Fairness, bias, and interpretability in artificial intelligence and machine learning models. As machine learning applications become more widespread, there has been great interest in implementing algorithms to transform usual business processes to realize efficiencies.
As machine learning applications become more widespread, there has been great interest in implementing algorithms to transform usual business processes to realize efficiencies. From loan approvals to judicial sentencing, consumers and citizens face the reality of a black-box model as the final arbiter behind some of the most important decisions and events in our lives. It has become more critical than ever to understand the question of bias and fairness in the models we create and ensure that they don’t create unintended and/or discriminatory outcomes.
Machine learning bias is not well-addressed, or even well-understood, in data science. But researchers and other practitioners have taken steps to highlight the importance of mitigating sources of bias and finding solutions to prevent harms resulting from them. The following articles are a selection of our best stories on bias, fairness, and interpretability. We hope that they enrich your understanding and act as a resource of best practices when you encounter biases in your own models.
Elliot Gunn, Editor at Towards Data Science.
By Cassie Kozyrkov — 4 min read
The AI bias trouble starts — but doesn’t end — with definition. “Bias” is an overloaded term which means remarkably different things in different contexts.
By Alexander Watson — 5 min read
Generate artificial records to balance biased datasets and improve overall model accuracy
By Scott Lundberg — 11 min read
Avoid the black-box use of fairness metrics in machine learning by applying modern explainable AI methods to measures of fairness.
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