AI Governance¶

May 1, 2023
in AI Governance, Operating Models, Organizational Learning
3 min read

Responsible AI needs distributed ownership

Microsoft's chief responsible artificial intelligence officer has published a reflection on the company's program, emphasizing leadership commitment, inclusive governance, and actionable standards. The timing is useful. Generative systems are moving into products rapidly, and many organizations are discovering that an ethics statement does not tell a product team what to do on Tuesday afternoon.

Responsible artificial intelligence (AI) needs a central function. It cannot remain the central function's job alone.

April 13, 2023
in AI Strategy, Enterprise Architecture, AI Governance
3 min read

Model choice creates a portfolio to govern

Amazon Web Services (AWS) has announced Amazon Bedrock, a managed service intended to give customers access to foundation models from several providers through a common cloud environment. The appeal is obvious: teams can experiment with different models and choose the capability that fits the application without building the underlying infrastructure themselves.

Choice reduces dependence on a single model. It also creates a portfolio that somebody has to govern.

March 30, 2023
in AI Governance, Knowledge Strategy, Organizational Learning
3 min read

A framework needs a community of practice

The National Institute of Standards and Technology (NIST) has launched its Trustworthy and Responsible Artificial Intelligence Resource Center, known as the Artificial Intelligence Resource Center (AIRC). The new site gathers the Artificial Intelligence Risk Management Framework (AI RMF), its playbook, crosswalks, and implementation resources in one place.

Central access is useful. The larger opportunity is to create a place where organizations learn how the framework behaves in practice.

February 16, 2023
in AI Governance, Human-Centered AI, Product Management
3 min read

Model behavior is an organizational decision

OpenAI has published a useful account of a difficult problem: how should a conversational artificial intelligence system behave, and who should decide? The company describes tensions among default behavior, user customization, safety boundaries, and the wide range of values held by people who use the system.

The question is often framed as model alignment. For organizations deploying these systems, it is also product governance. Every default encodes a decision about authority, acceptable variation, and whose judgment applies when values conflict.

January 26, 2023
in AI Governance, Risk Management, Human-Centered AI
3 min read

AI risk management begins with context

The National Institute of Standards and Technology (NIST) has released version 1.0 of its Artificial Intelligence Risk Management Framework (AI RMF). It is voluntary, sector-neutral, and deliberately flexible. That may frustrate anyone looking for a short compliance checklist. It is also the framework's most useful design choice.

Artificial intelligence (AI) risk is not a property of a model in isolation. It emerges from what the system is asked to do, the conditions under which it operates, the people who depend on it, and the organization's capacity to recognize and respond when it fails.

January 11, 2023
in AI Governance, Operating Models, Knowledge Strategy
3 min read

Responsible AI needs an operating system

Google has opened the year by publishing its most detailed account yet of how it puts artificial intelligence principles into practice. The report covers governance reviews, technical tools, education, and work across product teams. Its value is not that Google has found a universal formula. It is that the report makes a less glamorous truth visible: responsible artificial intelligence (AI) is an operating problem.

Principles matter. They tell an organization what it is trying to protect. But principles do not decide whether a particular use should proceed, determine which test is sufficient, or preserve the evidence behind a difficult exception.