Six Ways Machine Learning Threatens Social Justice « Machine Learning Times

When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice – linking to short videos that dive deeply into each one – and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism. When you use machine learning, you aren't just optimizing models and streamlining business. In essence, the models embody policies that control access to opportunities and resources for many people.