Ethics & Policy
What I’m Updating in My AI Ethics Class for 2025
The new technology developments have come in fast, but what has ethical or values implications that are going to matter long-term?
I’ve been working on updates for my 2025 class on Values and Ethics in Artificial Intelligence. This course is part of the Johns Hopkins Education for Professionals program, part of the Master’s degree in Artificial Intelligence.
Overview of my changes:
I’m doing major updates on three topics based on 2024 developments, and a number of small updates, integrating other news and filling gaps in the course.
Topic 1: LLM interpretability.
Anthropic’s work in interpretability was a breakthrough in explainable AI (XAI). We will be discussing how this method can be used in practice, as well as implications for how we think about AI understanding.
Topic 2: Human-Centered AI.
Rapid AI development adds urgency to the question: How do we design AI to empower rather than replace human beings? I have added content throughout my course on this, including two new design exercises.
Topic 3: AI Law and Governance.
Major developments were the EU’s AI Act and the raft of California legislation, including laws targeting deep fakes, misinformation, intellectual property, medical communications and minor’s use of ‘addictive’ social media, among other. For class I developed some heuristics for evaluating AI legislation, such as studying definitions, and explain how legislation is only one piece of the solution to the AI governance puzzle.
Miscellaneous new material:
I am integrating material from news stories into existing topics on copyright, risk, privacy, safety and social media/ smartphone harms.
Topic 1: Generative AI Interpretability
What’s new:
Anthropic’s pathbreaking 2024 work on interpretability was a fascination of mine. They published a blog post here, and there is also a paper, and there was an interactive feature browser. Most tech-savvy readers should be able to get something out of the blog and paper, despite some technical content and a daunting paper title (‘Scaling Monosemanticity’).
Below is a screenshot of one discovered feature, ‘sycophantic praise’. I like this one because of the psychological subtlety; it amazes me that they could separate this abstract concept from simple ‘flattery’, or ‘praise’.
What’s important:
Explainable AI: For my ethics class, this is most relevant to explainable AI (XAI), which is a key ingredient of human-centered design. The question I will pose to the class is, how might this new capability be used to promote human understanding and empowerment when using LLMs? SAEs (sparse autoencoders) are too expensive and hard to train to be a complete solution to XAI problems, but they can add depth to a multi-pronged XAI strategy.
Safety implications: Anthropic’s work on safety is also worth a mention. They identified the ‘syncophantic praise’ feature as part of their work on safety, specifically relevant to this question: could a very powerful AI hide its intentions from humans, possibly by flattering users into complacency? This general direction is especially salient in light of this recent work: Frontier Models are Capable of In-context Scheming. (NB: see also Anthropic’s work on alignment faking. Thanks for the pointer Sean Sica.
Evidence of AI ‘Understanding’? Did interpretability kill the ‘stochastic parrot’? I have been convinced for a while that LLMs must have some internal representations of complex and inter-related concepts. They could not do what they do as one-deep stimulus-response or word-association engines, (‘stochastic parrots’) no matter how many patterns were memorized. The use of complex abstractions, such as those identified by Anthropic, fits my definition of ‘understanding’, although some reserve that term only for human understanding. Perhaps we should just add a qualifier for ‘AI understanding’. This is not a topic that I explicitly cover in my ethics class, but it does come up in discussion of related topics.
SAE visualization needed. I am still looking for a good visual illustration of how complex features across a deep network are mapped onto to a very thin, very wide SAEs with sparsely represented features. What I have now is the Powerpoint approximation I created for class use, below. Props to Brendan Boycroft for his LLM visualizer, which has helped me understand more about the mechanics of LLMs. https://bbycroft.net/llm
Topic 2: Human Centered AI (HCAI)
What’s new?
In 2024 it was increasingly apparent that AI will affect every human endeavor and seems to be doing so at a much faster rate than previous technologies such as steam power or computers. The speed of change matters almost more than the nature of change because human culture, values, and ethics do not usually change quickly. Maladaptive patterns and precedents set now will be increasingly difficult to change later.
