Recap of the Responsible Tech Summit: From Principles to Power: Embedding Accountability in the AI Era

Photo of the panel conversation for Beyond Benchmarks: Turning Evaluations into Enforceable Standards. From Left to Right: Courtney Radsch, Rumman Chowdhury, Cathy O’Neil, and Alex Bores.

The Responsible Tech Summit, hosted by All Tech Is Human on October 29, 2026, at the Times Center in New York City, brought together leaders across technology, policy, advocacy, and academia to address the most urgent challenges in AI governance, Safety by Design, and ensuring that our tech future is aligned with a thriving democracy. 

Our theme, Embedding Accountability in the AI Era, featured panel discussions, keynote presentations, and fireside chats focused on building safer, more equitable digital ecosystems and promoting technology that serves the public interest. By bridging the gap between tech builders, policy experts, and civil society, our gathering served as a major platform for actionable framework design, responsible AI deployment strategies, and cross-sector alignment.

See all of the videos below.

The Responsible Tech Summit was made possible with the support of Mastercard, Workday, Siegel Family Endowment, Microsoft, Google, Pinterest, AE Studio, GitHub, Kapor Foundation, IAPP, Cinder, and Civic Health Project. If you would like to be involved in future summits, reach out to us (hello@alltechishuman.org).

All Tech Is Human is a non-profit organization providing the social infrastructure and connective tissue for the cross-sector Responsible Tech ecosystem. By strengthening the important human layer, we improve society’s ability to tackle thorny tech & society issues and align our tech future with the public interest.

 
 
 

Videos from the Responsible Tech Summit

Welcome remarks by David Ryan Polgar, Founder & President of All Tech Is Human. David opens with a hopeful message that carries a challenge: AI isn’t going to “wipe us out in the next ten years,” so the real job is to build a world worth living in, and that means tackling the harms that are already here. That includes nonconsensual intimate imagery, algorithmic bias, and harms to kids. The bigger crisis is that we struggle to work together across sectors. That’s the gap All Tech Is Human fills: instead of chasing one issue that could be irrelevant in the near future, it builds a lasting community of valued stakeholders who care about our collective tech future. He also pushes back on the idea that technology is inevitable or out of our control. People build it, so people decide its guardrails and rules. He closes by pitching New York City as the right home for responsible tech, a middle ground between Silicon Valley’s speed and the EU’s caution.

 

Andrew Rasiej opens with a look back on thirty years in the field. In 1997, he was running Irving Plaza, a New York City nightclub, and found the high school across the street had no computers. He emailed ten friends to help build a lab, and two hundred showed up. Without knowing it, they were starting to close the digital divide. That experience convinced him New York has a uniquely empathetic tech scene, one focused on shifting the power from the few to the many. But the divide has grown worse, he says, and governments and other institutions can’t keep up with the pace of change. His answer is to rethink civic engagement. He tells a story about picking up an apple in the middle of Fifth Avenue before someone could slip on it. For him, civic life is more than voting and jury duty: it means looking out for one another, without waiting for someone else to step in. He urges the audience to build new institutions rather than patching old ones, and to work together towards a fairer, more human world.

 

Beyond Benchmarks: Turning Evaluations into Enforceable Standards was moderated by Courtney Radsch (Director of the Center for Media & Digital Governance, Open Markets Institute) and featured Rumman Chowdhury (CEO, Humane Intelligence PBC), Cathy O’Neil (Founder, Author, Mathematician & Data Scientist, O'Neil Risk Consulting & Algorithmic Auditing), and Alex Bores (Assemblymember, New York City’s 73rd District).

Courtney Radsch opens by pointing out that AI testing is never neutral: someone decides what gets tested, how, and who pays for it. Rumman Chowdhury argues that most evaluations stop at a model’s technical capabilities and miss its effects on schools, the economy, and daily life, so experts in those fields should be doing the testing. Cathy O’Neil goes further, saying frontier models can’t truly be audited at all. A narrow hiring algorithm produces a predictable score, but chatbots are so unpredictable that every benchmark plugs one hole while new ones keep opening. The best we can do, she says, is measure specific harms to specific people, one dial at a time. Assemblymember Alex Bores describes how industry lobbying forced him to drop an auditing requirement from New York’s RAISE Act. Rather than accept a watered-down version that would become a ceiling, he cut it to pass the law’s safety and disclosure rules.

