How can you ensure that your machine learning models are not biased with respect to data privacy?

This is one important step often overlooked. The proper way is to create and maintain a data catalogue repository that documents the source, definition, data rules and logic, labelling and / or other pre-processing done, including time stamp of changes (if any), business owner, etc. That way, any subsequent use / interpretation can be managed with ease. The common way of use / mistake by analyst is to directly dive in to usage without proper understanding, or massaging of these raw data through scripting logics, and the problem is as different analytics / reports / ML models uses different scripting / filtering logics - thus creating issue of inconsistencies despite the use of common data source.

This part is technically the other core basic analytics checks on data. On top of data bias test, and the various statistical methods prescribed- it is recommended to also ensure that DQ checks are conducted to identify unintended noises with the data used. Secondly, it is important to consider dataset syncing of development vs implementation. For example - if sum of daily transaction datasets considered for model development is not available in actual / production environment - it doesn’t matter even if the ML model is strong if it cannot be implemented Lastly, consumption of raw datasets alone is insufficient to build strong ML models. Strong feature generation is an important component, almost the key secret sauce of a good ML model.

I think it is also very important to understand the kind of bias your models are prone to. With financial industry these are really strong compliance frameworks that prevent them using any protected attributes (gender, race etc.) to train their models but that leads to lack of testing for bias for such sources like in the case of Navy Federal Credit Union’s recent debacle. It’s important to test your datasets for such issues and be cognizant that bias can come unpredictable sources.

Typically when someone said that we have successfully implemented ML model, my first question will always be how is your model performing, is it stable, what performance metrics you are using to measure the model and what validation metrics were considered. Strangely enough, some vendors and people that I have met either does not track the performance, or just vaguely rely on customers’ feedback. Having sound performance metrics measurement allows you to understand your model, data and portfolio characteristics when business dynamics / environment changes, if there’s population shifts, data drift and also model lifecycle. Of course, if your infrastructure allows, nothing stop us from continuous training and retraining of model.

Ensuring machine learning models are not biased concerning privacy involves several steps. First, use anonymized data to train models, removing personally identifiable information. Implement privacy-preserving techniques like differential privacy to add noise to the data, protecting individual information. Regularly audit and test models for potential biases, ensuring fairness across different demographics. Lastly, comply with privacy regulations and seek expert advice to establish robust privacy measures throughout the ML process.