What are some strategies to ensure fairness in machine learning?

Throughout the analytics lifecycle, there will be elements of Bias and fairness testing that can be incorporated. During data quality checks/EDA, necessary steps can be adopted to identify features that can be classified under imbalance or bias prone features to ensure they are monitored throughout the process. Sometimes, the features may not get involved in the modelling, it is still essential to track their performance once identified and communicated/flagged accordingly

To assure fairness in machine learning, start by clearly defining the model's goal and context, considering stakeholders and legal frameworks. It's imperative to measure biases, using techniques like equal opportunity to identify disparities in outcomes. Employing adversarial training methods can mitigate detected biases, ensuring balanced representation. The subsequent evaluation should include fairness metrics, assessing the model's ethical standing, and pinpointing biases. Finally, transparent communication of results through detailed reports and visualizations is vital for maintaining accountability and explaining the model's decisions and rationale.

The focus should be on making the overall system and outcomes fair, not just the ML model itself. It's really important to understand what are the ethical consequences of your system.

Ensure fairness in machine learning to build trust and avoid harm. Define fairness goals and metrics. Collect and prepare fair data. Mitigate bias during model training. Evaluate the model for fairness. Communicate the results. Have a diverse and inclusive team. Additionally: Use interpretable models. Use human-in-the-loop systems. Continuously monitor and improve the system. Fairness is essential for machine learning systems. I know that it is not always easy to ensure fairness in machine learning. But it is essential that we make every effort to do so. Our machine learning systems should be fair to everyone, regardless of their race, gender, or other personal characteristics.

Here are some other metrics you could consider for evaluating model fairness across different groups, in addition to TPR, FPR and FNR as mentioned by Atul Raghunathan. 1. Demographic parity difference 2. Equalized odds difference 3. Error rate difference As a rookie, I found the Fairlearn toolkit (fairlearn.org) very useful for this. Doesn't take a long time to get acquainted and has a lot of tools needed to measure data and model bias (including those mentioned above). It also has the inbuilt bias mitigation framework which works similar to a GAN, except the adversary tries to minimize grouping constraint violation. Recommended for rookies.

In my experience, there are three stages when bias can be mitigated. Pre-processing, In Processing, and Post Processing. Preprocessing algorithms include using Learned Fair representations, relabeling and reweighting of the inputs. In Processing algorithms include using regularuzation or constrained optimization, and post-processing includes thresdhold calibration for regression to classification tasks, and also information witholding.

Utilization of explainable artificial intelligence, especially instance-level explanations, may help point out any unfair or unjustified assumptions of the model. Instance-level explanations bring explainability to the model's decision process behind the selected instances. For example, if a model's errors were generally affected by unrelated personal information, it could be stated that the model has a bias.

Check out Aequitas, they are an entirely open source Bias and Fairness Audit tool. "Aequitas, an open source bias audit toolkit developed by the Center for Data Science and Public Policy at University of Chicago, can be used to audit the predictions of machine learning based risk assessment tools to understand different types of biases, and make informed decisions about developing and deploying such systems."