The second half turns to power and money. Rumman says the focus on existential risk has crowded out discussions of harms like bias that are happening now, and that foundation rules barring lobbying keep civil society out of the room. To build a real market for independent evaluators, she proposes paying tech vendors based on whether their products actually deliver what they promise. Cathy credits lawsuits brought by grieving families with shifting public opinion where regulation stalled, and argues that real fines are what will bring the companies to auditors. Alex closes by rejecting the idea that we must choose between near-term and catastrophic risks. Keeping AI aligned with its users, he says, will take every tool available, from liability and audits to steep fines.

 

Kathy Pham (Faculty and Senior Fellow, Responsible AI, at Harvard University) structures her talk around two ideas: honoring expertise across disciplines matters more than ever, and better technology depends on paying attention to what usually goes unseen. She opens by having strangers in the audience tell each other what they’d be the go-to “phone a friend” for. Having the right expert to call, she says, makes big decisions faster and products better, whether you’re drafting state AI policy or designing a product. That matters even more now that companies brag about shipping faster and skipping product planning. Moving that fast makes it easy to skip the slow work of sitting with a nurse, a truck driver, or a student to understand what they actually face.

Her second theme comes from the idea of “making seen the unseen,” which means surfacing the people, communities, and lived experiences that technology tends to leave out. Otherwise, AI models trained on what’s already known just carry those gaps forward. The same goes for the less glamorous parts of tech, like infrastructure, energy, security, privacy, and the communities near new data centers. These get overlooked while consumer chatbots dominate the conversation. Drawing on a recent talk asking whether AI should reach bedtime stories to kids, she argues that each use of AI is a judgment call about what technology should take over and where human connection still matters. She closes by calling expertise in the room “an awesome kind of friction” that makes products better.

 

Safety by Design: Accountability at the Intersection of AI, Industry, and Advocacy was moderated by Shreya Singh Hernandez (Senior Manager, Aspen Digital, The Aspen Institute) and featured Julie Cordua (CEO, Thorn), Alexis Wales (Chief Information Security Officer, GitHub), and Phil Brennan (Co-Founder and COO, Cinder).

Shreya Singh Hernandez asks how to move from agreeing on what responsible AI looks like to actually building it. She starts by asking what each panelist would change about the last decade of tech safety. Julie Cordua says product managers should have been held to safety metrics, instead of handling safety as a separate team that only saw products right before launch. Phil Brennan wishes his early Facebook teams had put a dollar value on safety instead of treating it as a cost, something lawsuits are now doing for them. Alexis Wales draws on her years of experience in federal cybersecurity, arguing that safety teams deserve the same board attention, metrics, and budgets that security teams spent years earning. Phil adds that cybersecurity has another lesson to teach: companies can warn each other about threats without exposing user data, which remains much harder in trust & safety.

The panel then turns to measuring harm that never happened and to the risks of open-source AI. Julie notes that most AI-generated child sexual abuse material (CSAM) comes from open-source models. She describes Thorn’s research on spotting models that can produce such materials without generating it, and its work getting safety-by-design principles into NIST and IEEE standards. Phil argues that the best way to anticipate new harms is to bring in people who know how bad actors think, and points to app stores as powerful gatekeepers. On what’s working, Alexis credits GitHub’s decision to bring its safety and security teams closer, while Phil is encouraged to see trust & safety veterans at AI startups like Cinder. They end on a hopeful note: after a decade of cleaning up after social media, they see the larger AI companies taking note and choosing to build safety in from the start.

