Introduction
Recently, I was scrolling through Instagram and stopped at a photo of a remote Spanish beach. The water looked impossibly clear, and a little café nestled by the shore caught my eye. On a whim, I added it to my travel wish list. It made me pause and question: Would I have wanted to go there if an algorithm hadn’t put that post in front of me? Would I have even known about this place if an algorithm hadn’t put it on my screen? In a world where algorithms quietly organize so much of our online experience, it’s worth asking how many of our choices originate with us, and how many are shaped by lines of code.
Keep in mind that algorithmic recommendations guide over one-third of what people buy on Amazon and 80% of what they watch on Netflix [1]. We like to say “I chose to buy that” or “I decided to watch this,” but behind the scenes, carefully designed systems are nudging us to pick certain products or shows. It’s not just shopping and streaming, either. Everyday decisions—from the restaurants we try and the trips we plan to the matches we swipe on—are often influenced by recommendation engines and matching algorithms. At the heart of this realization is a question worth exploring: Do algorithms decide more than we think?
Algorithmic Appetites: Food Delivery and What We Eat
The last time I opened a food delivery app to order dinner, a neat list of “Recommended for You” restaurants and a section of popular picks immediately caught my eye. It felt convenient—almost as if the app already knew what I wanted. In reality, that convenience is powered by algorithms running quietly in the background. Rather than presenting every nearby restaurant in random order, these platforms use specialized ranking systems to decide which eateries you’ll see first [2]. For example, they personalize recommendations based on your ordering habits. If you often go for sushi, the algorithm notes that pattern and floats the best-reviewed sushi spots to the top of your feed [3]. Ratings and popularity also matter—restaurants with higher customer satisfaction or frequent orders tend to get extra visibility [3].
But your personal taste isn’t the only factor. Some platforms allow restaurants to pay for priority placement, or they have marketing deals that shift which options appear first [2]. So, when you see “top picks” or “popular near you,” part of that label might reflect sponsored positioning rather than pure user preference. In turn, you’re more likely to settle on these recommendations than to sift through the entire list of possibilities.
Is this nefarious? Not necessarily – often it aligns with what you’d want anyway (a tasty dinner delivered fast). In fact, the goal of these algorithms is to help you find something you’ll enjoy with minimal effort. But it does raise a curious point: your decision of what to eat is being co-crafted by an algorithm’s filtering. You might have had total freedom to order anything, yet in practice you’ll probably pick one of the first few restaurants the app shows. Your cravings are being channeled by design. In a sense, the menu has been pre-selected, tailored just for you, and you didn’t even notice the hidden hand. It’s a small example of digital determinism in daily life – a subtle nudge that might determine whether you end up eating burgers or salad tonight.
Instagrammable Destinations: Social Media and Travel Choices
Consider how we pick vacation spots these days. A decade ago, you might flip through a travel guide or swap stories with friends. Now, if you’re like many people, you rely on social media—especially Instagram—for trip ideas. Every day, millions of people upload meticulously framed images of beaches, mountains, and urban skylines, all vying for your attention. But these posts do more than just fuel daydreams: Instagram has significantly changed how we discover and choose destinations.
Take a closer look at what happens when you use Instagram. Its algorithmic feed and Explore page don’t show you the world at random; they highlight what’s popular, what’s trending, and what aligns with your interests. If you’ve been liking pictures of European castles, you’ll start seeing more medieval towns and misty fortresses. If the influencers you follow are heading to a certain island this summer, your Explore page will likely be flooded with images of that same island. In short, Instagram’s algorithm spotlights certain places over others, guided by both overall popularity and your specific engagement patterns [4]. The result is a condensed travel shortlist curated just for you.
This isn’t just theoretical. The concept of “Instagrammability”—how photogenic a place looks on social media—actively shapes people’s travel plans. Remarkably, one UK survey revealed that more than 40% of millennials rate a destination’s “Instagrammability” as the top factor in deciding where to go [5]. That means for a large slice of younger travelers, eye-catching photos can outweigh things like cost or culture. Another study, sponsored by Facebook, showed that 67% of travel enthusiasts turn to Instagram for vacation inspiration [5]. Basically, your feed doubles as a dynamic brochure.
