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Today, machine learning (ML) is the most commercially viable subdiscipline of artificial intelligence (AI). ML systems are used to make high-stakes decisions in employment, bail, parole, lending and in many other applications throughout the world’s economies. In a corporate setting, ML systems are used in all parts of an organization – from consumer-facing products, to employee assessments, back-office automation, and more. Indeed, the past decade has brought with it wider adoption of ML technologies. But it has also proven that ML presents risks to it’s operators and consumers. Unfortunately, and like nearly all other technologies, ML can fail – whether by unintentional misuse or intentional abuse. As of today, the Partnership on AI Incident Database holds over 1,000 public reports of algorithmic discrimination, data privacy violations, training data security breaches and other harmful failures. Such risks must be mitigated before organizations, and the general public, can realize the true benefits of this exciting technology. As of today, this still requires action from people — and not just technicians. Addressing the full range of risks posed by complex ML technologies requires a diverse set of talents, experiences, and perspectives. This holistic risk mitigation approach, incorporating technical practices, business processes, and cultural capabilities, is becoming known as responsible AI.