AI assurance encompasses a range of activities and methodologies aimed at verifying that AI systems operate as intended, are compliant with regulations, and are free from biases and errors that could lead to unfair or unsafe outcomes. And with the increasing integration of AI across various sectors—from healthcare to finance to government—the need for robust assurance mechanisms has never been more crucial.
The concept of assurance is not new. It has its roots in fields such as accounting and cybersecurity, ensuring that systems and processes are reliable and meet rigorous standards. In the context of AI, assurance involves providing stakeholders—developers, regulators, and users—with confidence that AI systems are safe, fair, and accountable.
While the concept is still evolving, AI assurance involves various governance mechanisms to develop trust in the compliance and risk management of AI systems. It includes tools and services necessary to provide trustworthy information about an AI system’s performance on issues such as fairness, safety, and reliability. Note that the current purported definition is heavily derived from the US’ National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF) in addition to the concepts laid out in the US AI EO and the EU AI Act.
As best practices and the greater AI ecosystem matures within agencies and organizations, AI assurance should cover the entire lifecycle of an AI system, from development to deployment and beyond. This includes regular audits, certification processes, and continuous monitoring to ensure the AI systems remain compliant and trustworthy throughout their operational life. As AI systems become more prevalent, the assurance processes will need to evolve to address their unique challenges (such as algorithmic biases and the need for explainability).
As AI continues to transform society, the need for effective, measured safeguards are vital to both building public trust and ensuring the systems themselves behave in expected ways. For example, AI systems can amplify biases present in training data, leading to unfair treatment of individuals or groups. They can also behave unpredictably in ways that are difficult to diagnose and correct. They can simply drift over time, becoming less accurate. Effective AI assurance is necessary to mitigate these risks and ensure that AI systems deliver their benefits without causing harm.
According to the United Kingdom’s Responsible Technology Adoption Unit, effective AI assurance requires a mature ecosystem of assurance products and services, including process and technical standards, repeatable audits, certification schemes, and advisory and training services. While United States’ NIST RMF addresses AI assurance from a risk management perspective, the ecosystem and practical application of this is still very much evolving and there is a need for better coordination among various stakeholders to address the fragmentation and confusion that currently exist in the field.
At the time of this document’s publishing (June 2024) the biggest identified challenge is the evolving nature of AI assurance and the lack of standardized practices and guidelines.
This white paper considers AI assurance and the efforts global governments have taken to form initial policies to regulate government agencies and private industry. However, these initial policies do not take into account varying private sector best practices given the demands of their varying missions and regulatory requirements imposed by sector.
While more prevalent in regulated industries- An example of this is the banking & financial industry and model risk management frameworks that have derived from OCC Guidelines. But different sectors may have varying requirements and expectations for AI systems which makes it difficult to establish a one-size-fits-all approach to assurance. Moreover, AI systems are inherently complex and often operate in a “black box” manner, making it challenging to understand their decision-making processes and identify potential issues.
Another issue is the dynamic nature of AI systems. Unlike traditional software, AI systems can change their behavior over time as they learn from new data. This requires continuous monitoring and updating of assurance practices to ensure that the systems remain compliant and trustworthy.
Several approaches can be employed to achieve effective AI assurance. These include:
Weights & Biases (W&B) is a leading MLOps and LLMops platform to train and fine-tune models, manage models from experimentation to production, and provides an AI system of record for the public sector. Weights & Biases can play a central role to help organizations achieve effective AI assurance with a suite of tools for tracking, visualizing, and optimizing machine learning models, ensuring that AI systems are developed and deployed responsibly.
Weights & Biases offers several features that are crucial for AI assurance:
Using W&B for AI assurance offers several benefits:
Weights & Biases has been on the forefront of AI development, trusted by leading foundational modeling and AI research private sector organizations including OpenAI, Microsoft, Cohere, and an ever-growing number of private and public sector organizations, including the US Department of Defense (W&B has IL5 certification) and US National Labs, and United Kingdom Government. Weights & Biases provides a proven, scalable, and secure platform to track and optimize these customers’ AI models, ensuring compliance with regulatory requirements and improving the transparency of their AI systems.
The practice of AI assurance will be a critical aspect of responsible AI development and deployment. As regulations evolve and AI systems become integrated to government agencies and the private sector, the need for proven platforms will continue to grow.
Weights & Biases provides a comprehensive platform that supports AI assurance by offering tools for tracking, visualizing, and optimizing machine learning models. By leveraging W&B, organizations can ensure that their AI systems are transparent, fair, and compliant with regulatory standards, ultimately building trust with users and stakeholders.
The journey towards effective AI assurance is ongoing, and platforms like Weights & Biases are vital for building a more trustworthy and reliable AI ecosystem. As the field continues to grow, it will be essential for organizations to adopt best practices and leverage advanced tools to achieve and maintain high standards of AI assurance.
Mark Kroto is the Head of Federal for Weights & Biases. With over a decade of experience with AI & data experience, Mark is passionate about helping clients leverage AI and MLOps to solve the complex problems of government that benefit mission and programs in agencies and industry alike. You can email him at mark.kroto@wandb.com
