Evaluating Model Fairness

Bias and fairness are crucial aspects to consider when developing machine learning models. Bias refers to systematic errors that arise due to discriminatory or unfair patterns in data, while fairness is the absence of prejudice or preference for an individual or group based on their characteristics. Sensitive groups, such as race, ethnicity, gender, age, religion, disability, and sexual orientation, are often the focus of fairness concerns in machine learning. Non-sensitive group bias occurs when a model consistently makes errors due to its inability to represent certain aspects of the data accurately. To address bias in machine learning models, it's important to identify the sources of bias and take steps to mitigate them. This can involve collecting more diverse and representative training data, selecting appropriate model architectures and algorithms, and using techniques such as regularization to prevent overfitting. It's also crucial to critically examine the assumptions and decisions made during the model-building process, and to involve diverse stakeholders in the development and evaluation of the model. Fairness metrics like recall parity, false positive rate parity, and disparate impact can help assess bias in machine learning models. The choice of fairness metric depends on the specific context and goals of the machine learning model being developed. Assessing bias for non-sensitive groups involves data analysis, model evaluation, and human review. The industry standard for evaluating fairness metric values is the four-fifths rule, which suggests a threshold between 0.8 and 1.25. The appropriate threshold for a fairness metric depends on factors such as acceptable levels of disparity, trade-offs with other performance metrics, and evaluation of multiple thresholds. To monitor fairness metrics for models in production, consider using tools like Arize to ensure that the models are fair, accurate, and aligned with values and goals.