Addressing

Pre-processing, in-processing and post-processing quantitative approaches. As well as non-quantitative approaches: limit the use of ML, interpretability, explanations, address the root cause, awareness of the problem and team diversity(Source: flaticon)Fairness in machine learning is a complicated issue. To make matters worse, the people responsible for building models do not necessarily have the skills to ensure they are fair. This is because the reasons for unfairness go beyond data and algorithms. This means solutions…

Credit: Pixabay/CC0 Public Domain In 2020, a commentary published in Cell urged the National Institutes of Health (NIH) to address long-standing funding disparities between Black and white researchers. According to a 2011 study, Black applicants were 10 percentage points less likely to receive NIH funding than white applicants. A feature article in Chemical & Engineering News, an independent news outlet of the American…