What’s important?
Human-Centered AI needs to become more than an academic interest, it needs to become a well-understood and widely practiced set of values, practices and design principles. Some people and organizations that I like, along with the Anthropic explainability work already mentioned, are Stanford’s Human-Centered AI, Google’s People + AI effort, and Ben Schneiderman’s early leadership and community organizing.
For my class of working AI engineers, I am trying to focus on practical and specific design principles. We need to counter the dysfunctional design principles I seem to see everywhere: ‘automate everything as fast as possible’, and ‘hide everything from the users so they can’t mess it up’. I am looking for cases and examples that challenge people to step up and use AI in ways that empower humans to be smarter, wiser and better than ever before.
I wrote fictional cases for class modules on the Future of Work, HCAI and Lethal Autonomous Weapons. Case 1 is about a customer-facing LLM system that tried to do too much too fast and cut the expert humans out of the loop. Case 2 is about a high school teacher who figured out most of her students were cheating on a camp application essay with an LLM and wants to use GenAI in a better way.
The cases are on separate Medium pages [here](https://medium.com/@nathanbos/0ad4caf4787f) and here, and I love feedback! Thanks to Sara Bos and Andrew Taylor for comments already received.
The second case might be controversial; some people argue that it is OK for students to learn to write with AI before learning to write without it. I disagree, but that debate will no doubt continue.
I prefer real-world design cases when possible, but good HCAI cases have been hard to find. My colleague John (Ian) McCulloh recently gave me some great ideas from examples he uses in his class lectures, including the Organ Donation case, an Accenture project that helped doctors and patients make time-sensitive kidney transplant decision quickly and well. Ian teaches in the same program that I do. I hope to work with Ian to turn this into an interactive case for next year.
Topic 3: AI governance
Most people agree that AI development needs to be governed, through laws or by other means, but there’s a lot of disagreement about how.
What’s new?
The EU’s AI Act came into effect, giving a tiered system for AI risk, and prohibiting a list of highest-risk applications including social scoring systems and remote biometric identification. The AI Act joins the EU’s Digital Markets Act and the General Data Protection Regulation, to form the world’s broadest and most comprehensive set of AI-related legislation.
California passed a set of AI governance related laws, which may have national implications, in the same way that California laws on things like the environment have often set precedent. I like this (incomplete) review from the White & Case law firm.
For international comparisons on privacy, I like DLA Piper’s website Data Protection Laws of the World.
What’s Important?
My class will focus on two things:
- How we should evaluate new legislation
- How legislation fits into the larger context of AI governance
How do you evaluate new legislation?
Given the pace of change, the most useful thing I thought I could give my class is a set of heuristics for evaluating new governance structures.
Pay attention to the definitions. Each of the new legal acts faced problems with defining exactly what would be covered; some definitions are probably too narrow (easily bypassed with small changes to the approach), some too broad (inviting abuse) and some may be dated quickly.
California had to solve some difficult definitional problems in order to try to regulate things like ‘Addictive Media’ (see SB-976), ‘AI Generated Media’ (see AB-1836), and to write separate legislation for ‘Generative AI’, (see SB-896). Each of these has some potentially problematic aspects, worthy of class discussion. As one example, The Digital Replicas Act defines AI-generated media as “an engineered or machine-based system that varies in its level of autonomy and that can, for explicit or implicit objectives, infer from the input it receives how to generate outputs that can influence physical or virtual environments.” There’s a lot of room for interpretation here.
Who is covered and what are the penalties? Are the penalties financial or criminal? Are there exceptions for law enforcement or government use? How does it apply across international lines? Does it have a tiered system based on an organization’s size? On the last point, technology regulation often tries to protect startups and small companies with thresholds or tiers for compliance. But California’s governor vetoed SB 1047 on AI safety for exempting small companies, arguing that “Smaller, specialized models may emerge as equally or even more dangerous”. Was this a wise move, or was he just protecting California’s tech giants?
