Philosophical Disquisitions

In this episode, Brenda Leong, Senior Counsel and Director of Artificial Intelligence and Ethics at Future of Privacy Forum, discusses the complexities of facial recognition technology. Topics include its history, uses, benefits, privacy risks, and whether it should be regulated or banned.

Nikita Aggarwal discusses algorithmic credit scoring, the problems of 'too big to fail' tech platforms, and AI crime. They explore the digitization and datafication of consumer credit markets, the risks and biases in credit scoring, and how law and regulation can address these issues. They also discuss the implications of big tech platforms like Facebook and the inadequacy of the existing legislative framework for data control.

Lots of algorithmic tools are now used to support decision-making in the criminal justice system. Many of them are criticised for being biased. What should be done about this? In this episode, I talk to Chelsea Barabas about this very question. Chelsea is a PhD candidate at MIT, where she examines the spread of algorithmic decision making tools in the US criminal legal system. She works with interdisciplinary researchers, government officials and community organizers to unpack and transform mainstream narratives around criminal justice reform and data-driven decision making. She is currently a Technology Fellow at the Carr Center for Human Rights Policy at the Harvard Kennedy School of Government. Formerly, she was a research scientist for the AI Ethics and Governance Initiative at the MIT Media Lab. You can download the episode here or listen below. You can also subscribe on Apple Podcasts, Stitcher, Spotify and other podcasting services (the RSS feed is here). Show notes Topics covered in this show include The history of algorithmic decision-making in criminal justiceModern AI tools in criminal justiceThe problem of biased decision-makingExamples of bias in practiceThe FAT (Fairness, Accountability and Transparency) approach to biasCan we de-bias algorithms using formal, technical rules?Can we de-bias algorithms through proper review and oversight?Should we be more critical of the data used to build these systems?Problems with pre-trial risk assessment measuresThe abolitionist perspective on criminal justice reform Relevant Links Chelsea's homepageChelsea on Twitter"Beyond Bias: Reimagining the terms "Ethical AI" in Criminal Law" by ChelseaVideo presentation of this paper"Studying up: reorienting the study of algorithmic fairness around issues of power." by Chelsea and orsKleinberg et al on the impossibility of fairnessKleinberg et al on using algorithms to detect discriminationThe Condemnation of Blackness by Khalil Gibran Muhammad Subscribe to the newsletter

 What happens if an autonomous machine does something wrong? Who, if anyone, should be held responsible for the machine's actions? That's the topic I discuss in this episode with Daniel Tigard. Daniel Tigard is a Senior Research Associate in the Institute for History & Ethics of Medicine, at the Technical University of Munich. His current work addresses issues of moral responsibility in emerging technology. He is the author of several papers on moral distress and responsibility in medical ethics as well as, more recently, papers on moral responsibility and autonomous systems. You can download the episode here or listen below. You can also subscribe on Apple Podcasts, Stitcher, Spotify and other podcasting services (the RSS feed is here).          Show Notes Topics discussed include:  What is responsibility? Why is it so complex? The three faces of responsibility: attribution, accountability and answerability Why are people so worried about responsibility gaps for autonomous systems? What are some of the alleged solutions to the "gap" problem? Who are the techno-pessimists and who are the techno-optimists? Why does Daniel think that there is no techno-responsibility gap? Is our application of responsibility concepts to machines overly metaphorical?   Relevant Links Daniel's ResearchGATE profile Daniel's papers on Philpapers "There is no Techno-Responsibility Gap" by Daniel "Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability" by Mark Coeckelbergh Technologically blurred accountability? by Kohler, Roughley and Sauer Subscribe to the newsletter

