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Artificial intelligence (AI) is now widely used throughout society. The value of AI and the efficiency gains it generates promote its adoption. Every day, most of us rely on AI to do things like autocomplete our text messages, navigate us to a new area, and recommend what movie to watch next. Aside from these widespread uses of AI, authorities are beginning to identify areas where there may be a larger risk. According to the European Commission’s Digital Strategy website, these higher-risk domains can include the application of AI in employment, finance, law enforcement, and healthcare settings, as well as other areas where the outcomes might have a substantial influence on individuals and society. As AI adoption grows, there is a growing realization that enabling trustworthy AI is critical, with 85% of customers and 75% of CEOs now acknowledging its significance. Establishing guidelines, such as IBM’s trust and transparency principles, is critical for guiding the development and usage of trustworthy AI. The establishment of proper governance mechanisms for AI systems is important to put these concepts into practice.
AI governance is the act of governing an organization through its corporate instructions, staff, processes, and systems to direct, evaluate, monitor, and take corrective action throughout the AI lifecycle to ensure that the AI system operates as the organization intends, as its stakeholders expect, and as required by relevant regulation. We anticipate that regulations governing AI systems will evolve quickly and that operators and developers of AI systems will need to adapt quickly when policy initiatives and new regulations are implemented. Agile techniques, which were first promoted in the context of software development, are built on ideals such as collaboration and responding to quick change. Governments throughout the world are now using agile governance models to respond fast as technology evolves and to encourage innovation in new technology fields such as blockchain, driverless vehicles, and AI. The adoption of an agile strategy in AI governance can assist AI adopters in determining if changes in governance and regulatory requirements are appropriately and timely incorporated.
To satisfy the predicted legal requirements for AI systems, an agile approach to AI governance can benefit from the deployment of RegTech. RegTech, as defined in the World Economic Forum white paper “Regulatory Technology for the Twenty-First Century,” is “the application of various new technological solutions that assist highly regulated industry stakeholders, including regulators, in setting, effectuating, and meeting regulatory governance, reporting, compliance, and risk management obligations.” Chatbots that can provide regulatory advice, cloud-based platforms for regulatory and compliance data management, and computer code that enables more automated processing of regulatory data are all examples of RegTech. These RegTech solutions can function as part of a larger AI governance process and be integrated as components of larger AI governance processes, which may include non-tech components such as an advisory board, use case reviews, and feedback systems. Strong stakeholder buy-in and starting with essentials like a clear definition of AI, internal rules, and clarification on current regulatory requirements can help with integration into existing processes.
IBM OpenPages with Watson is a RegTech solution that can assist adopters in navigating a quickly changing regulatory and compliance environment. IBM has assisted companies like Citi, Aviva, General Motors, and SCOR SE in using RegTech to satisfy governance requirements, minimize risks, and manage compliance. IBM OpenPages with Watson is also used as a basic RegTech component in its internal end-to-end AI governance process. IBM OpenPages with Watson can assist in the collecting of compliance data on AI systems in order to assess compliance against business policy and regulatory standards. The use of RegTech for AI governance from the beginning of regulatory requirements for AI systems can help in the construction of a centralized regulatory library to assist in data collecting and tracking when data would otherwise exist in silos throughout the business. The business can possibly benefit from efficiencies in the processes and resources that enable these solutions by adopting a consolidated RegTech solution.
We believe that in 2022 and beyond, RegTech will play a critical role in AI governance procedures. We anticipate that RegTech solutions will continue to evolve to address the demands of businesses affected by new rules, standards, and AI governance requirements. AI is also likely to drive new requirements for specific RegTech functionality such as bias assessments (which could include specific metrics such as disparate impact ratio), automated evidence to monitor for drift in AI models, and other functionality related to the transparency and explainability of AI systems.
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