Ethics of AI: Certificate Course for Content Creators

1. Introduction: Hi, everyone, and welcome to the ethics of AI. This is a short and fun certificate course where we will learn the basic principles of ethics applied to the principles of AI and in particular, the three primary areas in which AI is causing ethical concerns. So in Module one, we'll talk about the fundamental concepts of ethics and AI and what the tensions are. In Module two, we'll talk about the big three ethical questions in AI. And in Module three, we'll talk about building your own personal framework for how to wrestle with these issues. This won't be a dogmatic class, I hope. So the goal here is for you to make your own decisions and give you information. So I hope to see you on the inside. Suggesting. 2. Why This Class Exists: Okay, let's start with why this class exists. So AI is no longer theoretical. It's your DA. That's digital audio workstation. I teach a lot of audio classes. That's why you might see a couple, like, audio references here. It's in your DA, your marketing tools, your creative workflow. Whether you're using these tools or not, they're reshaping the creative landscape around you. This isn't a class about whether AI is good or bad. That framing is just too simple. This is about developing the critical thinking skills to make your own informed decisions as the technology evolves. Start with a provocative question that makes the stakes feel real. Something like, imagine you discover your favorite artist's new album was 80% generated by AI. Does that change how you feel about it? And why? This immediately grounds the discussion in something concrete and emotionally resonant. So what we're going to cover, we're going to cover the foundational concepts that you'll need to think clearly about AI ethics. The major ethical questions creators are wrestling with right now, a practical framework you can apply to any AI tool or situation and how to stay grounded as things continue to change. What we won't cover in this class. This is not a legal primer, and I am not a lawyer. The laws are lagging far behind the technology, and this is not a definitive rulebook. Anyone claiming to have all the answers is selling something. This is not pro AI or anti AI advocacy. The goal is to give you tools for thinking, not conclusions to adopt. 3. Understanding Ethics: Okay, let's start with foundational concepts. So in this, we're going to talk about kind of just defining what ethics means for us before we go forward. And I know if you're thinking, Well, this is just kind of academic, blah, blah, blah. It's not. It's going to help us understand what we're trying to understand about AI. So just stick with me, I promise. 4. What Do We Mean by "Ethics"?: Okay, so let's start with ethics versus morality versus the law. The different things. Ethics is practical philosophy. It's about systematically thinking through what we should do in specific situations. Morality tends to be more personal and intuitive. Your gut sense of right and wrong. Law is what society has codified and is notoriously slow to catch up with technology. Here's the key insight. Something can be legal but unethical or ethical but illegal. The law will tell you what you can get away with. Ethics asks what you should actually do. We're focused on ethics here because that's where you have agency, where your choices matter, regardless of what the rules say. Context matters enormously. Using AI to generate placeholder text while you're sketching out a course like me, is different from using it to write the final scripts. Using AI to master a track is different from using it to generate the entire composition. Ethical thinking requires nuance, not blanket rules. This might feel unsatisfying if you want clear guidelines, but the reality is that clear guidelines don't hold up across the variety of situations you'll encounter. Tech companies have terms of service and usage policies, but these are primarily designed to limit their liability, not to guide your creative practice. Platform guidelines will tell you what you can do, not what you should do. That gap is where personal ethics lives. Don't outsource your ethical thinking to corporations whose incentives don't align with yours. 5. The Core Tensions: Before we dive into specific questions, it helps to understand the fundamental tensions that underlie most AI ethics debates. These aren't problems to be solved. They're just tensions that we have to navigate. The first is efficiency. Or you could think of this as efficiency versus authenticity. AI tools can make you faster and more productive, but creative work has never been purely about efficiency. Part of what people value in art and education is the human struggle, the accumulated experience, the personal perspective. When you optimize for speed, what might you be sacrificing? Where's the line between smart tool use and hollowing out the thing that made your work valuable in the first place? There's no universal answer, but you need to know where your line is. The second core attention is accessibility or accessibility versus devaluation. AI lowers barriers to entry. Someone who couldn't afford studio time or years of training can now create polished sounding music. That's genuinely democratizing. It opens doors that were previously closed, but it also floods the market, potentially devaluing the skills that professionals spent years developing. The person who trained for a decade to master something now competes with someone who pressed a button. Both things can be true simultaneously. The question isn't which one is right, but how you navigate a world where both are happening. And the third tension that I want to talk about is innovation or innovation versus exploitation. New technology always disrupts existing systems. Sometimes that disruption is creative distraction that benefits everyone eventually. The printing press, the Internet. Sometimes it's extraction that enriches a few while harming many. AI is currently doing both, often at the same time, often in ways that are hard to untangle. The fact that AI enables amazing new possibilities doesn't mean it isn't also causing real harm. You don't have to pick a side. You just have to see it clearly. 