 

The Master's Tools: Using AI to Police AI: A conversation between Camille Francois (President, ROOST) and Julia Angwin (Founder, Proof News)

Julia Angwin opens by introducing Camille François as a role model. Eighteen months ago, Camille launched Robust Open Online Safety Tools (ROOST) to build and maintain open-source safety tools. Camille explains that the Internet was built on open, shared technology, but safety tools never were. Most of them date to the social media era, and they lag far behind the technology they’re meant to police. They’re also opaque, so users don’t know how their speech gets moderated, and expensive enough that only the biggest platforms can afford them. ROOST borrows from cybersecurity principles in that sense, where shared open-source building blocks mean no one has to reinvent encryption to launch a website. Julia adds that with big tech pulling back from content moderation, safety tools are what make it possible for smaller platforms to compete.

On AI, Camille says the technology both creates new harms and offers a chance to build better safeguards, but only if someone does the work. Safeguards are often weaker and more expensive to run in languages other than English, for example. Building safety tools in the open, she argues, lets real experts weigh in publicly. That beats the old model of consulting experts behind closed doors under nondisclosure agreements. Camille closes with her hope for a more diverse, decentralized Internet. Different communities would be free to define safety in their own ways, rather than relying on a handful of Silicon Valley companies.

 

Shared Risk, Shared Responsibility: Rethinking Responsible AI Governance. The conversation was moderated by Riki Parikh (Policy Director, Alliance for Secure AI) and featured Amanda Craig (Manager, Responsible AI Public Policy, Microsoft), Fernanda Del Castillo (Director of AI Governance, Mastercard), and Alisar Mustafa (Head of AI Safety, Duco).

Moderator Riki Parikh opens with a recent example: major US banks are finding third-party AI agents inside customer accounts and can’t tell whether they’re authorized. With little federal regulation, he asks, who’s responsible when an agent causes harm? Amanda Craig says agents act in the real world, so governance has to keep working while they run. That means limiting what an agent can access and do, testing it before and after launch, and watching what it does so someone can step in. Fernanda Del Castillo says information flows only one way along the AI supply chain. Her team can’t take vendors’ test results at face value and has to recheck them before deploying a model. Alisar Mustafa of Duco, which tests AI systems for companies and labs, warns that the biggest failures appear when risks combine and when models are tested over longer conversations or in languages other than English.

On liability, Fernanda argues that responsibility should follow whoever brings the risk and controls the decision. She describes Mastercard’s Agent Pay trust framework, which verifies an agent’s identity, its intent, and the limits on what it can do so that consumers, merchants, and banks can all trust a payment. Amanda adds that responsibility depends on who can see and control what, which is why monitoring matters for working out what went wrong. Alisar says shared responsibility only works if it’s spelled out in advance. On government’s role, the panelists favor shared standards like NIST’s and caution against rushing into agent-specific laws while existing ones still apply. For their one rule, Alisar asks for testing across languages and by human experts, Fernanda for ways to share testing information across the supply chain, and Amanda for strong governance programs that can keep up as the technology changes.

 

Alignment in Practice: Building AI That Wants to be Good. The conversation was moderated by Tori Elliott (Senior Reporter, WIRED), featuring Melanie Plaza (Chief Technology Officer, AE Studio) and Hale Sirin (Program Scientist, Schmidt Sciences AI and Advanced Computing Institute).

Tori Elliott asks whether AI can be good at scale, or whether its best uses come from narrow, specific contexts. Hale Sirin describes research she funds and leads on legal reasoning and humanities projects, including New Orleans jazz archives and dance and cinema history. She shows why “good” depends on context. A New Orleans team set out to digitize decaying records and found that people read the same Louisiana Creole and Cajun recordings in different dialects depending on their community, so the work now depends on residents’ feedback. Melanie Plaza says alignment isn’t a sci-fi doomsday concern but a practical question: does AI reliably do what we want, and what’s good, across the messy situations it meets in real life? That question, she says, is still unsolved.

Both argue that the riskiest problems sit at the tail: rare failures, and small communities and languages that offer little profit. Companies and labs have little reason to invest in those, so academia and philanthropy have to do the slow, failure-prone foundational research, even as government funding shrinks. They see any pause in AI development as a chance to speed up this work, not a solution in itself. Melanie warns that passing a set of evaluations can create false confidence. Hale adds that some qualities, like historical or cultural accuracy, have no clear right answer to measure against. Both are optimistic that when outside researchers do the risky early work, labs are far more willing to adopt what they find.

