About

Rob Reich is the McGregor-Girand Professor of Social Ethics of Science and Technology at Stanford University. His scholarship in political theory engages with the work of social scientists and engineers, with a focus on the governance of frontier science and technology. In 2024-25, he was on public service leave serving as Senior Advisor to the United States Artificial Intelligence Safety Institute. Reich has authored and edited several books, including 'System Error: Where Big Tech Went Wrong and How We Can Reboot' and 'Digital Technology and Democratic Theory.' He has also written extensively on philanthropy, including 'Just Giving: Why Philanthropy is Failing Democracy and How It Can Do Better' and 'Philanthropy in Democratic Societies.' His early work concentrated on democracy and education, with publications such as 'Bridging Liberalism and Multiculturalism in American Education' and 'Education, Justice, and Democracy.' Reich has testified before Congress and contributed to public discourse through major outlets like The New York Times, Washington Post, Wired, Time Magazine, The Atlantic, The Guardian, and the Stanford Social Innovation Review. He has received multiple teaching awards, including Stanford’s highest honor for teaching, the Walter J. Gores award. Prior to his academic career, he was a sixth grade teacher at Rusk Elementary School in Houston, Texas. Reich is also a board member of Boston Review and the Spencer Foundation, and he helped create the global movement #GivingTuesday, serving as its inaugural board chair.

Research topics

• Computer Science
• Political Science
• Sociology
• Engineering
• Data science
• Law
• Social Science
• Artificial Intelligence
• Engineering ethics
• Computer Security
• Management science
• Operating system

Selected publications

• AI and Political Theory

  2026-05-04

  articleOpen accessSenior author

  In mapping the emerging landscape of political theory on artificial intelligence, this chapter identifies significant contributions to date as well as areas for future research, concerning (1) theories of justice, (2) democracy, and (3) rights. It addresses issues of algorithmic injustice, the digital public sphere, digital-age human rights, as well as the contested possibility of AI rights. Along the way, the chapter assesses what existing theories imply for new policy questions in a world with AI and what basic theoretical commitments or assumptions may need to be rethought.

  PublisherOA PDFDOI

• Exploring the mutations of society in the era of generative AI

  AI and Ethics · 2025-01-13

  articleOpen access
  PublisherOA PDFDOI

• Philanthropy and the All-Affected Principle

  Cambridge University Press eBooks · 2024-11-14 · 6 citations

  book-chapterOpen accessSenior author

  The All-Affected Principle (AAP) in democratic theory claims that all who are affected by a decision should be able to have a voice in that decision.Questions immediately arise: How wide is the scope of the principle and what are its grounds?In this chapter, we focus initially on a question concerning scope.The AAP is most frequently assumed to apply to formal political decision making.We explore whether the principle should have any purchase in a particularly prominent and powerful extra-governmental domain: philanthropy.Should the All-Affected Principle be an important norm of good philanthropic practice?If the AAP is understood modestly, then perhaps this is already the case.Donors and grant-making foundations often acknowledge the importance of learning from the feedback of two groups affected by their decision making: the grantees whose activities may be shaped by donor preferences and conditions (the strings attached to grants), and the beneficiaries whose interests those grantees attempt to advance.However, there is reason to doubt this modest application of the AAP.In foundation philanthropy, it remains rare to provide unrestricted general operating support to grantees, and many of the most prominent foundations deploy a decidedly technocratic approach (sometimes called strategic giving) that relies on highly targeted grant making.This approach treats grantees as subcontractors whose task it is to carry out a particular component of a vision or theory of change developed by philanthropists.The voices of beneficiaries and the knowledge possessed by grantees are routinely neglected. 1 In general, foundations pay lip service to the notion of empowering grantees and beneficiaries, while reserving the right to define for themselves the interests and effects that are relevant to their objectives in grant making.An honest assessment of the AAP as applied to philanthropy reveals that the ways that most foundations have attempted to incorporate grantee and beneficiary voice fall very far short of the kinds of democratic reforms that the AAP envisions.

  PublisherOA PDFDOI

• Philanthropy and Caring for the Needs of Strangers

  Social research · 2024-03-01

  article1st authorCorresponding

  ABSTRACT: People have been giving away their money, property, and time to others for millennia. What's novel about the contemporary practice of philanthropy is the availability of tax incentives to give money away. Such incentives are built into tax systems in nearly all developed and many developing democracies. In this sense, philanthropy is not an invention of the state but ought to be viewed today as an artifact of the state. This paper specifies and assesses three possible justifications for the existence of tax incentives for charitable giving, identifies a distinctive role for philanthropy in democracies, and argues for a fundamental redesign of the current legal framework governing philanthropy. Empirically, giving to assist the needy and care for strangers is an uncommon form of giving in the United States. Normatively, it is but one potential justification for philanthropy.

