Ethical Machines

Giving AI systems autonomy in a military context seems like a bad idea. Of course AI shouldn’t “decide” which targets should be killed and/or blown up. Except…maybe it’s not so obvious after all. That’s what my guest, Michael Horowitz, formerly of the DOD and now a professor at the University of Pennsylvania argues. Agree with him or not, he makes a compelling case we need to take seriously. In fact, you may even conclude with him that using autonomous AI in a military context can be morally superior to having a human pull the trigger.

In August, I recorded a discussion with David Ryan Polgar, Founder of the nonprofit All Tech Is Human, in front of an audience of around 200 people. We talked about how AI mediated experiences make us feel sadder, that the tech companies don’t really care about this, and how people can organize to push those companies to take our long term well being more seriously.

Wendell Wallach, who has been in the AI ethics game longer than just about anyone and has several books to his name on the subject, talks about his dissatisfaction with talk of “value alignment,” why traditional moral theories are not helpful for doing AI ethics, and how we can do better.

It would be crazy to attribute legal personhood to AI, right? But then again, corporations are regarded as legal persons and there seems to be good reason for doing so. In fact, some rivers are classified as legal persons. My guest, David Gunkel, author of many books including “Person Thing Robot” argues that the classic legal distinction between ‘person’ and ‘thing’ doesn’t apply well to AI. How should we regard AI in a way that allows us to create it in a legally responsible way? All that and more in today’s episode

LLMs behave in unpredictable ways. That’s a gift and a curse. It both allows for its “creativity” and makes it hard to control (a bit like a real artist, actually). In this episode, we focus on the cyber risks of AI with Walter Haydock, a former national security policy advisor and the Founder of StackAware.

AI can stand between you and getting a job. That means for you to make money and support yourself and your family, you may have to convince an AI that you’re the right person for the job. And yet, AI can be biased and fail in all sorts of ways. This is a conversation with Hilke Schellmann, investigative journalist and author of ‘The algorithm” along with her colleague Mona Sloane, Ph.D., is an Assistant Professor of Data Science and Media Studies at the University of Virginia. We discuss Hilke’s book and all the ways things go sideways when people are looking for work in the AI era. Originally aired in season one.

We want accurate AI, right? As long as it’s accurate, we’re all good? My guest, Will Landecker, CEO Accountable Algorithm, explains why accuracy is just one metric among many to aim for. In fact, we have to make tradeoffs across things like accuracy, relevance, and normative (including ethical) considerations in order to get a usable model. We also cover whether explainability is important and whether it’s even on the menu and the risks of multi-agentic AI systems.

Should we allow autonomous AI systems? Who is accountable if things go sideways? And how is AI going to transform the future of military work? All this and more with my guest, Rosaria Taddeo, Professor of Digital Ethics and Defense Technologies at the University of Oxford.

The Silicon Valley titans talk a lot about intelligence and super intelligence of AI…but what is intelligence, anyway? My guest, former philosopher professor and now Director at Gartner Philip Walsh argues the SV folks are fundamentally confused about what intelligence is. It’s not, he argues, like horsepower, which can be objectively measured. Instead, whether we ascribe intelligence to something is a matter of what we communally agree to ascribe intelligence to. More specifically, we have to collectively agree on the criteria for intelligence and that’s when it makes sense to say “yeah this thing is intelligent.” But we don’t really have a settled collective agreement and that’s why we sort of want to say “this is not intelligence” at the same time we say, “How is this thing so smart?!” I think this is a crucial discussion for anyone who wants to think deeply about what to make of our new quasi/proto/faux intelligent companions.

How can we solve AI’s problems if we don’t understand where they came from? Originally aired in season one.