Ethical Machines

From the best of season 1.

Well, I didn’t see this coming. Talking about legal and philosophical conceptions of copyright turns out to be intellectually fascinating and challenging. It involves not only concepts about property and theft, but also about personhood and invasiveness. Could it be that training AI with author/artist work violates their self?

I talked with Darren Hick about all this, who wrote a few books on the topic. I definitely didn’t think he was going to bring up Hegel.

My guest and I have been doing AI governance for businesses for a combined 17+years. We started way before genAI was a big thing. But I’d say I’m more a qualitative guy and he’s more quant. Nick Elprin is the CEO of an AI governance software company, after all. How has AI ethics or AI governance evolved over that time and what does cutting edge governance look like? Perhaps you’re about to find out…

People, especially kids under 18, are forming emotional attachments with AI chatbots. At a minimum, this is…weird. Is it also unethical? Does it harm users? Is it, as my guest Robert Mahari argues, an affront to human dignity? Have a listen and find out.

You might want more online content moderation so insane conspiracy theories don’t flourish. Sex slaves in Democrat pizza shops, climate change is a hoax, and so on. But is it irrational to believe these things? Is content moderation - whether in the form of censoring or labelling something as false - the morally right and/or effective strategy? In this discussion Neil Levy and I go back to basics about what it is to be rational and how that helps us answer our questions. Neil’s fascinating answer in a nutshell: they’re not irrational and content moderation isn’t a good strategy. This is, I have to say, great stuff. Enjoy!

From the best of season 1. Part 2 of my conversation with Alex.

There’s good reason to think AI doesn’t understand anything. It’s just moving around words according to mathematical rules, predicting the words that come next. But in this episode, philosopher Alex Grzankowski argues that AI may not understand what it’s saying but it does understand language. In this episode we do a deep dive into the nature of human and AI understanding, ending with strategies for how AI researchers could pursue AI that has genuine understanding of the world.

From the best of season 1. Part 1 of my conversation with Alex Grzankowski.

It looks like ChatGPT understands what you’re asking. It looks like ChatGPT understands what it’s saying in reply. 

But that’s not the case.

Alex and I discuss what understanding is, for both people and machines and what it would take for a machine to understand what it’s saying.

One person driving one car creates a negligible amount of pollution. The problem arises when we have lots of people driving cars. Might this kind of issue arise with AI use as well? What if everyone uses the same hiring or lending or diagnostic algorithm? My guest, Kathleen Creel, argues that this is bad for society and bad for the companies using these algorithms. The solution, in broad strokes, is to introduce randomness into the AI system. But is this a good idea? If so, do we need regulation to pull it off? This and more on today’s episode.

With so many laws and so much case law, it’s virtually impossible for the layperson to know what’s legal and illegal. But what if AI can synthesize all that information and deliver clear legal guidance to the average person? Is such a thing possible? Is it desirable?

Author of the new book “Tech Agnostic: How Technology Became the World's Most Powerful Religion, and Why It Desperately Needs a Reformation” discusses, well, what do you think? It’s right there in the title. Go have a listen.

From the best of season 1: You might think it's outrageous that companies collect data about you and use it in various ways to drive profits. The business model of the "attention" economy is often objected to on just these grounds.

On the other hand, does it really matter if data about you is collected and no person ever looks at that data? Is that really an invasion of your privacy?

Carissa and I discuss all this and more. I push the skeptical line, trying on the position that it doesn't really matter all that much. Carissa has powerful arguments against me.

This conversation goes way deeper than 'privacy good/data collection bad' statements we see all the time. I hope you enjoy!

We use the wrong metaphor for thinking about AI, Shannon Vallor argues, and bad thinking leads to bad results. We need to stop thinking about AI as being an agent or having a mind, and stop thinking of the human mind/brain as a kind of software/hardware configuration. All of this is misguided. Instead, we should think of AI as a mirror, reflecting our images in a sometimes helpful, sometimes distorted way. Our shifting to this new metaphor, she says, will lead us to better, and ethically better, AI.

Canada Air blamed the LLM chatbot for giving false information about their bereavement fare policy. They lost the law suit because of course it’s not the chatbot’s fault. But what would it take to hold chatbots responsible for what they say? That’s the topic of discussion with my guest, philosopher Emma Borg.

California just signed a bill to drastically decrease deepfakes on social media. The worry, of course, is that they are already being used to unjustifiably sway voters. In this episode, one of the best from Season 1, I talk to Dean Jackson and Jon Bateman, experts on the role of deepfakes in disinformation campaigns. The bottom line? Deepfakes aren’t great but they’re not half the problem.

What does it look like to integrate ethics into the teams that are building AI? How can we make ethics a practice and not a compliance checklist? In today’s episode I talk with Marc Steen, author of the book “Ethics for People Who Work in Tech,” who answers these questions and more.

Doesn’t the title say it all? This is for anyone who wants the very basics on what AI is, why it’s not intelligent, and why it doesn’t pose an existential threat to humanity. If you don’t know anything at all about AI and/or the nature of the mind/intelligence, don’t worry: we’re starting on the ground floor.

Are we dependent on social media in a way that erodes our autonomy? After all, platforms are designed to keep us hooked and to come back for more. And we don’t really know the law of the digital lands, since how the algorithms influence how we relate to each other online in unknown ways. Then again, don’t we bear a certain degree of personal responsibility for how we conduct ourselves, online or otherwise? What the right balance is and how we can encourage or require greater autonomy is our topic of discussion today.

From the best of season 1: I talk a lot about bias, black boxes, and privacy, but perhaps my focus is too narrow. In this conversation, Aimee and I discuss what she calls “sustainable AI.” We focus on the environmental impacts of AI, the ethical impacts of those environmental impacts, and who is paying the social cost of those who benefit from AI.

Is our collective approach to ensuring AI doesn’t go off the rails fundamentally misguided? Is our approach too narrow to get the job done? My guest, John Basl argues exactly that. We need to broaden our perspective, he says, and prioritize what he calls an “AI ethics ecosystem.” It’s a big lift, but without it it’s an even bigger problem.

Many researchers in AI think we should make AI capable of ethical inquiry. We can’t teach it all the ethical rules; that’s impossible. Instead, we should teach it to ethically reason, just as we do children. But my guest thinks this strategy makes a number of controversial assumptions, including how ethics works and what actually is right and wrong.

It’s common to hear we need new regulations to avoid the risks of AI (bias, privacy violations, manipulation, etc.). But my guest, Dean Ball, thinks this claim is too hastily made. In fact, he argues, we don’t need a new regulatory regime tailored to AI. If he’s right, then in a way that’s good news, since regulations are so notoriously difficult to push through. But he emphasizes we still need a robust governance response to the risks at hand. What are those responses? Have a listen and find out!