# AI Recommendation Algorithms and Human Freedom: Perspectives from Tech and Philosophy ## James Brusseau speech --- ### Slide 1 ![Slide 1](../../DISC-0081_opening_slide_review_2026-10-27_01/frames/candidate_001_0000s.png) PACE UNIVERSITY Seidenberg School of Computer Science and Information Systems AI Recommendation Algorithms and Human Freedom Perspectives from Tech and Philosophy --- It's my sense that almost no work has been done in this area of discussion. So right in the present, what we are discussing is the intersection of recommendation algorithms, things like what movie Netflix suggests you might want to watch, and artificial intelligence. Now quite a bit of work has been done at this intersection for economic reasons. Companies like Netflix want you to stay on their site. So there is quite a bit going on there. But to my knowledge, very little or perhaps nothing has been done in the area of human freedom, AI, and recommendation algorithms. So the plan today is to begin mapping out what this area of investigation might look like, what kinds of questions arise, what dilemmas exist, what pathways forward might we find. --- ### Slide 2 ![Slide 2](../../DISC-0081_opening_slide_review_2026-10-27_01/frames/candidate_002_0097s.png) 1. The Locke Box Thought experiment: *What is human freedom?* You’re inside a pleasurable room that you don’t want to leave, and don’t try to leave, so you don’t realize the door has been locked from outside. Are you free? No, according to John Locke (*Essay Concerning Human Understanding*, 1689), because you cannot change your preferences and purposes (without encountering confinement). Genuine human freedom → *Ability to change what you want, and who you are* --- So I believe that everything is very, very contemporary. However, I also believe that the questions circulating through this area of investigation, the questions perhaps started more than 300 years ago with a philosopher named John Locke, who proposed the following thought experiment. What he asked us to imagine is a situation where we are in a room which is extremely pleasurable. That is, you can see here, we have wine and we have a wonderful view, have a nice something to lay on. You're in the room doing whatever you like. You are happy. You have no reason to leave. You don't want to leave. So you spend your time doing your little things in your room. But unbeknownst to you, someone has locked the door from the outside. So you cannot leave. And the question that Locke asks there is, well, are you free at that point as a human being? Do you have autonomy? You feel free. Of course, you're doing whatever you want to do. You don't even realize the door is closed, but are you truly or genuinely free? And here, what John Locke says is no. He says you are not free because you do not have the ability to change what you want. You're getting what you want in this great room, but you cannot change. You cannot change what you want. And in a certain sense, you cannot change who you are because you keep repeating these very pleasurable actions in this room. --- ### Slide 3 ![Slide 3](../../DISC-0081_opening_slide_review_2026-10-27_01/frames/candidate_003_0212s.png) 2. How do recommendation algorithms resemble Locke’s room? By reinforcing established interests and purposes we receive convenience and contentment, but we are confined to being who we are Netflix constantly proposes similar films Twitter/X echo chamber of preferences LinkedIn job opportunities replicate past roles Tinder surfaces the same person, with different name **Reproduced material — SHETLAND** SHETLAND SEASON 1 EPISODE 1 Starts in 0 seconds --- Okay. So that's 300 years ago. What does this have to do with recommendation algorithms? Well, what we want to say is it seems like something like Netflix is doing almost the same thing. It's recommending movies, films, documentaries that satisfy us, that we want to watch, that we enjoy. But the way it is doing that is by generating a list of recommendations from our past interests, from the movies we have given five stars to, from the documentaries we have watched all the way through. So the result is, in a certain sense, we are trapped in the interests and preferences that we have already established. We are happy in front of our TV watching Netflix. We're getting what we want, but there's no way to change what we want because everything we are offered resembles the things we have seen in the past. So if you watched Indiana Jones and you loved it, then the next thing you get is Mission Impossible or something. --- ### Slide 4 ![Slide 4](../../DISC-0081_opening_slide_review_2026-10-27_01/frames/candidate_004_0293s.png) 2. How do recommendation algorithms resemble Locke’s room? **Reproduced material — The New York Times Magazine** The New York Times Magazine Account How Everyone Got Lost in Netflix’s Endless Library Ten years after Silicon Valley remade TV, it’s become clear how the streaming revolution distorted our collective viewing habits — and sense of the culture. **Quotation — The New York Times Magazine** Lotz pointed out to me a seemingly banal but actually profound and strange experience that has become common in this era: You go to an Airbnb and turn on the TV, already open to someone else’s account, and you see all this *stuff* you didn’t even know existed. Same TV, same app, same subscription, the house might even feel similar your own — but the screen gives way to an alien world. --- Now, as it happens, something like this was referenced in the New York Times just this weekend. It came up in this article in the magazine. Specifically, there's this experience that is referenced, which has actually happened to me and maybe happened to you guys too, where you go to an Airbnb and you turn on the TV and it's someone else's Netflix. And you look at these films that are suggested and you think, where did that come from? I did not even know those things existed. There are all these programs that are completely foreign to you. So I think that what that shows is how much of a silo there is in Netflix, how you can be—trapped is not the right word because you're happy—how you can be satisfied by this range of movies and films that are completely different than anyone else's. And that satisfaction keeps you in the silo because you keep getting what you want. So again, I think what we're replicating here with