   Are robots like humans? Are they agents? Can we have relationships with them? These are just some of the questions I explore with today's guest, Sven Nyholm. Sven is an assistant professor of philosophy at Utrecht University in the Netherlands. His research focuses on ethics, particularly the ethics of technology. He is a friend of the show, having appeared twice before. In this episode, we are talking about his recent, great, book Humans and Robots: Ethics, Agency and Anthropomorphism. You can download the episode here or listen below. You can also subscribe on Apple Podcasts, Stitcher, Spotify and other podcasting services (the RSS feed is here). Show Notes: Topics covered in this episode include: Why did Sven play football with a robot? Who won? What is a robot? What is an agent? Why does it matter if robots are agents? Why does Sven worry about a normative mismatch between humans and robots? What should we do about this normative mismatch? Why are people worried about responsibility gaps arising as a result of the widespread deployment of robots? How should we think about human-robot collaborations? Why should human drivers be more like self-driving cars? Can we be friends with a robot? Why does Sven reject my theory of ethical behaviourism? Should we be pessimistic about the future of roboethics? Relevant Links Sven's Homepage Sven on Philpapers Humans and Robots: Ethics, Agency and Anthropomorphism 'Can a robot be a good colleague?' by Sven and Jilles Smids 'Attributing Agency to Automated Systems: Reflections on Human–Robot Collaborations and Responsibility-Loci' by Sven 'Automated Cars Meet Human Drivers: Responsible Human-Robot Coordination and The Ethics of Mixed Traffic' by Sven and Jilles Smids   Subscribe to the newsletter

If an AI system makes a decision, should its reasons for making that decision be explainable to you? In this episode, I chat to Scott Robbins about this issue. Scott is currently completing his PhD in the ethics of artificial intelligence at the Technical University of Delft. He has a B.Sc. in Computer Science from California State University, Chico and an M.Sc. in Ethics of Technology from the University of Twente. He is a founding member of the Foundation for Responsible Robotics and a member of the 4TU Centre for Ethics and Technology. Scott is skeptical of AI as a grand solution to societal problems and argues that AI should be boring. You can download the episode here or listen below. You can also subscribe on Apple Podcasts, Stitcher, Spotify and other podcasting services (the RSS feed is here).  Show Notes Topic covered include: Why do people worry about the opacity of AI? What's the difference between explainability and transparency? What's the moral value or function of explainable AI? Must we distinguish between the ethical value of an explanation and its epistemic value? Why is it so technically difficult to make AI explainable? Will we ever have a technical solution to the explanation problem? Why does Scott think there is Catch 22 involved in insisting on explainable AI? When should we insist on explanations and when are they unnecessary? Should we insist on using boring AI?   Relevant Links Scotts's webpage Scott's paper "A Misdirected Principle with a Catch: Explicability for AI" Scott's paper "The Value of Transparency: Bulk Data and Authorisation" "The Right to an Explanation Explained" by Margot Kaminski Episode 36 - Wachter on Algorithms and Explanations   Subscribe to the newsletter

Carissa Véliz, Research Fellow at the Uehiro Centre for Practical Ethics at Oxford, discusses the ethical questions raised by increased surveillance and tracking during the COVID-19 pandemic. Topics include the value of privacy, consent, digital contact tracing, the data economy, immunity passports, and the importance of focusing on the right things in responding to COVID-19.

There is a lot of data and reporting out there about the COVID 19 pandemic. How should we make sense of that data? Do the media narratives misrepresent or mislead us as to the true risks associated with the disease? Have governments mishandled the response? Can they be morally blamed for what they have done. These are the questions I discuss with my guest on today's show: David Shaw. David is a Senior Researcher at the Institute for Biomedical Ethics at the University of Basel and an Assistant Professor at the Care and Public Health Research Institute, Maastricht University. We discuss some recent writing David has been doing on the Journal of Medical Ethics blog about the coronavirus crisis. You can download the episode here or listen below. You can also subscribe to the podcast on Apple, Stitcher and a range of other podcasting services (the RSS feed is here).  Show Notes Topics discussed include... Why is it important to keep death rates and other data in context? Is media reporting of deaths misleading? Why do the media discuss 'soaring' death rates and 'grim' statistics? Are we ignoring the unintended health consequences of COVID 19? Should we take the economic costs more seriously given the link between poverty/inequality and health outcomes? Did the UK government mishandle the response to the crisis? Are they blameworthy for what they did? Is it fair to criticise governments for their handling of the crisis? Is it okay for governments to experiment on their populations in response to the crisis? Relevant Links David's Profile Page at the University of Basel 'The Vital Contexts of Coronavirus' by David 'The Slow Dragon and the Dim Sloth: What can the world learn from coronavirus responses in Italy and the UK?' by Marcello Ienca and David Shaw 'Don't let the ethics of despair infect the ICU' by David Shaw, Dan Harvey and Dale Gardiner 'Deaths in New York City Are More Than Double the Usual Total' in the NYT (getting the context right?!) Preliminary results from German Antibody tests in one town: 14% of the population infected Do Death Rates Go Down in a Recession? The Sun's Good Friday headline Subscribe to the newsletter

