Ethical Machines

Every specialist in anything thinks they should have a seat at the AI ethics table. I’m usually skeptical. But psychologist Madeline Reinecke, Ph.D. did a great job defending her view that – you guessed it – psychologists should have a seat at the AI ethics table. 

Our conversation ranged from the role of psychologists in creating AI that supports healthy human relationships to when children start and stop attributing sentience to robots to loving relationships with AI to the threat of AI-induced self-absorption. I guess I need to have more psychologists on the show.

A fun format for this episode. In Part I, I talk about how I see agentic AI unfolding and what ethical, social, and political risks come with it. In part II, Eric Corriel, digital strategist at the School of Visual Arts and a close friend, tells me why he thinks I’m wrong. Debate ensues.

Jaahred Thomas is a VC friend of mine who wanted to talk about the evolving landscape of AI ethics in startups and business generally. So rather than have a normal conversation like people do, we made it an episode! Jaahred asks me a bunch of questions about AI ethics and startups, investors, Fortune 500 companies, and more, and I tell him the unvarnished truths about where corporate America is in the AI ethics journey and what startup founders should and shouldn’t spend their time doing.

From the best of season 1: The hospital faced an ethical question: should we deploy robots to help with elder care?

Let’s look at a standard list of AI ethics values: justice/fairness, privacy, transparency, accountability, explainability.

But as Ami points out in our conversation, that standard list doesn’t include a core value at the hospital: the value of caring.

And that’s one example of one of three objections to a view he calls “Principalism.” Principalism is the view that we do AI ethics best by first defining our AI ethics values or principles at that very abstract level. This objection is that the list will always be incomplete.

Given Ami’s expertise in ethics and experience as a clinical ethicist, it was insightful to see how he gets ethics done on the ground and his views on how organizations should approach ethics more generally.

From the best of season 1: Innovation is great…but hype is bad. Not only has all this talk of innovation not increased innovation, but it also creates a bad environment in which leaders can make reasoned judgments about where to devote resources. So says Lee Vinsel, professor in the Department of Science, Technology and Society at Virgina Tech, in this Ethical Machines episode.

ALSO! We want proactive regulations before the sh!t hits the fan, right? Not so fast, says Lee. Proactive regulations presuppose we’re good at predicting how technologies will be applied, and we have a terrible track record on that front. Perhaps reactive regs are appropriate (and we need to focus on making a more agile government).

Super interesting conversation that will push you to think differently about innovation and what appropriate regulation looks like.

“Sustainability,” “purpose/mission/value driven”, “human-centric design.” These are terms companies use so they don’t have to say “ethics.” My contention is that this is bad for business and bad for society at large. Our world, corporate and otherwise, is confronted with a growing mountain of ethical problems, spurred on by technologies that bring us fresh new ways of realizing our familiar ethical nightmares. These issues do not disappear via semantic legerdemain. We need to name our problems accurately if we are to address them effectively.

Democracy is about how we ought to distribute power in society and, more specifically, it’s the claim that people ought to have a significant say in how they are ruled. So if we’re talking about AI’s impact on democracy, we should focus on how our use of AI interact with our concern that democracy be respected, upheld, and improved.  

But my guest Ted Lechterman, UNESCO Chair in AI Ethics and Governance at IE University’s School of Humanities, argues that our current discussion on AI and democracy is far too narrow. We need to widen our aperature if we’re going to discuss and ultimately address the issues that really matter.

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.