6. Why You Should Care: Why you should care and why any person who creates things should care. Remember that you're shaping norms. You're not just a consumer of these tools. You're a participant in shaping norms. The decisions you make now multiplied across millions of creators will determine what normal looks like in five years. And that's not hyperbole. Industries develop ethical standards through the accumulated choices of practitioners. When enough people behave a certain way, it becomes the expectation. Your choices matter beyond your own practice. And your audience is listening. Trust is the foundation of the creator audience relationship. How you navigate AI will affect that trust, whether you are transparent about it or not. People can often sense when something feels off, even if they can't articulate why. And if they later discover you weren't being straight with them, the damage is too hard to undo. Your ethical choices aren't just abstract. They have real consequences for your relationships with the people who support your work. And don't underestimate your own creative satisfaction. Beyond external considerations, there's the internal question. What kind of creative practice do you want to have? What role do you want AI to play in your work? These are personal questions that only you can answer, but you need a framework to think them through. Otherwise, you'll drift into patterns that might not serve you, taking shortcuts that feel hollow or refusing tools that could genuinely help all without ever consciously choosing. 7. The Questions: Alright, now that we've looked at the big tensions, let's look at what the big questions are in ethics around AI. They tend to fall under three kind of broad categories. And that is how the model is made is the first one, meaning what data it's using, the consent of the people who provided that data. This also includes how it effectively is a type of sampling where it's creating new things based on previous work. So that's thing number one. Number two is the labor argument and displacement of labor, which is, you know, how it is currently and will probably more put a lot of people out of jobs and what that means. And then the third big thing is transparency and disclosure, knowing who's using AI and for what and what is AI generated and what isn't. So let's dive into those now. 8. Training Data & Consent: Alright, the big questions, number one, where the model comes from training data and consent. So large language models and generative AI systems are trained on massive datasets to scrap from the Internet. Books, articles, forum posts, social media, images, audio. Most of this content was created by humans who never consented to having their work used this way and who receive no compensation or credit. This is the foundation on which all these tools are built. Now defenders of current AI training practices argue that this is no different from how humans learn. We all absorb influences and transform them into something new. Every artist learns by studying other artists. Critics argue there's a meaningful difference between a human spending years developing a style through study and practice versus a machine ingesting millions of examples to statistically recombine them. One is growth, the other is harvesting. Neither side has a monopoly on truth here. The human learning analogy isn't perfect. Machines don't experience art the way we do, but the extraction critique can also be overstated. All culture builds on what came before. What matters is that you've thought it through and know where you stand rather than just absorbing one side's framing. Now also consider that when you use an AI tool, you're benefiting from this training data, whether you think about it or not. The question becomes, does that implicate you ethically? And if so, what, if anything, should you do about it? Some creators avoid AI entirely for this reason. Others use it while advocating for better compensation systems. Others don't worry about it at all. There's no consensus, but pretending the question doesn't exist isn't an option if you want to think ethically. Let's look at an example. Visual art generators, mid journey, stable diffusion, similar tools. They're all trained on datasets that include millions of copywritten works without permission. Some artists have found that these systems can generate images in the style of specifically living artists with disturbing accuracy. This is probably the clearest current example of the consent problem, and it's worth examining even if you work primarily in audio. Audio AI, the area I work most in, is on a similar trajectory, just a bit behind. Models trained on copywritten recordings, stem separation tools that enable unauthorized sampling, voice cloning that can replicate specific performers. These raise parallel questions. The visual art situation is a preview of where music is headed. 9. Labor & Displacement: All right, onto the labor and displacement argument. So first, when we think about the question, the issue of labor displacement, let's start by thinking about who benefits from AI. Currently, the primary beneficiaries are the companies building and selling the tools, obviously, users who can produce more with less effort and consumers who get cheaper and faster content. This isn't unheartily bad. Efficiency gains can be genuinely valuable, but it's worth being clear eyed about who's winning. Who is harmed or at risk? Workers whose skills are being automated face real displacement. Creators whose work train the models receive nothing in return, and potentially everyone faces a future where quality and diversity decline as human expertise is devalued. These harms are harder to see because they're diffuse and slow moving, but they're real. We often talk about the democratization that AI brings. And the AI companies definitely love to frame their tools as this democratizing force because now anyone can make music, can write, create art. And that's actually very true. The barriers to entry are genuinely lower with AI tools. But it's worth asking democratization for whom and at whose expense, lowering barriers to entry is good. But devaluing skilled labor and concentrating profits in a few tech companies is less clearly good. Both are happening simultaneously. A fallacy that often comes up in this area is called the inevitable fallacy, which is a common argument of AI is inevitable, so you might as well adapt. But technologies aren't forces of nature. They're shaped by human choices, including yours. The form AI takes, how it's regulated, what norms develop around it, all of this is still being determined. Inevitable quote is often a way of foreclosing ethical discussion, of making you feel powerless, so you'll stop asking questions, but don't fall for. Now, where is your opinion in all of this? You likely exist on multiple sides of this equation. You might use AI to work more efficiently while also worrying about AI coming for your job. You might benefit from AI generated content in some context while creating original content in others. Sitting with this complexity is more honest than pretending you're purely on one side or the other. 