Six chilling ways machine learning threatens social justice – IAM Network

There is no question that machine learning can and will change the way the world looks and runs, but with great power should come a significant amount of caution.When millions of lives are potentially being affected, it's important to ensure that predictive models aren't using inputs that make them inherently discriminatory, making sensitive inferences, or perpetuating negative and exploitative programs for the sake of efficiency and maximized profits.The process of establishing meaningful standards for machine learning will take time, but it is an imperative, not an option.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 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.