Government guidance: 10 core principles for stewardship of AI apps

I started supporting U.S. government contracts first as an operations research analyst, but that work quickly transitioned to being called advanced analytics, then cloud analytics, followed by data science, machine learning and now artificial intelligence (AI). Undoubtedly, many of you have seen the name of your field change over the years.

What’s interesting to me at this juncture is that the name-change-pathway seems to have broken through the clouds, and now it is top-of-mind in the zeitgeist. Evidence abounds in the popular business media, but we are also seeing some “official chatter” as well. For example, the U.S. government has published a variety of content addressing artificial intelligence and machine learning over the last few years, with the latest publication coming out just this week from the Executive Office of the President: Guidance for Regulation of Artificial Intelligence Applications (https://www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memo-on-Regulation-of-AI-1-7-19.pdf).

Ostensibly, the U.S. government is suggesting that the AI revolution is utterly transformative – a revolution that can do great good, but that must be nurtured and managed with precision and care. It starts with this:

• The policy of the United States Government [is] to sustain and enhance the scientific, technological and economic leadership position of the United States in AI.
• The deployment of AI holds the promise to improve safety, fairness, welfare, transparency and other social goals, and America’s maintenance of its status as a global leader in AI development is vital to preserving our economic and national security. 

These are excellent, laudable goals, but what of the government regulation? What can we expect to nurture, or manage, with precision and care? In its current draft form, the official guidance from the Office of Management and Budget will lay out 10 core principles for the stewardship of AI applications:

1. Public trust in AI – “the government’s regulatory and non-regulatory approaches to AI promote reliable, robust and trustworthy AI applications, which will contribute to public trust in AI”
2. Public participation – “agencies should provide ample opportunities for the public to provide information and participate in all stages of the rulemaking process”
3. Scientific integrity and information quality – “approaches to AI applications should leverage scientific and technical information and processes”
4. Risk assessment and management – “approaches to AI should be based on a consistent application of risk assessment and risk management across various agencies and various technologies”
5. Benefits and costs – “[agencies should] … select those approaches that maximize net benefits (including potential economic, environmental, public health and safety, and other advantages; distributive impacts; and equity)”
6. Flexibility – “agencies should pursue performance-based and flexible approaches that can adapt to rapid changes and updates to AI applications”
7. Fairness and nondiscrimination – “agencies should consider … whether the AI application at issue may reduce levels of unlawful, unfair or otherwise unintended discrimination as compared to existing processes”
8. Disclosure and transparency – “transparency and disclosure can increase public trust and confidence in AI applications”
9. Safety and security – “agencies should … encourage the consideration of safety and security issues throughout the AI design, development, deployment and operation process”
10. Interagency coordination – “a coherent and whole-of-government approach to AI oversight requires interagency coordination”

As an analytics society, at the forefront of this technological trend toward true or perceived artificial intelligence, do we agree with this framework? Do we agree with these priorities? Are the authors of these guidance documents consulting with our members? Join an existing conversation or start one of your own, and let us take hold of what is happening in the technical age we are living in.