10. Transparency & Disclosure: Okay, the third big question around AI ethics is transparency and disclosure. So AI involvement exists on a spectrum from trivial to total, on one end, using AI for spell check or grammar suggestions or using AI for research or brainstorming. Further using AI to generate drafts that you heavily edit or further still using AI to generate content that you lightly edit. At the far end, using AI to generate final content wholesale. Most people agree the first category requires no disclosure. Most people agree the last category does or should. The middle is murkier. So let's talk about disclosure. Disclosure matters for audience trust. People feel deceived when they learn something they valued for being human and it wasn't. It also matters for industry norms. Without disclosure, it's impossible to develop shared standards. It matters for your own integrity. Secrets tend to corrode things over time. Even if no one ever finds out, you know, and that affects how you feel about your work. But disclosure is complicated because where do you draw the line? Do you disclose every tool you use? Context matters. A social media post has different stakes than a master class. There's competitive disadvantage. If others aren't disclosing, you might look worse for being honest. And it's genuinely confusing. What even counts as AI generated when you've heavily edited it? There are no easy answers here, which is why you need a framework rather than a rule set. So here's a useful test. Would you be comfortable if your audience knew exactly how you used AI in creating something? If the answer is no, then that's worth examining. You might decide you're fine with it anyway, but at least you're making a conscious choice rather than avoiding the question. 11. The 3 Questions: Alright, in this section we'll be talking about building your personal framework. So again, because we are humans that like patterns, I have a series of three questions that you can ask yourself that might help you come up with an answer to the issues at hand. So let's dive in. 12. Spectrum Thinking: All right. The three questions you should ask yourself before using any AI tool. Question one, what was it trained on and am I comfortable with that? Not all AI tools are equal in their ethical baggage. Some are trained on licensed content, some are trained on public domain material. Some are trained on scraped copywritten work. Some companies are transparent about training data, others are opaque. This isn't about finding clean tools, necessarily. They may not exist, but about making informed choices rather than ignorant ones. Practical steps. Check the company's documentation if it's available, look for news coverage about training data controversy. Consider whether the tool could replicate specific artists styles, that would be a red flag and decide what level of certainty you need before using the tool. Question two, what human work am I replacing or augmenting? There's a meaningful difference between using AI to do something you couldn't do at all and using AI to do something faster than you could do yourself and using AI to do something instead of hiring a human. None of these is automatically wrong, but they have different ethical weights. If you're using AI to avoid paying a human for skilled work, you should at least be honest with yourself about that. Questions to consider. Would I have done this work myself, otherwise? Would I have hired someone to do this work? Am I using AI because it's genuinely better or just cheaper and faster? What skills am I not developing because AI is doing this for me? Alright, Q three of the big three, would I be comfortable if my audience knew exactly how I made this? This is the transparency test made explicit. Imagine your most discerning student or your most loyal customer knew the precise role AI played in whatever you're creating. How do you feel about that? If you feel defensive or uncomfortable, that's information. You might still decide to proceed, but you're making a conscious choice rather than avoiding the question. 13. The Three Questions to Ask Before Using any AI Tool: So if we think of AI as being able to be one of these four things on our sort of creator spectrum here, then it gives us a pretty good view of it. So AI can behave like a tool, all the way on the left side, where you're still doing creative work, but it just helps. It can be like a collaborator. Where you're in charge, but it's contributing creatively. Or it could work like a ghost writer, like hiring someone to write it for you, where in that case, it's doing the creative work, and you're just editing and curating. Or, lastly, the most extreme is a total replacement, where any human involvement is optional. The different uses of AI fall at different points in this spectrum. The same tool can be used in different ways. There's no single right answer about where the line should be, but you should know where your use falls. 14. Where Do You Draw the Line?: Now, your line might be different from someone else's, and that's fine. It might also shift over time as you gain experience with the tools or as thinking evolves. The goal isn't to fix a permanent position, but to be intentional about where you are at any given moment. A few factors that might inform where you draw the line. Would be your values around authenticity and craftsmanship, your business model, and what your audience expects, the specific context, if it's a personal project or if it's commercial work, the stakes involved, and your own skill level in the relevant area. Now, lastly, on this topic, avoid extremes. To positions to be skeptical of is AI is just like any other tool, that argument ignores the genuine difference between AI and earlier tools, the scale, the training data issues, the displacement effects, and any AI use is cheating or unethical. That ignores legitimate uses and prevents nuanced thinking. Reality is messier than either extreme allows. 