadvantages. Fairness metrics quantify the presence of bias in our model. Often, systematic bias results from underlying data.
- Individual and Group Notions of Fairness
- Root Cause Analysis and Informed Mitigation
- Picking A Group Fairness Metric
- Putting It All Together
Usually, fairness is thought of as a concept that is enforced between two groups. A widely cited example is ProPublica’s analysis of the criminal risk assessments provided by the company NorthPointe. ProPublica’s review of NorthPointe’s predictions showed that the algorithm trained to predict whether incarcerated people would re-offend was biased a...
Let’s say we built a credit decisioning model that determines whether an individual should receive a loan. The model is trained on data that do not use any specific demographic features, such as gender or ethnicity. However, when evaluating outcomes after the fact, it can be seen that the model finds a slight correlation between approval rates and ...
Fairness is a sensitive concept. The decision of which group fairness metric to benchmark a model against defines a version of the world to which we aspire. Are we looking to make sure that men and women are given an opportunity at exactly equal rates? Or do we instead want to make sure that the proportion of unqualified people who are accepted by ...
In this blog post, we discussed three key points to creating a comprehensive fairness workflow for ensuring fairer machine learning models. Here’s to building better and more trustworthy models! 🍻
- Divya Gopinath
Mar 24, 2021 · What is fairness? In many ways, bias and fairness in AI are two sides of the same coin. While there is no universally agreed upon definition for fairness, we can broadly define fairness as the absence of prejudice or preference for an individual or group based on their characteristics.
Jul 18, 2022 · Selection bias can take many different forms: Coverage bias: Data is not selected in a representative fashion. EXAMPLE: A model is trained to predict future sales of a new product based on...
Law: fairness includes protecting individuals and groups from discrimination or mis-treatment with a focus on prohibiting behaviors, biases and basing decisions on cer-tain protected factors or social group categories.
Nov 6, 2019 · November 06, 2019. SEAN GLADWELL/Getty Images. Share. Save. Summary. Along with the growth of AI have come serious questions about our ability to build unbiased, “fair” algorithms. And it’s true...
Nov 8, 2022 · Defining fairness. A key step in approaching fairness is understanding how to detect bias in your data. Defining fairness at the start of the project’s outset and assessing the metrics used as...
