Where did my model go wrong? Toolkits of Interpretability, Bias and Fairness to the rescue at Data Science Festival

On the 20th May 2023, I presented at “Where did my model go wrong? Toolkits of Interpretability, Bias and Fairness to the rescue” workshop at the Data Science Festival 2023 conference.

As Machine Learning (ML) techniques are becoming more accessible to develop with the availability of open source software, an increase of data available across industries, and accessibility of online learning platforms to upskill people. Many industries are starting to adopt these techniques as a way to solve business problems, automate processes, improve profits or reduce costs. But what happens when a company wants to solve a problem through a ML model and simply it does not work. Where did the model go wrong?

In this talk, I covered the process of developing a ML model. At the different model development stages, he will highlight the components where a model can introduce biases and errors and will describe what are the techniques that can help to correct them. Once the model is developed, he will go through methods to interpret the ML models and how to verify if the model is producing fair outcomes.

The presentation is at the video below