15. When To Be More Cautious: Okay, some other ways to think about AI and come up with some of your own reactions. Spend some time thinking about some of the more high stake situations and how that's different than your everyday situation. What that means is a high stake situation might be something like content that will be someone's first introduction to you or your brand or educational content where students are really trusting your expertise. Work you're charging premium prices for or content about sensitive topics. So those are different than your everyday situation. Also, keep in mind to watch out for signs that you're talking yourself into something that you're not comfortable with, like everyone else is doing it, no one will know. It's not technically lying. I don't have time to do it properly. You get the drift. These aren't always wrong, but they're flags to slow down and think more carefully. When you find yourself making these arguments, pause and ask whether you'd make them to your most respected peer. Oh 16. How to Keep Up with Technology: As we all know, technology doesn't stop. Things are going to keep changing, and it's important the questions we're asking keep changing as well. So in this section, we'll talk about techniques for staying grounded while the technology keeps changing. 17. The Landscape Will Keep Shifting: AI capabilities are evolving faster than our ability to develop ethical frameworks around them. What's cutting edge today will be obsolete in two years. This means any specific rules you develop will need constant revision. Don't get too attached to any particular positions. Be attached to the process of thinking them through. Every time a new capability is added to AI, it brings new ethical questions like voice cloning, authenticity and identity. What does it mean when anyone can sound like you? Video generation. How do we navigate a world where video isn't proof anymore? Autonomous agents, who's responsible when the AI acts on its own? Real time AI. What's live when AI is always helping? You can't anticipate every question, but you can develop thinking skills to navigate them. And remember that some people want a simple checklist, like, AI is okay and not okay. This is tempting but counterproductive. The technology, the norms, and the context are all moving targets. What you need is a framework for thinking, not a fixed set of rules. Rules are brittle. Frameworks are adaptive. 18. Resources for Staying Current: So let's talk about your values. What do you really care about? Strip away the technology for a minute. What matters to you in your creative work? Is it authenticity? Does your work reflect genuine experience in your perspective? Is it craftsmanship? Do you take pride in the quality and care of your process? Is it service? You help your students or your audience genuinely improve? Is it honesty? You don't mislead people about what they're getting? Sustainability. You can maintain this practice long term without burning out or compromise. Or as a community, you contribute positively to your field and your peers. AI choices should flow from these values and not override them. But when you're unsure about a specific situation, return to your values and ask which choice is most aligned. And think about your values as a competitive advantage. In a world of AI generated content, human values can become differentiators. The more homogenized AI output becomes, the more people will seek out work that feels genuinely human. That isn't a distinctive perspective. That comes from real experience. Your values aren't just ethical guides. They're part of what makes your work worth paying for. This isn't cynical. It's recognizing that ethics and business can align. 19. Anchor in Your Values: Let's talk about staying current really quick and some resources for that. Because remember that ethical thinking about AI isn't a one time exercise. You need ongoing input to keep your framework relevant. The technology changes, the norms evolve, and your own understanding deepens. Build this into your practice. So types of sources to follow for AI news, technology news. I'm not going to tell you specific news outlets, but watch out for technology news. Look out for legal and policy developments, what's being regulated by the government. Keep an eye on creator communities, how your peers are navigating these questions. Look at academic research, if that's your thing, and what long term studies are revealing. And critical voices, people raising concerns about AI harms. And be wary of pure AI hype from people with stuff to sell and pure AI doom from people with attention to capture. Certainly in either direction is a red flag. The most useful voices tend to be nuanced, willing to acknowledge complexity and honest about their uncertainty. Find those people and listen to them. 20. Key Takeaways: Alright, a few key takeaways. First, there are no easy answers, but there are better questions. The three question framework gives you a practice tool for navigating specific decisions. Use it, write the questions down, make them part of your workflow. Over time, asking them will become automatic. Your position is allowed to evolve. You don't need to figure everything out now. Start where you are, make conscious choices, and refine your thinking as you gain experience. The goal isn't to reach a final answer. It's to be thoughtful along the way. Number three, transparency is almost always better than concealment. One in doubt err on the side of honesty with yourself and your audience. Secrets tend to become liabilities. Even if disclosure feels risky in the short term, it builds trust in the long term. Number four, which isn't on the slide, but I'm going to add it anyway. Your values are what anchor you. Technology is always changing, but your values don't have to be. Let them guide you. When the specific situation is unfamiliar, return to what you care about and let that inform your choice. And the last one, your shaping norms. The choices you make contribute to what normal becomes. That's both responsibility and opportunity. You're not just adapting to a world being shaped by others. You're one of the people shaping it. 21. Thanks for Watching!: Alright, quick little final thought. Ethics isn't about being perfect. It's about being thoughtful. So thanks for watching. Thanks for being a part of this class. I hope you are asking these questions, and get a lot out of it. Thanks a bunch. See you soon.