How to Build Accountability into Your AI
It’s not easy to know how to manage and deploy AI systems responsibly today. But the U.S. Government Accountability Office has recently developed the federal government’s first framework to help assure accountability and responsible use of AI systems. It defines the basic conditions for accountability throughout the entire AI life cycle — from design and development to deployment and monitoring — and lays out specific questions for leaders and organizations to ask, and the audit procedures to use, when assessing AI systems.
When it comes to managing artificial intelligence, there is no shortage of principles and concepts aiming to support fair and responsible use. But organizations and their leaders are often left scratching their heads when facing hard questions about how to responsibly manage and deploy AI systems today.
That’s why, at the U.S. Government Accountability Office, we’ve recently developed the federal government’s first framework to help assure accountability and responsible use of AI systems. The framework defines the basic conditions for accountability throughout the entire AI life cycle — from design and development to deployment and monitoring. It also lays out specific questions to ask, and audit procedures to use, when assessing AI systems along the following four dimensions: 1) governance, 2) data, 3) performance, and 4) monitoring.
Our goal in doing this work has been to help organizations and leaders move from theories and principles to practices that can actually be used to manage and evaluate AI in the real world.
Too often, oversight questions are asked about an AI system after it’s built and already deployed. But that is not enough: Assessments of an AI or machine-learning system should occur at every point in its life cycle. This will help identify system-wide issues that can be missed during narrowly defined “point-in-time” assessments.
Building on work done by the Organisation for Economic Co-operation and Development (OECD) and others, we have noted that the important stages of an AI system’s life cycle include:
Design: articulating the system’s objectives and goals, including any underlying assumptions and general performance requirements.
Development: defining technical requirements, collecting and processing data, building the model, and validating the system.
Deployment: piloting, checking compatibility with other systems, ensuring regulatory compliance, and evaluating user experience.
Monitoring: continuously assessing the system’s outputs and impacts (both intended and unintended), refining the model, and making decisions to expand or retire the system.
This view of AI is similar to the life-cycle approach used in software development. As we have noted in separate work on agile development, organizations should establish appropriate life-cycle activities that integrate planning, design, building, and testing to continually measure progress, reduce risks, and respond to feedback from stakeholders.
At all stages of the AI life cycle, it is important to bring together the right set of stakeholders. Some experts are needed to provide input on the technical performance of a system. These technical stakeholders might include data scientists, software developers, cybersecurity specialists, and engineers.
But the full community of stakeholders goes beyond the technical experts. Stakeholders who can speak to the societal impact of a particular AI system’s implementation are also needed. These additional stakeholders include policy and legal experts, subject-matter experts, users of the system, and, importantly, individuals impacted by the AI system.
All stakeholders play an essential role in ensuring that ethical, legal, economic, or social concerns related to the AI system are identified, assessed, and mitigated. Input from a wide range of stakeholders — both technical and non-technical — is a key step to help guard against unintended consequences or bias in an AI system.
