Responsible AI 2020: Expectations for the Year Ahead

  1. Governance — the underpinnings for responsible AI point to the need for end-to-end enterprise governance. At a high level, governance for AI should enable an organization to address important questions about the decision-making process of AI applications — identifying accountability; determining how AI aligns with business strategy; finding the business processes could be modified to improve results; putting controls in place to track performance and locate problems; and deciding whether the results are consistent and reproducible.
  2. Ethics and regulation — the primary goal is to aid organizations develop AI that is ethical and compliant with relevant regulations.
  3. Explainability — provide a vehicle for AI-driven decisions to be interpretable and easily explainable by those who are affected by them.
  4. Security — help organizations develop AI systems that are safe to use.
  5. Bias — address issues of bias and fairness so that organizations are able to develop AI systems designed to mitigate unwanted bias and achieve decisions that are fair in a well-communicated way.
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