Is it enforceable, flexible, and ‘future-proof’? Technology legislation is very difficult to get right because technology is a fast-moving target. If it is too specific it risks quickly becoming obsolete, or worse, hindering innovations. But the more general or vague it is, the less enforceable it may be, or more easily ‘gamed’. One strategy is to require companies to define their own risks and solutions, which provides flexibility, but will only work if the legislature, the courts and the public later pay attention to what companies actually do. This is a gamble on a well-functioning judiciary and an engaged, empowered citizenry… but democracy always is.
How does legislation fit into the bigger picture of AI governance?
Not every problem can or should be solved with legislation. AI governance is a multi-tiered system. It includes the proliferation of AI frameworks and independent AI guidance documents that go further than legislation should, and provide non-binding, sometimes idealistic goals. A few that I think are important:
Other miscellaneous topics
Here’s some other news items and topics I am integrating into my class, some of which are new to 2024 and some are not. I will:
Thanks for reading! I always appreciate making contact with other people teaching similar courses or with deep knowledge of related areas. And I also always appreciate Claps and Comments!
Ethics & Policy
AI and ethics – what is originality? Maybe we’re just not that special when it comes to creativity?
I don’t trust AI, but I use it all the time.
Let’s face it, that’s a sentiment that many of us can buy into if we’re honest about it. It comes from Paul Mallaghan, Head of Creative Strategy at We Are Tilt, a creative transformation content and campaign agency whose clients include the likes of Diageo, KPMG and Barclays.
Taking part in a panel debate on AI ethics at the recent Evolve conference in Brighton, UK, he made another highly pertinent point when he said of people in general:
We know that we are quite susceptible to confident bullshitters. Basically, that is what Chat GPT [is] right now. There’s something reminds me of the illusory truth effect, where if you hear something a few times, or you say it here it said confidently, then you are much more likely to believe it, regardless of the source. I might refer to a certain President who uses that technique fairly regularly, but I think we’re so susceptible to that that we are quite vulnerable.
And, yes, it’s you he’s talking about:
I mean all of us, no matter how intelligent we think we are or how smart over the machines we think we are. When I think about trust, – and I’m coming at this very much from the perspective of someone who runs a creative agency – we’re not involved in building a Large Language Model (LLM); we’re involved in using it, understanding it, and thinking about what the implications if we get this wrong. What does it mean to be creative in the world of LLMs?
Genuine
Being genuine, is vital, he argues, and being human – where does Human Intelligence come into the picture, particularly in relation to creativity. His argument:
There’s a certain parasitic quality to what’s being created. We make films, we’re designers, we’re creators, we’re all those sort of things in the company that I run. We have had to just face the fact that we’re using tools that have hoovered up the work of others and then regenerate it and spit it out. There is an ethical dilemma that we face every day when we use those tools.
His firm has come to the conclusion that it has to be responsible for imposing its own guidelines here to some degree, because there’s not a lot happening elsewhere:
To some extent, we are always ahead of regulation, because the nature of being creative is that you’re always going to be experimenting and trying things, and you want to see what the next big thing is. It’s actually very exciting. So that’s all cool, but we’ve realized that if we want to try and do this ethically, we have to establish some of our own ground rules, even if they’re really basic. Like, let’s try and not prompt with the name of an illustrator that we know, because that’s stealing their intellectual property, or the labor of their creative brains.
I’m not a regulatory expert by any means, but I can say that a lot of the clients we work with, to be fair to them, are also trying to get ahead of where I think we are probably at government level, and they’re creating their own frameworks, their own trust frameworks, to try and address some of these things. Everyone is starting to ask questions, and you don’t want to be the person that’s accidentally created a system where everything is then suable because of what you’ve made or what you’ve generated.
Originality
That’s not necessarily an easy ask, of course. What, for example, do we mean by originality? Mallaghan suggests:
Anyone who’s ever tried to create anything knows you’re trying to break patterns. You’re trying to find or re-mix or mash up something that hasn’t happened before. To some extent, that is a good thing that really we’re talking about pattern matching tools. So generally speaking, it’s used in every part of the creative process now. Most agencies, certainly the big ones, certainly anyone that’s working on a lot of marketing stuff, they’re using it to try and drive efficiencies and get incredible margins. They’re going to be on the race to the bottom.
