Lawfare Daily: Helen Toner and Zach Arnold on a Common Agenda for AI Doomers and AI Ethicists
Sep 13, 2024
auto_awesome
Helen Toner, Director of Strategy at Georgetown's CSET, and Zach Arnold, Analytic Lead also at CSET, dive into the rift between AI ethicists and doomsayers. They highlight the pressing need for a unified approach to AI governance amid rising stakes. The duo discuss the potential for bipartisan cooperation in legislation and the critical role of third-party audits to ensure transparency. They also touch on personal data privacy challenges and the necessity for continuous dialogue on AI’s societal implications, exploring pathways for a safer technological future.
The podcast emphasizes the need for AI regulation advocates to find common ground between 'doomers' and 'ethicists' to strengthen governance efforts.
Investing in AI measurement science is crucial for establishing regulatory foundations that objectively evaluate AI models for bias and reliability.
Deep dives
The Divide in AI Regulation Advocacy
The podcast discusses the divergent camps in AI regulation advocacy, specifically the 'Ethicists' and 'Doomers.' Ethicists focus on addressing current harms associated with AI technologies, such as algorithmic bias and non-consensual deepfakes, striving to mitigate risks affecting marginalized groups. In contrast, the Doomers anticipate potential future threats posed by advanced AI, speculating on existential risks to humanity. Although both groups share concerns for AI oversight, their differing priorities lead to an often polarized discourse that complicates the pursuit of common regulatory solutions.
Challenges of Political Consensus
The conversation highlights the complexities of achieving political consensus on AI regulation, noting that diverse perspectives within advocacy groups can hinder cohesive policymaking. Policymakers face difficulties when presented with conflicting opinions from experts, leading to a lack of clear direction on regulatory frameworks. Additionally, the emerging influence of Big Tech lobbyists adds a layer of complexity, as competing interests seek to shape legislation to either promote or hinder regulation efforts. This political landscape fosters a challenging environment for advocates aiming to unify perspectives for effective AI governance.
Importance of AI Measurement Science
A significant proposal presented in the podcast revolves around investing in AI measurement science to establish regulatory foundations. This involves developing the capacity to objectively evaluate AI models for bias, reliability, and potential harms, which is essential for responsible governance. By creating standards for measurement, stakeholders can better assess the risks associated with AI technologies and ensure accountability. It emphasizes that without these foundational measures, creating effective regulations will be exceedingly difficult, as current tools to evaluate AI systems are grossly inadequate.
Establishing a Third-Party Audit Ecosystem
The discussion underscores the value of establishing an effective ecosystem of independent third-party auditors for AI systems, promoting transparency and accountability within the industry. Auditors would provide verification of claims made by AI companies, mitigating information asymmetries that often exist between developers and regulators. Drawing on models from other industries, the podcast discusses the potential for government oversight to set quality standards for these evaluators. This approach aspires to ensure that AI systems operate within ethical boundaries and adhere to established regulations, creating a balance between innovation and safety.
Helen Toner, Director of Strategy and Foundational Research Grants at Georgetown University's Center for Security and Emerging Technology (CSET), and Zach Arnold, Analytic Lead at Georgetown University's Center for Security and Emerging Technology, join Kevin Frazier, Assistant Professor at St. Thomas University College of Law and a Tarbell Fellow at Lawfare, to discuss their recent article "AI Regulation's Champions Can Seize Common Ground—or Be Swept Aside." The trio explore the divide between AI "doomers" and "ethicists," and how finding common ground could strengthen efforts to govern AI responsibly.