Of course, discovering destinations on Instagram isn’t necessarily bad. A single photo can introduce you to, say, the lavender fields of Provence or a tucked-away waterfall in Indonesia. It can also boost tourism in off-the-beaten-path locations. But there’s a downside: when everyone sees the same attention-grabbing spots, we can end up with herd behavior. Certain places—like Santorini, Bali, or Iceland—experience massive popularity surges because they look great in photos and therefore get more screen time [6]. That screen time, in turn, attracts even more visitors, creating a self-perpetuating travel trend. Your “I really want to go there” feeling might be fueled by Instagram’s feed, subtly shaping your vacation dreams.
Dating Apps and Algorithmic Matchmaking
Choosing whom we date is among the most personal decisions we make. Not long ago, it was mostly about chance meetings or introductions through friends. Today, online dating apps have become a primary way people find partners. With that shift comes a key change: behind each dating app is an algorithm taking on the role of Cupid. So, how much of your love life is being shaped by code?
First, consider how widespread these apps are. A 2019 study found that almost 40% of U.S. couples met through online dating [7], making it the most popular avenue for new relationships. That means for many people, an algorithm effectively decides which potential partners they see. As one tech writer put it, “one way or another, the algorithm decides who you’re going to meet” [8]. When you open Tinder, Hinge, or another platform, you aren’t browsing every possible person in your vicinity; you’re seeing those the algorithm’s filters have chosen for you.
What does that look like in practice? These apps typically rank and display profiles based on your stated preferences, your swiping behavior, and even your perceived attractiveness. Tinder famously used an internal system to score users by desirability, then primarily matched them with others at a similar level [9]. So, if the collective swipes labeled you a “7 out of 10,” you’d mostly encounter people in that same range.
Moreover, many dating platforms use machine learning to learn your “type.” If you frequently swipe right on profiles mentioning hiking, or showing a certain style, the system notices and highlights more profiles along those lines. Hinge, for example, tries to figure out both who you’ll like and who’s likely to like you back [10]. This approach boosts efficiency—giving you matches the app predicts will interest you. Yet it also means that from the moment you log on, an algorithm guides nearly every step.
From a user’s perspective, it still feels like you’re the one deciding whom to swipe on. Indeed, you have the final say. But at the same time, you’re only choosing from the subset of profiles that the system decided to put in front of you. In other words, your romantic possibilities are filtered by an algorithm before you even begin.
Philosophical Perspectives on Free Will in the Algorithmic Age
Philosophers have long debated the nature of free will in light of determinism—the view that every event, including human decisions, inevitably follows from prior causes. If the universe is entirely deterministic, then in a strict sense, all our choices might be predetermined outcomes of earlier states: atoms colliding in fixed ways, neurons firing by unbreakable physical laws. Free will would seem more like a comforting illusion than a tangible force. Yet compatibilists propose that even if our actions have causes, it is still possible to act freely when our decisions align with our genuine intentions, desires, and reasoning. For a concise look at these debates, there is a fantastic video by Kurzgesagt that provides an accessible overview.
In an era where algorithms routinely shape our online experience, the question becomes how these automated processes interact with whatever freedom we might possess. Some forms of recommendation technology aim to broaden users’ horizons by suggesting entirely new genres of music, articles on topics they have never explored, or cuisines they have never tried. Even so, each “new” suggestion is itself the product of a data-driven model that decides, based on past behavior or predicted preferences, which possibilities to highlight. If these systems appear to expand a person’s worldview, they might still be steering them along paths that the algorithm deems appropriate.
Another perspective is that algorithms act like hidden gatekeepers, directing individuals toward certain choices and away from others. A user may feel as though they are independently opting to watch a new film or read an unexpected piece of news, while in reality an algorithm decided which options to prioritize on-screen. The fact that these choices seem novel does not fully negate the possibility that every prompt or suggestion was orchestrated by code. In this sense, whether one remains within a familiar content circle or ventures into unexplored territory, the system’s invisible framework might be doing much of the guiding.