 

Trust as Strategy and the Fight Against AI-Enabled Fraud. The conversation was moderated by Sandra Khalil (Associate Director, All Tech Is Human) and featured Anya Schiffrin (Co-Director, Technology Policy and Innovation, Columbia University (SIPA)), Mackenzie Thomas (Group PMM, Google DeepMind), and Jeff Collins (Director of Trust & Safety, Amazon Web Services (AWS)).

Moderator Sandra Khalil leads a conversation on how AI is fueling fraud and what it takes to rebuild trust. Mackenzie Thomas explains SynthID, an invisible watermark that stays with AI-generated images, video, audio, and text even after they’re cropped or screenshotted. Anyone can now check for it by asking Google tools whether something is real. Jeff Collins says AI lets small teams run faster, more personalized scams around the clock. Bad actors exploit the seams between AI models, cloud providers, platforms, and banks, so no single company can stop them alone. Anya Schiffrin points out that scams now reach nearly everyone, including fake AI immigration lawyers on TikTok and romance scams that drain older people’s savings, and that they erode trust in institutions more broadly.

The panel then turns to solutions. Anya describes how countries like Singapore and Taiwan treat fraud as a pipeline running from phone companies to platforms to banks, and coordinate across all of them. She argues that the companies best placed to stop scams cheaply, especially big social media platforms, should bear the most liability. Jeff calls for shared, machine-readable formats for reporting abuse so companies can warn each other at machine speed, pointing to the Global Signal Exchange as a promising start. In closing, Anya calls for steep fines on platforms that ignore warnings, Mackenzie urges designing for ordinary users rather than the people in the room, and Jeff asks banks, cloud companies, and AI developers to share more signals. That unglamorous work, he says, is how you keep your grandmother from getting scammed.

 

Digital Entropy, a keynote by J. Trevor Hughes (President and CEO, IAPP) at the Responsible Tech Summit.

Trevor Hughes describes a state he calls “digital entropy”: disorder that keeps growing as fast-moving technology, geopolitics, and a pile of new rules collide. The EU alone has more than one hundred tech-focused laws, and Ireland has named fifteen different regulators for the EU AI Act. Meanwhile, US states have introduced close to a thousand AI bills this year. AI has also broken down the walls between privacy, cybersecurity, intellectual property, trust and safety, and other fields that used to work in silos, and organizations are still figuring out how to manage the overlap. Many have simply tacked “AI governance” onto the chief privacy officer’s title. IAPP’s research finds that only 17 percent of organizations have mature AI governance in place.

To show what’s missing, he asks the audience whether anyone feared for their life when they flipped a light switch that morning. A century and a half ago, people did. Today we trust that switch because of generations of building codes, safety certifications, inspectors, and licensed electricians. AI needs the same kind of trust built by people, he argues, but we haven’t trained the tens of thousands of AI governance professionals and auditors we already needed years ago. Policies that call for a human in the loop, he says, have to answer a basic question: who are those humans, and what tools will they need to do the work?

 

Better Tech Begins with Better Questions. Lightning Talk by Joshua Elder (Senior VP and Head of Grantmaking, Siegel Family Endowment).

Joshua Elder argues that in the rush to adopt AI, we’re skipping a basic step: asking whether we’re asking the right questions. He draws on his background as an educator. When he was pitched an AI tool to catch students cheating, the real question that occurred to him was what learning should look like now that AI is changing how we find knowledge and solve problems. Narrow questions like, “How can we get more schools to adopt AI?” quietly assume that AI is the answer. Broader ones, like “What do students and teachers need, and when could AI help?” leave room for other solutions. Because questions shape what we study, fund, and build, he says they work like infrastructure.

He also notes that questions carry power. Asking “How can we ensure AI is used ethically?” assumes we already agree on what’s ethical. Asking “Who gets to define ethical AI?” opens the door to community-driven standards and public ownership. Too often, he says, the people with money and authority set the questions, and communities are asked for feedback only after the big decisions are made. He closes with three calls to action: check your assumptions, bring communities in for co-designing before the question is settled, and be ambitious and rigorous about what’s worth asking in the first place.