  PublisherDOI

• Against Perpetuity

  2023-01-01

  other1st authorCorresponding
  PublisherDOI

• 9 TESTING THE BOUNDARIES OF PARENTAL AUTHORITY OVER EDUCATION: THE CASE OF HOMESCHOOLING

  New York University Press eBooks · 2022-06-24 · 19 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• System Error: Where Big Tech Went Wrong and How We Can Reboot

  Perspectives on Science and Christian Faith · 2022 · 41 citations

  1st authorCorresponding
  • Computer Science
  • Computer Security
  • Computer Science

  SYSTEM ERROR: Where Big Tech Went Wrong and How We Can Reboot by Rob Reich, Mehran Sahami, and Jeremy M. Weinstein. New York: HarperCollins Publishers, 2021. 352 pages. Hardcover; $27.99. ISBN: 9780063064881. *Remember when digital technology and the internet were our favorite things? When free Facebook accounts connected us with our friends, and the internet facilitated democracy movements overseas, including the Arab Spring? So do the authors of this comprehensive book. "We shifted from a wide-eyed optimism about technology's liberating potential to a dystopian obsession with biased algorithms, surveillance capitalism, and job-displacing robots" (p. 237). *This transition has not escaped the notice of the students and faculty of Stanford University, the elite institution most associated with the rise (and sustainment) of Silicon Valley. The three authors of this book teach a popular course at Stanford on the ethics and politics of technological change, and this book effectively brings their work to the public. Rob Reich is a philosopher who is associated with Stanford's Institute for Human-Centered Artificial Intelligence as well as their Center for Ethics in Society. Mehran Sahami is a computer science professor who was with Google during the startup years. Jeremy Weinstein is a political science professor with experience in government during the Obama administration. *The book is breathtakingly broad, explaining the main technical and business issues concisely but not oversimplifying, and providing the history and philosophy for context. It accomplishes all this in 264 pages, but also provides thirty-six pages of notes and references for those who want to dive deeper into some topics. The most important section is doubtless the last chapter dealing with solutions, which may be politically controversial but are well supported by the remainder of the book. *Modern computer processors have enormous computational power, and a good way to take advantage of that is to do optimization, the subject of the first chapter. Engineers love optimization, but not everything should be done as quickly and cheaply as possible! Optimization requires the choice of some quantifiable metric, but often available metrics do not exactly represent the true goal of an organization. In this case, optimizers will choose a proxy metric which they feel logically or intuitively should be correlated with their goal. The authors describe the problems which result when the wrong proxy is selected, and then excessive optimization drives that measure to the exclusion of other possibly more important factors. For example, social media companies that try to increase user numbers to the exclusion of other factors may experience serious side effects, such as the promotion of toxic content. *After that discussion on the pros and cons of optimization, the book dives into the effects of optimizing money. Venture capitalists (VCs) have been around for years, but recent tech booms have swelled their numbers. The methodology of Objectives and Key Results (OKR), originally developed by Andy Grove of Intel, became popular among the VCs of Silicon Valley, whose client firms, including Google, Twitter, and Uber, adopted it. OKR enabled most of the employees to be evaluated against some metric which management believed captured the essence of their job, so naturally the employees worked hard to optimize this quantity. Again, such a narrow view of the job has led to significant unexpected and sometimes unwanted side effects. *The big tech companies are threatened by legislation designed to mitigate some of the harm they have created. They have hired a great many lobbyists, and even overtly entered the political process where possible. In California, when Assembly Bill 5 reclassified many independent contractors as employees, the affected tech companies struck back with Proposition 22 to overturn the law. An avalanche of very expensive promotion of Proposition 22 resulted in its passage by a large margin. *It is well known that very few politicians have a technical background, and the authors speculate that this probably contributes to the libertarian leaning prominent in the tech industry. The authors go back in history to show how regulation has lagged behind technology and industrial practice. An interesting chapter addresses the philosophical question of whether democracy is up to the task of governing, or whether government by experts, or Plato's "philosopher kings" would be better. *Part II of the book is the longest, addressing the fairness of algorithms, privacy, automation and human job replacement, and free speech. The authors point out some epic algorithm failures, such as Amazon being unable to automate resumé screening to find the best candidates, and Google identifying Black users as gorillas. The big advances in deep learning neural nets result from clever algorithms plus the availability of very large databases, but if you've got a database showing that you've historically hired 95% white men for a position, training an algorithm with that database is hardly going to move you into a future with greater diversity. Even more concerning are proprietary black-box algorithms used in the legal system, such as for probation recommendations. Why not just let humans have the last word, and be advised by the algorithms? The authors remind us that one of the selling points of algorithmic decision making is to remove human bias; returning the humans to power returns that bias as well. *Defining fairness is yet another ethical and philosophical question. The authors give a good overview of privacy, which is protected by law in the European Union by the General Data Protection Regulation. Although there is no such federal law in America, California has passed a similar regulation called the California Consumer Privacy Act. At this point, it's too soon to evaluate the effect of such regulations. *The automation chapter is entitled "Can humans flourish in a world of smart machines?" and it covers many philosophical and ethical issues after providing a valuable summary of the current state of AI. Although machines are able to defeat humans in games like chess, go, and even Jeopardy, more useful abilities such as self-driving cars are not yet to that level. The utopian predictions of AGI (artificial general intelligence, or strong AI), in which the machine can set its own goals in a reasonable facsimile of a human, seem quite far off. But the current state of AI (weak AI) is able to perform many tasks usefully, and automation is already displacing some human labor. The authors discuss the economics, ethics, and psychology of automation, as human flourishing involves more than financial stability. The self-esteem associated with gainful employment is not a trivial thing. The chapter raises many more important issues than can be mentioned here. *The chapter on free speech also casts a wide net. Free speech as we experience it on the internet is vastly different from the free speech of yore, standing on a soap box in the public square. The sheer volume of speech today is incredible, and the power of the social media giants to edit it or ban individuals is also great. Disinformation, misinformation, and harassment are rampant, and polarization is increasing. *Direct incitement of violence, child pornography, and video of terrorist attacks are taken down as soon as the internet publishers are able, but hate speech is more difficult to define and detect. Can AI help? As with most things, AI can detect the easier cases, but it is not effective with the more difficult ones. From a regulatory standpoint, section 230 of the Communications Decency Act of 1996 (CDA 230) immunizes the platforms from legal liability due to the actions of users. Repealing or repairing CDA 230 may be difficult, but the authors make a good case that "it is realistic to think that we can pursue some commonsense reforms" (p. 225). *The final part of the book is relatively short, but addresses the very important question: "Can Democracies Rise to the Challenge?" The authors draw on the history of medicine in the US as an example of government regulation that might be used to reign in the tech giants. Digital technology does not have as long a history as medicine, so few efforts have been made to regulate it. The authors mention the Association for Computing Machinery (ACM) Software Engineering Code of Ethics, but point out that there are no real penalties for violation besides presumably being expelled from the ACM. Efforts to license software engineers have not borne fruit to date. *The authors argue that the path forward requires progress on several fronts. First, discussion of values must take place at the early stages of development of any new technology. Second, professional societies should renew their efforts to increase the professionalism of software engineering, including strengthened codes of ethics. Finally, computer science education should be overhauled to incorporate this material into the training of technologists and aspiring entrepreneurs. *The authors conclude with the recent history of attempts to regulate technology, and the associated political failures, such as the defunding of the congressional Office of Technology Assessment. It will never be easy to regulate powerful political contributors who hold out the prospect of jobs to politicians, but the authors make a persuasive case that it is necessary. China employs a very different authoritarian model of technical governance, which challenges us to show that democracy works better. *This volume is an excellent reference on the very active debate on the activities of the tech giants and their appropriate regulation. It describes many of the most relevant events of the recent past and provides good arguments for some proposed solutions. We need to be thinking and talking about these issues, and this book is a great

