AI Governance¶

November 15, 2023
in AI Engineering, Platform Engineering, AI Governance
3 min read

AI studios need production discipline

Microsoft has announced the public preview of an artificial intelligence (AI) development environment, Azure AI Studio, at Ignite. It brings model selection, data grounding, evaluation, content safety, and deployment tooling into a common workspace. The platform reflects how quickly generative AI development is moving from isolated notebooks toward managed application delivery.

A studio can make the path to a prototype remarkably short. The path to a dependable product still needs discipline.

November 6, 2023
in AI Governance, Knowledge Strategy, Product Management
3 min read

GPTs turn prompting into configuration management

At its first developer conference, OpenAI has introduced generative pre-trained transformers (GPTs): custom versions of ChatGPT that can combine instructions, uploaded knowledge, and selected capabilities for a particular purpose. People can build them without conventional programming and share them inside an organization or, eventually, through a public store.

This will make useful experimentation easier. It will also turn a large number of informal prompts into organizational configurations that can affect real work.

October 26, 2023
in AI Governance, Risk Management, Operating Models
3 min read

Frontier preparedness needs decision rights before the crisis

OpenAI has announced a Preparedness team and a challenge focused on risks from increasingly capable artificial intelligence models. The effort will examine areas such as cybersecurity, persuasion, autonomy, and other severe harms, with the aim of connecting evaluation to development and deployment decisions.

Preparedness is not only the ability to detect a dangerous capability. It is the ability to decide and act while the evidence is incomplete and the stakes are rising.

September 19, 2023
in AI Governance, AI Security, Risk Management
3 min read

Scaling policies turn capability into a management trigger

Anthropic has published a Responsible Scaling Policy (RSP) that ties increasingly strong safety and security measures to evidence that a model has reached particular dangerous capabilities. The policy introduces Artificial Intelligence Safety Levels (ASLs), loosely inspired by the graduated containment used for biological hazards.

The specific thresholds will require continued research. The management pattern is already useful: decide in advance which evidence changes the organization's obligations.

August 22, 2023
in Knowledge Strategy, AI Engineering, AI Governance
3 min read

Retrieval is a knowledge-governance problem

International Business Machines (IBM) Research has published a clear explanation of retrieval-augmented generation, an approach that gives a large language model access to external sources at the time of a request. Instead of relying only on patterns encoded during training, the system retrieves relevant material and uses it to generate a more current, domain-specific, and potentially verifiable answer.

Retrieval-augmented generation (RAG) is a promising architecture. It is not a substitute for governing the knowledge being retrieved.

July 6, 2023
in Platform Engineering, AI Governance, Product Management
3 min read

Stable model access is part of the control plane

OpenAI has made the Generative Pre-trained Transformer 4 (GPT-4) application programming interface generally available to existing paying developers and announced a retirement path for several older completion and embedding models. The two developments belong together. Production access is not only about opening capacity; it is about managing change.

When an external model becomes part of an application, version stability and migration become product concerns.

June 22, 2023
in AI Governance, Standards, Organizational Learning
3 min read

Generative AI guidance should be built in public

The National Institute of Standards and Technology (NIST) has formed a public working group to help develop guidance for generative artificial intelligence. The group will begin by considering how the Artificial Intelligence Risk Management Framework (AI RMF) applies to these systems and will draw input from industry, academia, government, and civil society.

That open process is not a detour on the way to a standard. It is part of how a credible standard is made.

May 25, 2023
in AI Assurance, Risk Management, AI Governance
3 min read

Model evaluation needs an early-warning function

Researchers from Google DeepMind and several partner organizations have proposed a framework for evaluating general-purpose artificial intelligence models for dangerous capabilities and misalignment. Their central idea is to test for emerging risks early enough that developers can change training, security, or deployment decisions before a capability becomes difficult to contain.

That makes evaluation more than a scorekeeping function. It becomes an early-warning system.