 

Rebuilding the Commons: Community and Pluralism in the AI Era. The conversation was moderated by Kristin Hansen (Executive Director, Civic Health Project) and featured Carrie Varoquiers (Chief Impact Officer, Workday Foundation), Eli Pariser (Co-Director, New_ Public), and Alison Lee (Chief R&D Officer, The Rithm Project).

Moderator Kristin Hansen sets out to leave the audience hopeful, with a conversation about connection, community, and pluralism in the age of AI. Eli Pariser argues that AI can strengthen human connection. Throughout history, he says, people have found ways to use new technologies for the public good, the way public parks once started as a radical idea. New_  Public’s app Roundabout applies that thinking to neighborhoods online, with local “stewards” who keep conversations productive. It is now expanding across Chicago through a partnership with Chicago Public Media. Carrie Varoquiers explains how her excitement over ChatGPT turned to alarm once she saw how well it simulates empathy. The real existential risk, she argues, is the slow fraying of our relationships. Workday’s research found that one in five Gen Z workers says their workplace relationships have gotten worse since AI arrived. The foundation is responding with microgrants for potlucks, block parties, and game nights.

Alison says young people often hide their AI use for fear of being judged, so her team uses play to open up the conversation. She leads the panel and audience in a round of the AI Effect, a card game that asks players whether a use of AI supports or erodes human connection. She also shares a story from upcoming research about a young man whose AI helped him repair a fight with his girlfriend one day, then took his side in a rant the next. AI amplifies the habits we bring to it, she says, for better or worse. Carrie points to a wave of prosocial builders making tools that bring people together offline. Eli closes with hope: many of the systems that haven’t worked for everyone are now up for reimagining, and that work shouldn’t be left to Silicon Valley alone.

 

Looking Back to Look Ahead: Co-Designing AI After a Decade of Tech Lessons. A fireside chat between Sinead Bovell (Founder, WAYE) and Beth Goldberg (Executive Director, Humanity AI).

Futurist Sinead Bovell opens by calling AI a general-purpose technology, the kind society rebuilds itself around, and asks what the social media era can teach us. Beth Goldberg, who spent about a decade at Google working to reduce social media’s harms, goes back further, to the early Internet. Its builders deliberately made it an open public good, while social media later locked data and power inside a few companies. AI is at that same early-’90s moment now, she says. The rails for the AI economy are being laid, and the window to shape them is short. Beth makes the case for co-design with a story about her first job. She built a crowdsourcing platform for reporting human rights abuses during the Arab Spring, and almost no one used it, because she never asked people on the ground what they needed. Years later at Jigsaw, her team spent long stretches listening to residents of Bowling Green, Kentucky, before building a digital town hall with them. Ten percent of the town took part.

To change how the big AI labs behave, Beth pushes for collective agency rather than the narrow kind the labs promise, like having an agent clear your to-do list. She points to musicians and Hollywood unions that bargained together with AI companies. She’s also excited about a new benchmark from the MIT Media Lab that measures how AI affects people, including their creativity, critical thinking, and well-being. Universities, governments, and investors could use it to reward better models. Sinead adds that independent AI auditors, overseen by government the way accountants are, may matter more than waiting on new regulation. Both close with a call for positive visions of the AI future, noting that most Americans picture either nothing or the Terminator. Beth shares hers, including teachers and students setting the rules for an AI tutor together.

Learn About the Supporters of This Year’s Responsible Tech Summit

Workday

​Workday operates at the heart of the enterprise – HR, finance, and IT – where the margin for error is effectively zero. By tightly coupling AI with the context, guardrails, and trusted processes that run the business, Workday goes beyond AI that assists work to agents that do the work and drive measurable outcomes. More than 11,500 organizations worldwide, including more than 65% of the Fortune 500, trust Workday to deliver.

​Mastercard

​Mastercard is a global technology company in the payments industry, connecting consumers, financial institutions, merchants, and governments in more than 200 countries and territories. The company is committed to building a sustainable, inclusive digital economy that benefits everyone, everywhere.

