New AI Governance is an Opportunity for Business Leaders to Accelerate Innovation and Profitability

Abhishek Gupta is a Senior Responsible AI Leader and Expert at the Boston Consulting Group (BCG) and Founder and Principal Investigator at the Montreal AI Ethics Institute. Risto Uuk is a policy researcher at the Future of Life Institute. Richard Mallah is his AI safety researcher at the Future of Life Institute. Frances Pye is an associate with the Boston Consulting Group.

Fritzchens Fritz / AI better image / GPU shot etching 1 / CC-BY 4.0

As the capabilities of AI, especially generative AI, advance rapidly, there is a growing need for governance systems to develop AI responsibly in ways that are beneficial to society. Much of the current discussion on responsible AI (RAI) focuses on risk mitigation. While important, this precautionary narrative overlooks the means by which regulation and governance foster innovation.

Suppose companies in various industries take a proactive approach to corporate governance. If so, this has the potential to boost innovation (similar to the UK government’s white paper on innovation-enhancing approaches to AI regulation) and profitability not only for individual companies, but also for the industry as a whole that designs, develops and deploys AI. We argue that there is. . This can be achieved through a variety of mechanisms outlined below, including improved system quality, project viability, safety competition to the top, usage feedback, and increased government funding and signaling.

Organizations that recognize this early will not only be able to innovate and become more profitable sooner, but they may also benefit from first-mover advantage.

1. Influential AI governance improves the quality of AI systems.

Successful adoption of AI governance tools can lead to higher quality AI systems. These high-quality systems should have fewer failures and be more trusted by downstream developers, consumers, and regulators, and can contribute to innovation and increased profitability through a variety of means. Governance standards that demonstrate this quality improvement, such as risk management standards and output explainability, increase trust levels with customers and create more robust AI systems that catch errors earlier in the AI ​​lifecycle and fix them more efficiently. By creating a development process, we will further facilitate this effect and improve the capabilities of AI. system developers to quickly address points of failure.

Graph: Key pathways for how good AI governance leads to innovation and profitability for business leaders.

Specific examples: Actionable codes of conduct, including reporting and transparency requirements. Companies can use codes of conduct to build concrete AI governance practices across the lifecycle of their AI systems. This will improve our internal AI development practices and improve the quality of our AI systems. This high-quality system works more reliably and more efficiently, reducing the risk of costly errors, project cancellations, or downtime, thereby increasing your return on investment (ROI). BCG’s recent RAI study found that companies that achieve RAI before scaling have a 9% reduction in AI system failure rates compared to those that don’t.

Such a framework also allows organizations to differentiate their brand to consumers and potential employees and avoid costly reputational damage. These mechanisms are especially important in the business-to-consumer (B2C) industry, where competition is intense and customer touch is high. Other governance tools that may induce similar mechanisms include three lines of defense (3LOD), regulatory sandboxes, incident databases, assurance standards, trusted rating lists, or regulations that set rules for game traffic lights. And so on. A comprehensive framework like the NIST AI RMF combines several of these elements to provide a ready-to-implement reference for your organization.

2. Strengthening AI governance can holistically drive the entire industry forward.

In addition to the specific mechanisms outlined above, an ecosystem of governance tools can work together to grow an industry, foster a safe race to the top, and facilitate the creation of new sub-industries.

First, regulation helps establish clear guidelines and standards for the development and deployment of AI systems, including criteria for accuracy, reliability, and risk management. Such guidelines provide a stable and predictable framework for innovation, reducing uncertainty and risk in AI system development. This will increase the participation of developers in this area and encourage greater investment from public and private organizations, thereby revitalizing the industry as a whole.

Regulatory mechanisms can also work together to foster a race to the top in safety, boosting confidence and the industry as a whole. The commercial aviation industry provides such an example. The Civil Aviation Safety Team (CAST) monitors incident data, often from voluntary reporting programs, to proactively detect risks and implement mitigation strategies before an incident occurs. Since that initiative, the risk of fatalities in US civil aviation has decreased by 83% from 1998 to 2008, while the size of the commercial aviation industry has increased by approximately 5% per year in passenger-kilometers. This race to the top in safety will increase assurance and consumer confidence, leading to more investment from more credible investors.