But originality is hard to quantify. I think that actually it doesn’t happen as much as people think anyway, that originality. When you look at ChatGPT or any of these tools, there’s a lot of interesting new tools that are out there that purport to help you in the quest to come up with ideas, and they can be useful. Quite often, we’ll use them to sift out the crappy ideas, because if ChatGPT or an AI tool can come up with it, it’s probably something that’s happened before, something you probably don’t want to use.
More Human Intelligence is needed, it seems:
What I think any creative needs to understand now is you’re going to have to be extremely interesting, and you’re going to have to push even more humanity into what you do, or you’re going to be easily replaced by these tools that probably shouldn’t be doing all the fun stuff that we want to do. [In terms of ethical questions] there’s a bunch, including the copyright thing, but there’s partly just [questions] around purpose and fun. Like, why do we even do this stuff? Why do we do it? There’s a whole industry that exists for people with wonderful brains, and there’s lots of different types of industries [where you] see different types of brains. But why are we trying to do away with something that allows people to get up in the morning and have a reason to live? That is a big question.
My second ethical thing is, what do we do with the next generation who don’t learn craft and quality, and they don’t go through the same hurdles? They may find ways to use {AI] in ways that we can’t imagine, because that’s what young people do, and I have faith in that. But I also think, how are you going to learn the language that helps you interface with, say, a video model, and know what a camera does, and how to ask for the right things, how to tell a story, and what’s right? All that is an ethical issue, like we might be taking that away from an entire generation.
And there’s one last ‘tough love’ question to be posed:
What if we’re not special? Basically, what if all the patterns that are part of us aren’t that special? The only reason I bring that up is that I think that in every career, you associate your identity with what you do. Maybe we shouldn’t, maybe that’s a bad thing, but I know that creatives really associate with what they do. Their identity is tied up in what it is that they actually do, whether they’re an illustrator or whatever. It is a proper existential crisis to look at it and go, ‘Oh, the thing that I thought was special can be regurgitated pretty easily’…It’s a terrifying thing to stare into the Gorgon and look back at it and think,’Where are we going with this?’. By the way, I do think we’re special, but maybe we’re not as special as we think we are. A lot of these patterns can be matched.
My take
This was a candid worldview that raised a number of tough questions – and questions are often so much more interesting than answers, aren’t they? The subject of creativity and copyright has been handled at length on diginomica by Chris Middleton and I think Mallaghan’s comments pretty much chime with most of that.
I was particularly taken by the point about the impact on the younger generation of having at their fingertips AI tools that can ‘do everything, until they can’t’. I recall being horrified a good few years ago when doing a shift in a newsroom of a major tech title and noticing that the flow of copy had suddenly dried up. ‘Where are the stories?’, I shouted. Back came the reply, ‘Oh, the Internet’s gone down’. ‘Then pick up the phone and call people, find some stories,’ I snapped. A sad, baffled young face looked back at me and asked, ‘Who should we call?’. Now apart from suddenly feeling about 103, I was shaken by the fact that as soon as the umbilical cord of the Internet was cut, everyone was rendered helpless.
Take that idea and multiply it a billion-fold when it comes to AI dependency and the future looks scary. Human Intelligence matters
Publishing stands at a pivotal juncture, said Jeremy North, president of Global Book Business at Taylor & Francis Group, addressing delegates at the 3rd International Conference on Publishing Education in Beijing. Digital intelligence is fundamentally transforming the sector — and this revolution will inevitably create “AI winners and losers”.
True winners, he argued, will be those who embrace AI not as a replacement for human insight but as a tool that strengthens publishing’s core mission: connecting people through knowledge. The key is balance, North said, using AI to enhance creativity without diminishing human judgment or critical thinking.
This vision set the tone for the event where the Association for International Publishing Education was officially launched — the world’s first global alliance dedicated to advancing publishing education through international collaboration.
