Identify guiding principles for responsible AI

In the last unit, we discussed some of the societal implications of AI. We touched on the responsibility of businesses, governments, NGOs, and academic researchers to anticipate and mitigate unintended consequences of AI technology. As organizations consider these responsibilities, more are creating internal policies and practices to guide their AI efforts.

At Microsoft, we’ve recognized six principles that we believe should guide AI development and use: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. For us, these principles are the cornerstone of a responsible and trustworthy approach to AI, especially as intelligent technology becomes more prevalent in the products and services we use every day.

Fairness

AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or employment, they should make the same recommendations to everyone with similar symptoms, financial circumstances, or professional qualifications.

To ensure fairness in your AI system, you should:

• Understand the scope, spirit, and potential uses of the AI system by asking questions such as, how is the system intended to work? Who is the system designed to work for? Will the system work for everyone equally? How can it harm others?
• Attract a diverse pool of talent. Ensure the design team reflects the world in which we live by including team members that have different backgrounds, experiences, education, and perspectives.
• Identify bias in datasets by evaluating where the data came from, understanding how it was organized, and testing to ensure it’s represented. Bias can be introduced at every stage in creation, from collection to modeling to operation. The Responsible AI Dashboard, available at the Resources section, includes a feature to help with this task.
• Identify bias in machine learning algorithms by applying tools and techniques that improve the transparency and intelligibility of models. Users should actively identify and remove bias in machine learning algorithms.
• Leverage human review and domain expertise. Train employees to understand the meaning and implications of AI results, especially when AI is used to inform consequential decisions about people. Decisions that use AI should always be paired with human review. Include relevant subject matter experts in the design process and in deployment decisions. An example would be including a consumer credit subject matter expert for a credit scoring AI system. You should use AI as a copilot, that is, an assisting tool that helps you do your job better and faster but requires some degree of supervising.
• Research and employ best practices, analytical techniques, and tools from other institutions and enterprises to help detect, prevent, and address bias in AI systems.

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