  PublisherDOI

• Introduction

  2021-01-01 · 2 citations

  book-chapterSenior author
  PublisherDOI

• A Political Theory of Philanthropy

  2021-04-08

  book-chapter1st authorCorresponding

  People have been giving away their money, property, and time to others for millennia. In short, the author have some good prima facie reasons to doubt that philanthropy is redistributive in effect or eleemosynary in aim. When the justification for tax incentives for philanthropy runs along the pluralist line, philanthropy is not, at least in the first instance, about assisting the poor or disadvantaged; it is instead about protecting and promoting a flourishing and pluralistic civil society. Philanthropy is now embedded within a framework of public policies, many centered on the tax regime, that structures its practice and alters its shape from what it would otherwise be without the state’s intervention. A political theory of philanthropy might offer a defense, or several distinct defenses, of state incentives for giving money away, but the current practice of state-supported philanthropy, especially in the United States, is indefensible.

  PublisherDOI

• On the Opportunities and Risks of Foundation Models

  arXiv (Cornell University) · 2021 · 2169 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

  PublisherOA PDFDOI

Frequent coauthors

• Marietje Schaake

  6 shared

• Lucy Bernholz

  Stanford University

  5 shared

• Amy Gutmann

  4 shared

• Chiara Cordelli

  University of Chicago

  4 shared

• Stephen Macedo

  Princeton University

  4 shared

• Debra Satz

  Stanford University

  3 shared

• Jeremy M. Weinstein

  2 shared

• Nancy L. Rosenblum

  2 shared

Labs

• Center for Human Rights and International JusticePI

Education

• Ph.D., Political Science

  Stanford University

  1994

• M.A., Political Science

  Stanford University

  1991

• B.A., Political Science

  Harvard University

  1988

Awards & honors

• Walter J. Gores award, Stanford’s highest honor for teaching