​Pinterest

​Pinterest is a visual search and discovery platform where people find inspiration, curate ideas and shop products — all in a positive place online. Headquartered in San Francisco, Pinterest has over 600 million monthly active users worldwide.

​Microsoft

​Microsoft is a global technology leader empowering every person and organization on the planet to achieve more through its platforms and productivity tools. The company is committed to advancing responsible AI and ensuring emerging technologies are developed and deployed safely and ethically.

​Siegel Family Endowment

​Siegel Family Endowment takes an inquiry-driven approach to grantmaking, informed by the scientific method. As technology reshapes society, we fund organizations asking critical questions about its impact on learning, work, and infrastructure. Founded in 2011 by David Siegel, co-founder and co-chairman of Two Sigma, we advance technology in the public interest, supporting research and initiatives that imagine a more inclusive, resilient technology-driven society.

​AE Studio

​AE Studio is an applied AI and AI alignment company that builds software products for clients while advancing research on making advanced AI systems safe and beneficial. Its recent work with Anthropic, GRAM, trains dangerous knowledge into removable modules so it can be stripped from a model before release. AE Studio reinvests its commercial success into neglected approaches to AI alignment research. Learn more at ae.studio.

​Google

​Google's mission is to organize the world's information and make it universally accessible and useful. As a leader in AI and computing, the company is focused on developing technology responsibly to benefit people and society.

​Kapor Foundation

​The Kapor Foundation works at the intersection of racial equity and technology to expand access to K-12 computer science and postsecondary tech pathways, improve tech accountability and worker protections, and increase investments in infrastructure and innovation. We advocate for policy changes, build the capacity of partners, conduct research, support partners through grantmaking, and invest in entrepreneurship and VC. We believe that when technology includes diverse perspectives and is harnessed to address societal challenges, we can transform the tech ecosystem for Black, Latine, and Native communities, and society as a whole. Kapor Foundation works collectively with the Kapor Center family of organizations, Kapor Capital, and SMASH to reimagine and reconstruct a more inclusive and equitable technology sector.

​Check out Kapor Foundation's Responsible AI Principles for Advancing a More Equitable Innovation Future.

​IAPP

​The IAPP is a policy-neutral, not-for-profit association founded in 2000 with a mission to define, promote and improve the professions of privacy, AI governance and digital responsibility globally.

A quote from the team: “…Moving responsible technology from principle to practice requires people equipped to make it happen. IAPP supports that growing community of professionals working across digital responsibility, bringing together privacy, AI governance, cybersecurity, law, risk and related disciplines. Through research, training, certification, professional development and a global community, IAPP helps professionals build the skills, knowledge and connections needed to turn responsible technology into effective, durable governance.”

​Civic Health Project

​Civic Health Project is a nonpartisan nonprofit organization dedicated to advancing scalable solutions to the dangerous divisions that threaten American democracy. Since our founding in 2019, we have supported more than 75 academic researchers, grassroots practitioners, creatives, and technologists who are helping reduce polarization, strengthen civic norms, and foster social cohesion. Today, Civic Health Project focuses primarily on exploring the vast potential of promising new technologies to help resolve conflict, improve civil discourse and deliberation, foster social cohesion, and uphold societal norms.

​Normsy.ai, our flagship in-house project, brings this mission to life. It identifies toxic content in real time and equips users to respond with respectful, trust-building messages grounded in social science and supported by artificial intelligence. We are also part of the initiative The Forum, a new model for civic leadership at a time when trust is low, division is high, and the need for credible, citizen-led solutions has never been greater.

​GitHub

​GitHub is the world’s leading platform for agentic software development — powered by Copilot to build, scale, and deliver secure software. Over 225 million developers, including more than 90% of the Fortune 100 companies, use GitHub to collaborate, and more than 140,000 organizations have adopted GitHub Copilot.

​Cinder

​Cinder is the industry’s first Trust and Safety operations platform to help organizations combat Internet abuse at scale. We provide Trust and Safety teams with a single system to manage complex integrity operations and investigations to create a safe environment for their users.

 

Learn About the Speakers at This Year’s Responsible Tech Summit

 

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