The adoption of Machine Learning (ML) in decision making has amplified the risk of socially-biased outcomes. Anyone (not just data scientists) working on ML tools holds immense power over shaping the future of our world. However, we can use the power for good and train models that help to drive positive social change sooner. This talk will provide context for this issue, explore real-world examples and discuss ideas for potential solutions.

1. SOCIAL IMPLICATIONS OF BIAS IN MACHINE LEARNING
Fiona Coath

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https://slate.com/technology/2019/02/san-francisco-facial-recognition-technology-bill-ban.html
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