Yet it can also be argued that the data these algorithms rely on originates in spontaneous human actions—clicks, likes, or reading habits that were not themselves dictated by a single grand plan. If personal tastes can influence and reshape the recommendations, then there may be room for genuine back-and-forth between user and algorithm. The individual’s voluntary decisions help determine the system’s outputs, while the system’s outputs, in turn, affect the user’s future decisions. Whether this interplay ultimately bolsters or restricts free will remains an open question in a world increasingly orchestrated by predictive technology.
Are We Still Choosing?
All these examples – dinner, travel, dating, and we could add others like the news articles we read or the music we discover or the things we buy – paint a picture of our lives subtly guided by algorithmic recommendations. It’s not that we have no choice (we aren’t robots on a fixed program), but our choices are being shaped by the way options are presented to us. This notion, often referred to as “digital determinism,” suggests that in an era of pervasive algorithms, our decisions may be increasingly influenced by data-driven suggestions that undermine our sense of autonomy [11].
Wharton professor Kartik Hosanagar, who studies algorithmic decision-making, put it nicely in an interview: we still have free will “at some level,” but we “don’t have the level of independent decision-making we think we have.” We think we see a set of options and independently choose, “but algorithms are actually nudging us in interesting ways,” he says [1].
A Future of Algorithmic Influence
“Do algorithms decide more than we think?” is a question that calls for awareness rather than alarm. The examples we’ve explored—from dinner choices and travel plans to dating and beyond—show just how deeply algorithms can intertwine with our daily decisions. Their influence is often greater than we suspect, yet not so absolute that we have no control. Whether these digital suggestions empower us or limit us may depend on how we choose to engage with them, both individually and collectively.
It’s not a dystopia of total control, nor is it a perfectly neutral state of affairs. Algorithms often work behind the scenes, guiding our preferences in subtle ways we don’t always detect. At the same time, we can observe, question, and even resist these nudges when we recognize them. In short, the interplay between our agency and algorithmic influence is complex, but it doesn’t need to rob us of our ability to make meaningful choices. By staying informed, experimenting with alternative perspectives, and demanding transparency where we can, we retain a stake in how this technology shapes our lives. The crucial step is to remain conscious of the ways algorithms might steer us—so that when we do make our choices, we can be sure they’re truly ours.
References
[1] Hosanagar, K. (2019). A Human's Guide to Machine Intelligence. https://www.kartikhosanagar.com/humans-guide/
[2] The Verge. (2020). How food delivery apps use algorithms to influence what you order. https://www.theverge.com/2020/8/4/21353225/doordash-ubereats-grubhub-restaurants-pricing-promotions
[3] CKitchen. (2021). How food delivery algorithms determine what you see. https://www.ckitchen.com/blog/2021/9/how-food-delivery-apps-control-your-dinner.html
[4] ICEF Monitor. (2020). Instagram’s growing role in influencing international student travel decisions. https://monitor.icef.com/2020/01/how-instagram-is-influencing-student-travel-decisions/
[5] Big 7 Travel. (2019). Instagrammability now a top priority for millennial travelers. https://bigseventravel.com/40-of-millennials-prioritize-instagrammability-when-traveling/
[6] Quartz. (2018). Instagram is ruining travel. https://qz.com/1465594/instagram-is-ruining-travel
[7] Stanford University. (2019). Disintermediating your friends: How online dating in the United States displaces other ways of meeting. https://www.pnas.org/content/116/36/17753
[8] The Spinoff. (2022). Tinder’s algorithm is micromanaging your dating life. https://thespinoff.co.nz/society/10-06-2022/tinders-algorithm-is-micromanaging-your-dating-life
[9] Fast Company. (2016). Tinder’s matching algorithm is based on the Elo rating system. https://www.fastcompany.com/3062987/tinders-matching-algorithm-is-based-on-the-elo-rating-system
[10] Hinge. (2021). How Hinge uses machine learning to improve your matches. https://medium.com/hinge/how-we-built-the-most-compatible-feature-on-hinge-5fcf92f59dc1
[11] Nature HSS Communications. (2021). Personalization and autonomy in algorithmic environments. https://www.nature.com/articles/s41599-021-00801-w