Netflix is the John Locke experiment. --- ### Slide 5 ![Slide 5](../../DISC-0081_opening_slide_review_2026-10-27_01/frames/candidate_005_0371s.png) 3. How recommendations reinforce who I am: NETFLIX Content-based filtering Similar movies Liked by Bob Recommended to Bob Collaborative filtering Liked by Alice and Bob Similar users Liked by Alice, recommended to Bob --- Now very quickly, how does Netflix actually do this? What are their tools? Here are two things that Netflix really does. One is, and Spotify does this too, one is if you like a movie like Indiana Jones, they try to figure out what other movies are like that movie, maybe similar actors or a similar plot or something, and then they recommend you that, right? So if you liked one thing, you get something else which is just like what you've already enjoyed. The other strategy they use, collaborative filtering, is they try to find a kind of twin for you out in the world. So if Marianne likes Indiana Jones and Mission Impossible, and I like Mission Impossible and Indiana Jones, and then she watches another Mission Impossible movie and enjoys it, then that movie will be recommended to me. Again, it's the same thing, whether it's my own past and my own interests or my interests as they're reflected off someone else, I'm caught in the preferences that I have already established. --- ### Slide 6 ![Slide 6](../../DISC-0081_opening_slide_review_2026-10-27_01/frames/candidate_006_0439s.png) 3. Note Film recommendations relatively insignificant form of imprisonment, but... Netflix Twitter/X LinkedIn Tinder Home (Which neighborhood is recommended...) Healthcare (Genetic traits of children...) --- So as a quick note, it's true that Netflix doesn't matter. I mean, who cares if you're imprisoned by Netflix? I mean, who cares if you don't have human freedom when you're on your sofa drinking a beer, watching a movie? Doesn't matter. But you can move up levels, right, from Netflix to Twitter. If you get to LinkedIn, then you're talking about your professional life. If you get to Tinder, then you're talking about your personal life, who you will marry. If you're searching—I was speaking with some of our panelists about where we live—one way you end up living somewhere is the real estate engine recommends apartments for you. Same thing. Maybe in the future, when we are choosing the genetic makeup of our children, we will get recommendations: do you want child A or B, right, suited to you and your own past, your own established preferences. So I think it's true of—well, of course it's true that Netflix is not a profound subject, but I believe that the dilemmas that are at play in Netflix recommendations are significant now and will be very significant in the future, significant for our human freedom. --- ### Slide 7 ![Slide 7](../../DISC-0081_opening_slide_review_2026-10-27_01/frames/candidate_007_0529s.png) 4. What is to be done? Genuine human freedom → *Ability to change what we want, and who we are* For recommendation algorithms to expand human freedom, recommend: Not what you want, not what you don’t want, but what you *could* want **Reproduced material — hulu** A HULU DOCUMENTARY 69 [partly occluded title] NDEZ hulu --- Okay, so we're getting toward the end of the introduction, the last two points. The first one is, well, what can we do? If, if—and you don't have to accept this, maybe you don't believe John Locke's definition of freedom—but if you accept that that freedom means the ability to change who you are, then for recommendations what we want is not something we already want, because that just keeps us who we are, and not something we don't want, because that's not a recommendation, that's like random. We want a film, a documentary, something that we could want. Not something we want now, but something we could want, something that would correspond with different kinds of interests that we don't yet have, something that would indicate a sort of enjoyment that we have not yet experienced. That's what we would look for if we want to have human freedom in these recommendation algorithms, right? We want suggestions that help us change the kinds of things we want, that help us develop new preferences. --- ### Slide 8 ![Slide 8](../../DISC-0081_opening_slide_review_2026-10-27_01/frames/candidate_008_0609s.png) 4. What is to be done? Not what you want, not what you don’t want, but what you *could* want = A hard problem! Recommendations should be: Unfamiliar (Unexpected/Novel/Diverse) = Not imitating past selections, not technically accurate Engaging (Relevant to who the user *could* be, provoking curiosity + exploration) = Not random \*Accuracy subordinated to provocation --- And then penultimate point, this is a really hard problem. Mary Lou will talk about this perhaps in a bit. We need to find things—in order to get this kind of recommendation, we need to find things that are unfamiliar, right? Something that's not like what we've seen before, but it also has to be engaging, relevant. If you stop for a second and think about that, that's extraordinarily difficult. How exactly do you find that kind of film to recommend for a user? Something that they've never seen before or thought of before. Kind of like when you walk into the house with someone else's Airbnb set, excuse me, someone else's Netflix set. So it's completely foreign, but it's also engaging. That's the difficulty. --- ### Slide 9 ![Slide 9](../../DISC-0081_opening_slide_review_2026-10-27_01/frames/candidate_009_0664s.png) 5. Conclusion What is the problem? • Recommendation algorithms provide convenience but diminish freedom by limiting future prospects to established preferences: We are imprisoned by who we are. Where and how does this problem exist in the real world? Solutions? Directions to explore? Anusha Dandapani Chief Data & Analytics Officer United Nations International Computing Centre Dr. Mary Lou Maher Software & Information Systems UNC Charlotte Mary Anne Powers Executive Producer & Digital Marketing Consultant James Brusseau Philosophy, Pace University, NYC CS, University of Trento, Italy --- Good. So that is the introduction. This is the problem. The problem is that recommendation algorithms provide convenience and satisfaction, but they diminish freedom by limiting our future prospects to our established preferences. We are imprisoned by who we are. So that is the introduction.