Philip Dawson is a lawyer and public policy adviser specializing in the governance of digital technologies and AI. Currently serving as head of policy at Armilla AI, he has held senior roles at a United Nations agency, in government, and at an AI software firm and has worked with international organizations, academic research institutes, nonprofits, and private companies as an independent adviser on a range of AI policy issues. Dawson is a leader in national data and AI standards efforts in Canada and a member of the U.N. Global Pulse Expert Group on the Governance of Data and AI. He has degrees from the London School of Economics and McGill.
|Mature RAI programs minimize AI system failures. Neither agree nor disagree||“RAI programs have the potential to minimize AI system failures. While organizations have begun investing in RAI policies, procedures, and training, a large gap persists around the testing frameworks and tools needed to provide deeper insights into model quality, performance, and risk. A superficial approach to AI testing has meant that a large proportion of today’s AI projects either fail in development or risk contributing to real-world harms after they are released. To reduce failures, mature RAI programs must take a comprehensive approach to AI testing and validation.”|
|RAI constrains AI-related innovation. Strongly disagree||“Probably the greatest evidence we have that RAI does not stifle but rather unlocks innovation is the emergence of an increasingly large and expanding market of RAI SaaS providers developing everything from AI-enhanced de-identification tools to data quality solutions, automated quality assurance platforms, model testing and validation toolkits, and continuous monitoring tools — all of which will be leveraged to help operationalize compliance and emerging certification programs. RAI is accelerating time to market for organizations seeking to realize the benefits of AI for their businesses, and, as a result, it has led to the innovation of an entire new industry to support this demand.”|
|Organizations should tie their responsible AI efforts to their corporate social responsibility efforts. Neither agree nor disagree||
“Organizations may wish to tie their responsible AI efforts to their corporate social responsibility efforts, but this is a secondary priority. Establishing RAI policies and practices should be understood first and foremost as a proactive response to emerging legal, governance, and technical standards and an authentic expression of corporate values. Without this critical step, embedding RAI principles or themes into CSR programming will lack legitimacy, and in time it may ultimately undermine an organization's credibility.
Organizations that take a holistic approach to generalizing RAI across their operations, however, including adopting related CSR programs or meeting recognized ESG standards, have a better chance of getting off on the right foot. In this context, applying RAI to traditional CSR efforts can help translate commitments to shared principles of equity, fairness, and inclusion into broader-based social and environmental impact — which investors, boards, employees, business partners, clients, and consumers alike measure with increasing levels of scrutiny.”
|Responsible AI should be a part of the top management agenda. Strongly agree||
“Achieving responsible AI in practice requires translating emerging legal obligations and ethical principles into corporate policies and guidelines that engage a cross-functional team of legal, ethics, policy, risk and compliance, data science, and research professionals. In many cases, the effort will involve significant investment into new resources, such as personnel with sociotechnical expertise, or the procurement of new technical tools — for instance, to monitor the quality and performance of AI systems — or obtaining industry certifications. In short, implementing responsible AI demands significant organizational change and strategic direction.
As such, top management seeking to realize the long-term opportunity of artificial intelligence for their organizations will benefit from a holistic corporate strategy under its direct and regular supervision. Failure to do so will result in a patchwork of initiatives and expenditures, longer time to production, damages that could have been prevented, reputational damages, and, ultimately, opportunity costs in an increasingly competitive marketplace that views responsible AI as both a critical enabler and an expression of corporate values.”