“Derek leben: All of us are going to become ethicists”
The Road to Accountable AI Podcast
Virtual, 2025
Carnegie Mellon business ethics professor Derek Leben joins Kevin Werbach to trace how AI ethics evolved from an early focus on embodied systems—industrial robots, drones, self-driving cars—to today’s post-ChatGPT landscape that demands concrete, defensible recommendations for companies. Leben explains why fairness is now central: firms must decide which features are relevant to a task (e.g., lending or hiring) and reject those that are irrelevant—even if they’re predictive. Drawing on philosophers such as John Rawls and Michael Sandel, he argues for objective judgments about a system’s purpose and qualifications. Getting practical about testing for AI fairness, he distinguishes blunt outcome checks from better metrics, and highlights counterfactual tools that reveal whether a feature actually drives decisions. With regulations uncertain, he urges companies to treat ethics as navigation, not mere compliance: Make and explain principled choices (including how you mitigate models), accept that everything you do is controversial, and communicate trade-offs honestly to customers, investors, and regulators. In the end, Leben argues, we all must become ethicists to address the issues AI raises...whether we want to or not.
“AI Fairness” (Talk)
International association for Safe and Ethical AI
Paris, 2025
“AI and Fair distribution in high-stakes decisions”
NWU Buisness school (virtual talk)
Virtual, 2024
Important decisions are increasingly being made with the aid of AI systems. These decisions can determine the allocation of benefits, like jobs, loans, and educational opportunities. They can also determine the allocation of losses, like vehicle navigation systems, risk-assessment for policing and parole, and medical triage. Companies designing these systems therefore have an ethical obligation to ensure that the recommendations and decisions of these systems are fair. But what does it mean for automated decisions to be fair? This talk will discuss some of the difficult decisions that must be made when designing fair AI systems, including: Which features should be used by the system in its decisions? What efforts should be made to correct the effects of historical injustice in the data? How should we balance the trade-offs in designing fair AI systems?
Navigating the Frontier: Creativity, Ethics, and Fairness in the Age of AI
Data Natives, Imagining the Good AI
BERLIN, 2024
Arikia Millikan, Founder at CTRL + X
Bernie Su, A three-time award-winning director
Derek Leben, CMU Professor and Author of Ethics for Robots
Boris Eldagsen, visual artist & Founder promptwhispering AI
“Cooperation, Maximin, and the foundations of ethics”
CMU PHILOSOPHY, PITTSBURGH FORMAL EPISTEMOLOGY WORKSHOP
Pittsburgh 2023
A Social Contract view about meta-ethics proposes that normative principles can be causally and functionally explained as solutions to cooperation problems, and they can therefore be evaluated by how effectively they solve these problems. However, advocates of the Social Contract view have often not specified details about what counts as a cooperation problem, and what solutions to it would look like. I propose that we define cooperation problems as interactions where there exists at least one Strong Pareto-Improvement on every pure Nash Equilibrium (willfully ignoring mixed solutions). We will explore a range of solutions to this problem, and how these solutions correspond to various normative principles. In the past, I have advocated the Maximin principle as an optimal solution to cooperation problems, but this turns out to be incomplete at best, and mistaken at worst.
Technology: Hopes, Fears, Unintended Consequences
Building an Ethical Future Forum, CMU
PITTSBURGH, 2023
Panelists:
Dr. Rayid Ghani, Professor of Computer Science, CMU
Michael Barber, Director of Data Science R&D, Highmark
Derek Leben, Associate Teaching Professor of Business Ethics, CMU
“FAIRNESS IN MACHINE LEARNING”
MILA, MONTREAL SPEAKER SERIES ON AI ETHICS
Montreal, 2020
There are many incompatible ways to measure fair outcomes for machine learning algorithms. In this talk, I characterize rates of success and error across protected groups(race, gender, sexual orientation) as a distribution problem, and describe the possible solutions to this problem according to different normative principles from moral and political philosophy. These normative principles are based on various competing attributes within a distribution problem: intentions, compensation, desert, consent, and consequences. Each principle will be applies to a sample risk assessment classifier to demonstrate the philosophical arguments underlying different sets of fairness metrics.
“Discrimination in Targeted Marketing”
BBC
London, 2019
Using information about group membership to make judgments about individuals is discrimination, and discrimination is wrong. So is the use of demographic information in advertising and recommendation algorithms an instance of morally objectionable discrimination? This talk at the BBC examines what the differences may be between algorithms that use group membership as data in the criminal justice system versus those in targeted marketing.
CYBERSECURITY AND ARTIFICIAL INTELLIGENCE
CENTER FOR ETHICS AND THE RULE OF LAW (CERL)- UNIVERSITY OF PENNSYLVANIA
philadelphia, 2019
Moderator: Professor Claire Finkelstein, CERL Founder & Faculty Director; Algernon Biddle Professor of Law and Professor of Philosophy, University of Pennsylvania
Panelists:
Professor Gary Brown, Professor of Cyber Law at the College of Information and Cyberspace, National Defense University
The Honorable Thomas Ayres, General Counsel, United States Air Force
Professor Derek Leben, Department Chair and Associate Professor of Philosophy at the University of Pittsburgh at Johnstown
LTC Christopher Korpella, Associate Professor and Director of the Robotics Research Center at the United States Military Academy at West Point
Sean Carroll’s "Mindscape,” 2019
Episode 30- Derek Leben on ethics for Robots and AI
Episode Description: “It’s hardly news that computers are exerting ever more influence over our lives. And we’re beginning to see the first glimmers of some kind of artificial intelligence: computer programs have become much better than humans at well-defined jobs like playing chess and Go, and are increasingly called upon for messier tasks, like driving cars. Once we leave the highly constrained sphere of artificial games and enter the real world of human actions, our artificial intelligences are going to have to make choices about the best course of action in unclear circumstances: they will have to learn to be ethical. I talk to Derek Leben about what this might mean and what kind of ethics our computers should be taught. It’s a wide-ranging discussion involving computer science, philosophy, economics, and game theory.”
The Dissenter, 2019
Episode 206: Derek Leben: Ethics for Robots, Contractarianism, Self-Driving Cars
Machine Ethics podcast, 2018
Episode 23- Derek leben
Episode Description: “This month I'm talking with Derek Leben about his new book Ethics for Robots: How to Design a Moral Algorithm. We also dive into a general framework for machine ethics, contractarianism, Rawls’ original position thought experiment (which is one of my favourite ethical thought experiments), maximin function approach to machine ethics, and whether robots should respect the consent of a person in life threatening circumstances...”