The Future of RegTech for AI Governance

The use of artificial intelligence (AI) is now commonplace throughout society. The adoption of AI is driven by its utility and the improvements in efficiency it creates. Every day, most of us rely on AI for tasks like autocompleting our text messages, navigating our route to a new location, and recommending what movie to watch next. Beyond these common uses of AI, there are also uses that regulators are beginning to identify as areas where there may be a higher risk. These higher-risk areas can include uses of AI in employment, financial, law enforcement, and healthcare settings, as well as other areas where outcomes can have a significant impact to individuals and society [1].  Trustworthy outcomes are critical for all AI systems, particularly in high-risk contexts, and this is a key factor in why the market for responsible AI solutions is expected to double in size in 2022 [2]. The expected market growth coincides with increasing recognition that enabling trustworthy AI is important, with 85% of consumers and 75% of executives now recognizing its importance [3]. Establishing principles, such as IBM’s principles for trust and transparency, are important for guiding the development and use of trustworthy AI [3,4]. Central to putting these principles into practice is establishing the appropriate governance mechanisms for AI systems.

AI governance will require an agile approach

AI governance is an organization’s act of governing, through its corporate instructions, staff, processes, and systems to direct, evaluate, monitor, and take corrective action throughout the AI lifecycle, to provide assurance that the AI system is operating as the organization intends, as its stakeholders expect, and as required by relevant regulation. Regulations focused on AI systems are expected to evolve rapidly. In fact, regulators around the world have already been actively working to build policies for AI, as evidenced by the existence of more than 700 policy initiatives in 60 countries focused on AI [5]. While it is still unclear how the regulatory landscape will evolve, it is likely that proposals from the European Union, United States, and China will have a significant influence on the regulatory landscape over the next two years [6]. Operators and developers of AI systems will need to adjust quickly as policy initiatives and new regulations are passed. Agile approaches, first championed in a software development context, are based on values that include collaboration and responding to rapid change [7]. Agile governance approaches are now being used by governments around the world to respond quickly as technology advances and enable innovation in emerging technology areas such as blockchain, autonomous vehicles, and AI [8]. AI governance will also require the adoption of an agile approach in the coming years to ensure changes in governance and regulatory requirements are integrated appropriately and in a timely manner.

Integrating RegTech into broader AI governance process

An agile approach to AI governance can benefit from the use of RegTech to meet the expected regulatory requirements for AI systems. As defined in the “Regulatory Technology for the 21st Century” World Economic Forum white paper, RegTech 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 [9].” Examples of RegTech include chatbots that can advise on regulatory questions, cloud-based platforms for regulatory and compliance data management, and computer code that enables more automated processing of data relating to regulations [9]. These RegTech solutions will operate as part of a wider AI governance process and should be integrated as components of broader AI governance mechanisms that can include non-tech components such as an advisory board, use case reviews, and feedback mechanisms [10]. Integrating into existing processes requires strong stakeholder buy-in and beginning with basics such as a clear definition of AI, internal policies, and clarity on current legal requirements.

Case studies on OpenPages: Using RegTech for AI governance

IBM OpenPages with Watson is a RegTech solution that enables adopters to stay ahead in an environment with rapidly changing regulatory and compliance demands [11]. IBM has helped clients such as Citi, Aviva, General Motors, and SCORSE to leverage this RegTech to successfully meet governance requirements, mitigate risks, and enable compliance [11, 12, 13, 14, 15].  IBM is also using IBM OpenPages with Watson as a foundational RegTech component in its internal end-to-end AI governance process. IBM OpenPages with Watson is enabling the collection of compliance data on AI systems to evaluate compliance against corporate policy and regulatory requirements. The use of RegTech for AI governance from the early outset of regulatory requirements for AI systems has the benefit of enabling a centralized regulatory library to facilitate collection of data and tracking where data would otherwise exist in silos across the business. By leveraging a centralized RegTech solution, the business also benefits from efficiencies in the processes and resources enabling these solutions.

Looking forward: RegTech expected to play a central role in AI governance practices

RegTech will play a central role in AI governance practices in 2022 and beyond. We can expect that RegTech solutions will continue to adapt to meet the needs of companies who will be impacted by new regulations, standards, and AI governance requirements. AI is also likely to drive unique requirements for specific RegTech functionality relating to bias assessments (including specific metrics like disparate impact ratio), automated evidence to monitor for drift in AI models, and other functionality relating to the transparency and explainability of AI systems.

References

[1] https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

[2] https://www.forrester.com/blogs/predictions-for-2022-successfully-riding-the-next-wave-of-ai/

[3] https://www.ibm.com/thought-leadership/institute-business-value/report/ai-ethics-in-action

[4] https://www.ibm.com/policy/trust-principles/

[5] https://oecd.ai/en/dashboards

[6] https://www2.deloitte.com/xe/en/insights/industry/technology/technology-media-and-telecom-predictions/2022/ai-regulation-trends.html

[7]  https://agilemanifesto.org/

[8] https://www.weforum.org/global_future_councils/gfc-on-agile-governance/articles/regulation-for-the-fourth-industrial-revolution-in-2020

[9] WEF “Regulatory Technology for the 21st Century” report https://www.weforum.org/whitepapers/regulatory-technology-for-the-21st-century

[10] https://www3.weforum.org/docs/WEF_Responsible_Use_of_Technology_The_IBM_Case_Study_2021.pdf

[11] https://www.ibm.com/industries/banking-financial-markets/risk-compliance?utm_content=SRCWW&p1=Search&p4=43700069715740264&p5=e&gclid=EAIaIQobChMI89XvofiW9wIVQsmUCR0nVwneEAAYASAAEgI1tfD_BwE&gclsrc=aw.ds

[12] https://www.ibm.com/blogs/journey-to-ai/2021/07/citi-transforms-critical-internal-audit-with-machine-learning-nlp-and-ai/

[13] https://www.ibm.com/cloud/blog/how-aviva-modernizes-operational-risk-management-for-a-more-engaging-user-experience

[14] https://www.ibm.com/case-studies/general-motors

[15] https://www.ibm.com/case-studies/scor-se-ai-watson-cloud/

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