Reena Jana
Reena is Head of AI Research & Standards for Trust and Safety at Google. She and her team of senior HCI and UX researchers collaborate with research, policy, design, and global community-facing teams on shaping best practices and industry standards for trusted and safe AI. Prior to Google, Reena was a product owner at IBM's Design Lab and narrative strategy lead on IBM's Strategic Editorial & Creative team, Executive Editor at frog design, and BusinessWeek's Innovation Department Editor.
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Calibrating Trustworthiness in GenAI
Allison Woodruff
Derrick Feldmann
Colleen Thompson-Kuhn
The Advertising Council Research Institute, The Advertising Council Research Institute (2026)
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Generative or “GenAI”—a type of artificial intelligence that can create new content, including text, images, music, and videos, by learning from existing data—is a constantly changing and improving tool gaining widespread use around the world. According to McKinsey’s 2024 Global Survey on AI adoption, 65% of professionals reported their organizations regularly using GenAI, up from 33% the year prior.
With GenAI no longer a new tool, and one with user adoption continuing to increase year over year, the Ad Council Research Institute (ACRI), in partnership with Google, set out to understand what the American public knows and feels about GenAI in 2025. Who’s familiar with GenAI, and who uses it? How do they feel about its role in work and at home? How much do these users believe in its usefulness and benefits? What messaging (explanations and in-app statements) are most helpful for users?
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Ten Insights from Other Domains That Inform Responsible AI Frameworks
Allison Woodruff
Angela McKay
Dunstan Allison-Hope
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (2026), 104–115
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The rapid growth of AI systems is being accompanied by new guidelines, principles, standards, regulations, and best practices (hereafter “frameworks”) that seek to ensure the responsible design, development, deployment, and use of AI systems.
Our premise is that the substance, implementation, and evolution of these AI frameworks can be informed by the practical experience of pursuing similar desired outcomes in other relevant domains (e.g., content moderation, human rights, climate change). This will help ensure that mistakes are not repeated and more rapid progress is made.
We used a “repetition test” to generate the following ten insights from other domains. Insights passing the “repetition test” are those that experts with thousands of hours of practical experience often repeat when describing the best practices that have emerged from their domain.
AI frameworks can draw from these ten insights, rather than invent entirely new approaches.
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“Discover AI in Daily Life”: An AI Literacy Lesson for Middle School Students
Allison Woodruff
Annica Schjott Voneche
Kelly Thunstrom
Rebecca L. Hardy
Derek R. Aoki
SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2 (2023), pp. 1327
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We describe “Discover AI in Daily Life”, a lesson in Google’s Applied Digital Skills curriculum. The lesson introduces elements of AI literacy and is freely available online at g.co/DiscoverAI. It is designed for middle school students while also supporting high school and adult learners.
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