Social implications of Bias in Machine Learning

Social implications of Bias in Machine Learning

The issue of how existing social bias is proliferated through machine learning is already present. Everyone working on ML tools, not only data scientists, all hold immense power to shape our future. However we can use that same power for good and train models that instead help drive positive social change sooner. That is why we all need to get a grounding in this issue, hear a collection of real examples and start discovering ideas for solutions.

During this talk I will address: – How our datasets and algorithms share societies historical biases. – Why this could cause results to be inaccurate and further exaggerate existing discrimination. – How we can measure the impact and change this from a risk into a powerful solution.

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