As machine learning (ML) systems continue to improve, its integration to systems making up the society becomes more seamless. Right now, ML is involved in making critical decisions such as court decisions and job hirings. Without a doubt, using ML in these processes will lead to more efficiency. With a good design, ML systems can also eliminate the biases humans have when it comes to their decisions. On the other extreme, this integration could end up really ugly.

The opportunities that artificial intelligence (AI) can unlock for our world -- from discovering cures to diseases that kill millions each year to significantly reducing carbon emissions -- are expanding every day -- and is already enabling pathways to financial inclusion, citizen engagement, more affordable healthcare, and many more vital systems and services. The same types of machine learning systems that might have highlighted a certain post in your Facebook newsfeed based on your online activity are being leveraged, for instance, to highlight certain applicants in a hiring process. While public attention often focuses either on the existential threats artificial super-intelligence poses to humanity ("the robots are coming to kill us"), or the opposite salvation narrative (" AI will solve all our problems") there is a more immediate-but less visible- risk that our reliance on ML-driven decision making poses in terms of the reinforcement of systemic bias and discrimination. Machine learning technologies are already making life-altering decisions for human lives on a daily basis. Examples come from the New York Times: "Algorithms can decide where kids go to school… where building code inspections should be targeted, and even what metrics are used to rate a teacher."

The World Economic Forum (WEF)'s Global Future Council on Human Rights recently issued a white paper to provide a framework for developers to prevent discrimination in the development and application of machine learning (ML). The paper is based on research and interviews with industry experts, academics, human rights professionals and others working at the intersection of machine learning and human rights. The paper proposes a framework based on four guiding principles - active inclusion, fairness, right to understanding, and access to redress - for developers and businesses looking to use machine learning. Artificial intelligence systems based on machine learning are already being used to make decisions which have significant, life-altering impact on people, such as hiring of job applicants, granting loans and releasing prisoners on parole. Machine learning systems can help to eliminate human bias in decision-making, but they can also end up reinforcing and perpetuating systemic bias and discrimination.