I'm still thinking a lot about the COVID-19 pandemic. In this episode I turn away from some of the 'classical' ethical questions about the disease and talk more about how to understand it and form reasonable beliefs about the public health information that has been issued in response to it. To help me do this I will be talking to Katherine Furman. Katherine is a lecturer in philosophy at the University of Liverpool. Her research interests are at the intersection of Philosophy and Health Policy. She is interested in how laypeople understand issues of science, objectivity in the sciences and social sciences, and public trust in science. Her previous work has focused on the HIV/AIDs pandemic and the Ebola outbreak in West Africa in 2014-2015. We will be talking about the lessons we can draw from this work for how we think about the COVID-19 pandemic. You can download the episode here or listen below. You can also subscribe to the podcast on Apple, Stitcher and a range of other podcasting services (the RSS feed is here). Show Notes Topics discussed include: The history of explaining the causes of disease Mono-causal theories of disease Multi-causal theories of disease Lessons learned from the HIV/AIDs pandemic The practical importance of understanding the causes of disease in the current pandemic Is there an ethics of belief? Do we have epistemic duties in relation to COVID-19? Is it reasonable to believe 'rumours' about the disease? Lessons learned from the 2014-2015 Ebola outbreak The importance of values in the public understanding of science Relevant Links Katherine's Homepage Katherine @ University of Liverpool "Mono-Causal and Multi-Causal Theories of Disease: How to Think Virally and Socially about the Aetiology of AIDS" by Katherine "Moral Responsibility, Culpable Ignorance, and Suppressed Disagreement" by Katherine "The international response to the Ebola outbreak has excluded Africans and their interests" by Katherine Imperial College paper on COVID-19 scenarios Oxford Paper on possible exposure levels to novel Coronavirus   Subscribe to the newsletter

We have a limited number of ventilators. Who should get access to them? In this episode I talk to Lars Sandman. Lars is a Professor of Healthcare Ethics at Linköping University, Sweden. Lars’s research involves studying ethical aspects of distributing scarce resources within health care and studying and developing methods for ethical analyses of health-care procedures. We discuss the ethics of healthcare prioritisation in the midst of the COVID 19 pandemic, focusing specifically on some principles Lars, along with others, developed for the Swedish government. You download the episode here or listen below. You can also subscribe to the podcast on Apple, Stitcher and a range of other podcasting services (the RSS feed is here). Show Notes The prioritisation challenges we currently face Ethical principles for prioritisation in healthcare Problems with applying ethical theories in practice Swedish legal principles on healthcare prioritisation Principles for access to ICU during the COVID 19 pandemic Do we prioritise younger people? Chronological age versus biological age Could we use a lottery principle? Should we prioritise healthcare workers? Impact of COVID 19 prioritisation on other healthcare priorities   Relevant Links Lar's Webpage Swedish Legal Principles Background to the Swedish Law New priority principles in Sweden (English Translation by Christian Munthe) "Principles for allocation of scarce medical interventions" by Persad, Werthheimer and Emanuel (good overview of the ethical debate) The grim ethical dilemma of rationing medical care, explained - Vox.com Subscribe to the newsletter