Finally, governance can also trigger the creation of new industries to meet governance goals. For example, organizations may innovate in cost-effective ways to increase the explainability of their models and experiment with more creative propositions such as regulated markets.

3. Effective AI governance can significantly increase the trust of governments and governance bodies in organizations developing and deploying AI systems. This is important for investing, raising and signaling to other investors.

Governments and governance bodies have a rich history of successful investments in AI technology and its inputs (Open Data Institute, Horizon Europe, etc.) It has also acted as a demand-side stimulator for high innovation. Many of the technologies we use today. Examples of such include his innovations at DARPA that formed the basis of the Internet, and financial support for new technologies (such as consumer solar panels) through subsidy programs. Moreover, governments are important buyers of technology and can also facilitate the large-scale adoption of new technologies. Finally, government funding also acts as a signaling mechanism to demonstrate the value and safety of investment in private industry. Such investments will help drive innovation in AI systems. Therefore, implementing effective AI governance will enable organizations to access the innovation and profitability benefits that government trust provides.

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Contrary to popular belief that regulation stifles innovation or that AI regulation should only be considered for risk mitigation, AI governance can improve the quality of AI systems and improve AI safety. It provides organizations with significant innovation opportunities by driving competition to the top in the industry. , to encourage greater government investment in AI technology. Additionally, early investments in AI governance capabilities can give organizations a competitive edge as first movers.

Acknowledgments: The authors would also like to thank Sabrina Kuespert for her valuable contributions and insights.

Abhishek Gupta is a Senior Responsible AI Leader and Expert at Boston Consulting Group (BCG), advising clients on building end-to-end Responsible AI (RAI) programs. He is also the Founder and Principal Investigator of the Montreal AI Ethics Institute, an international non-profit research institute whose mission is to democratize AI ethics literacy. He holds his BCG Henderson Institute Fellowship on Augmented Collective Intelligence, which studies complementary strengths in human-machine hybrid populations. Through his work as Chair of the Green Software Foundation’s Standards Working group, he has led the development of software carbon intensity standards to measure the environmental impact of these systems. He specializes in the operation, evaluation and evaluation of RAI and its deployment within an organization. He reduces the environmental impact of these systems. He has advised governments, multilateral organizations, academic institutions and companies around the world. He is the author of the widely read “State of AI Ethics Report” and “The AI ​​Ethics Brief”. He previously worked as a machine learning engineer at Microsoft, where his team helped solve the toughest technical challenges faced by his biggest Microsoft customers. He also served on his CSE Responsible AI Committee at Microsoft. Learn more about his work here.

Risto Uukis is a Policy Fellow at the Future of Life Institute, focused on AI policymaking research to maximize the social benefits of increasingly powerful AI systems. Additionally, he is a Working Group Expert on the Future of Work for the Global Partnership on AI, a Global Priorities Fellow of the Forethought Foundation, and an International Strategy Forum Fellow of Schmidt Futures. Previously, forward-looking at the World Economic Forum, he worked on a project on the future of the AI ​​economy, conducted research on trustworthy AI at the European Commission, and Berkeley Existential Risks, where he provided research support on European AI policy for the initiative. .

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Richard Mallahis is an AI safety researcher at the Future of Life Institute, focusing on the technical, strategic, and policy aspects of transformative AI safety. He has over 20 years of experience as an AI R&D leader, with experience in high-stakes risk management, management consulting, product management, and entrepreneurship. It brings a wide-angle perspective to construction work and aims to do more. Beneficial outcomes from moving to general-purpose AI systems. His new Center for AI Risk Management & Alignment will focus on closing important interdisciplinary gaps to improve societal risk from future innovative AI systems. Richard is a Senior Fellow at the Foresight Institute, a Senior Advisor at VC firm Lionheart Ventures, Director of AI Strategy at SuccessIf, and Executive Board Member of the Global Heath Initiative on the Ethics of Autonomous and Intelligent Systems at the IEEE.

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Frances Pye is an associate in the Boston Consulting Group’s London office focused on digital, data and technology strategy. Prior to this, she earned a master’s degree in technology policy from the University of Cambridge, where she studied the role of AI standards as effective AI governance her tools. She is also a Global Shaper for the World Economic Forum and co-leads the Data Her initiative on ways to increase public participation in discussions about the future of the economy.

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