Unveiled at the conference cohosted by the Beijing Institute of Graphic Communication and the Publishers Association of China, the AIPE brings together nearly 50 member organizations with a mission to foster joint research, training, and innovation in publishing education.
Tian Zhongli, president of BIGC, stressed the need to anchor publishing education in ethics and humanistic values and reaffirmed BIGC’s commitment to building a global talent platform through AIPE.
BIGC will deepen academic-industry collaboration through AIPE to provide a premium platform for nurturing high-level, holistic, and internationally competent publishing talent, he added.
Zhang Xin, secretary of the CPC Committee at BIGC, emphasized that AIPE is expected to help globalize Chinese publishing scholarships, contribute new ideas to the industry, and cultivate a new generation of publishing professionals for the digital era.
Themed “Mutual Learning and Cooperation: New Ecology of International Publishing Education in the Digital Intelligence Era”, the conference also tackled a wide range of challenges and opportunities brought on by AI — from ethical concerns and content ownership to protecting human creativity and rethinking publishing values in higher education.
Wu Shulin, president of the Publishers Association of China, cautioned that while AI brings major opportunities, “we must not overlook the ethical and security problems it introduces”.
Catriona Stevenson, deputy CEO of the UK Publishers Association, echoed this sentiment. She highlighted how British publishers are adopting AI to amplify human creativity and productivity, while calling for global cooperation to protect intellectual property and combat AI tool infringement.
The conference aims to explore innovative pathways for the publishing industry and education reform, discuss emerging technological trends, advance higher education philosophies and talent development models, promote global academic exchange and collaboration, and empower knowledge production and dissemination through publishing education in the digital intelligence era.
yangyangs@chinadaily.com.cn
Publishing stands at a pivotal juncture, said Jeremy North, president of Global Book Business at Taylor & Francis Group, addressing delegates at the 3rd International Conference on Publishing Education in Beijing. Digital intelligence is fundamentally transforming the sector — and this revolution will inevitably create “AI winners and losers”.
True winners, he argued, will be those who embrace AI not as a replacement for human insight but as a tool that strengthens publishing”s core mission: connecting people through knowledge. The key is balance, North said, using AI to enhance creativity without diminishing human judgment or critical thinking.
This vision set the tone for the event where the Association for International Publishing Education was officially launched — the world’s first global alliance dedicated to advancing publishing education through international collaboration.
Unveiled at the conference cohosted by the Beijing Institute of Graphic Communication and the Publishers Association of China, the AIPE brings together nearly 50 member organizations with a mission to foster joint research, training, and innovation in publishing education.
Tian Zhongli, president of BIGC, stressed the need to anchor publishing education in ethics and humanistic values and reaffirmed BIGC’s commitment to building a global talent platform through AIPE.
BIGC will deepen academic-industry collaboration through AIPE to provide a premium platform for nurturing high-level, holistic, and internationally competent publishing talent, he added.
Zhang Xin, secretary of the CPC Committee at BIGC, emphasized that AIPE is expected to help globalize Chinese publishing scholarships, contribute new ideas to the industry, and cultivate a new generation of publishing professionals for the digital era.
Themed “Mutual Learning and Cooperation: New Ecology of International Publishing Education in the Digital Intelligence Era”, the conference also tackled a wide range of challenges and opportunities brought on by AI — from ethical concerns and content ownership to protecting human creativity and rethinking publishing values in higher education.
Wu Shulin, president of the Publishers Association of China, cautioned that while AI brings major opportunities, “we must not overlook the ethical and security problems it introduces”.
Catriona Stevenson, deputy CEO of the UK Publishers Association, echoed this sentiment. She highlighted how British publishers are adopting AI to amplify human creativity and productivity, while calling for global cooperation to protect intellectual property and combat AI tool infringement.
The conference aims to explore innovative pathways for the publishing industry and education reform, discuss emerging technological trends, advance higher education philosophies and talent development models, promote global academic exchange and collaboration, and empower knowledge production and dissemination through publishing education in the digital intelligence era.
yangyangs@chinadaily.com.cn
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