Advancing Responsible AI Innovation A Playbook 2025

Government leaders Key roadblocks organizations encounter from the broader ecosystem Absence of guiding principles, benchmarks, and shared accountability structures, impacting responsible AI design and implementation. New AI industries without design standards, such as AI therapy and companionship (which are emerging as the number-one generative AI use case81), highlight sensitive data collection and privacy concerns and pose unique challenges in terms of trust and security, drawing attention to the need for metrics that assess risk across psychological, ethical and social dimensions. Misaligned expectations across the AI value chain, from third-party vendors to the organization’s specific responsible AI practices, leading to friction or inconsistencies in downstream use. Reliance on venture capital or corporate backing as AI funding models, prioritizing short-term monetization and market success over long-term governance of products that promote the common good. Actions for government leaders –Take a socio-technical approach to risk management: Evolve government AI frameworks, policies and regulations to move beyond narrow technical engineering perspectives and consider the role of broader societal forces in determining AI’s outcomes.82 Example approaches include: –Fund interdisciplinary research on AI’s economic, social, environmental and political effects. –Ensure employees have a voice in the deployment of workplace AI technologies, including protecting organizing rights, strengthening whistleblower protections and prohibiting surveillance practices that deter collective action or expression. –Prevent outsized influence from any individual stakeholder group in deciding what constitutes risk or harm, or to what values AI should be aligned.83 –Harmonize responsible design standards for AI: Collaborate across borders and work with the design community and impacted stakeholder groups to create consensus around design risks and mitigation approaches (see Case study 8). Develop public toolkits to drive awareness and fund sandboxes to experiment with safety- centred user experience (UX) innovation. Encourage adoption of standards and frameworks for impacted stakeholder groups. For example, AI products used by children need design standards for age-appropriate interfaces, explainability and safeguards against manipulation, false or misleading information.84 –Address evolving human-AI interaction impacts: Adopt a multi-pronged approach, which could include: –Informing ethical design standards with multi-disciplinary research that assesses impacts across diverse stakeholder groups, such as child-85 or older adult-facing86 products offering AI companionship. –Proactively examine emerging areas of human-AI interaction, such as AI use in neurotechnology. –Assess impacts on data practices, including collection and monetization of sensitive data, such as for engagement-based design. Address gaps in data governance policies (see Play 2). –Creating public campaigns to increase awareness of the benefits and risks, including AI literacy in education systems. –Working with multilateral bodies to enforce broad international adherence to human rights. –Incentivize the diversity of business models: Encourage approaches to alternative revenue generation opportunities that can deliver AI products with greater human alignment and evaluate models based on measures of success beyond profit and engagement metrics, such as contributions to scientific advancement and/or societal well- being. Enable academia and civil society to participate in public-interest frontier AI R&D with public compute, data access, and focused research grants, to offset the high costs associated with AI initiatives. Advancing Responsible AI Innovation: A Playbook 30