1. A Minimal Account of Authenticity

Many expressions belonging to the lexical field of authenticity are ambiguous. In everyday language, we speak of people who are “true to themselves” and actions that reflect “who they really are” without needing to explain what this means. Philosophers too frequently employ figurative expressions to describe authenticity. Bernard Williams, for instance, once said that his life writings are unified by the “idea that some things are in some real sense really you, or express what you and others aren’t” (Jeffries Reference Jeffries2002). This echoes an idea that Charles Taylor attributes to Herder: “There is a certain way of being human that is my way. I am called upon to live my life in this way, and not in imitation of anyone else’s.” (Taylor Reference Taylor1992: 28–29) Philosophers also use allegorical phrases when they discuss inauthenticity. As Varga and Guignon point out (2023), Heidegger’s analysis of Dasein is guided by the idea that a person can “lose herself” by unreflectively acting as a member of “the herd.” More recently, Harry Frankfurt (Reference Frankfurt1988) has described persons as beings who have the power to make some of their desires more truly their own and withdraw themselves from others. When we succeed or fail in following the desires we reflectively endorse, Frankfurt argues, “the satisfactions at stake are those which accrue to a person of whom it may be said that his will is his own” and “the corresponding frustrations are those suffered by a person of whom it may be said that he is estranged from himself” (22). When we succumb to weakness of will, “our hearts are at best divided, and they may even not be in what we are doing at all” (163).

Can we disambiguate the metaphorical language of authenticity? In this section, I offer a philosophical account of this concept. From my perspective, authenticity is a multi-dimensional notion. This means that Alice can be more authentic than Bob along some axis of authenticity while simultaneously being less authentic than he is on another axis of authenticity. In simpler terms, Alice can be more authentic than Bob in some sense but less authentic than him in some other sense. Such an idea will become more concrete once I start identifying dimensions of authenticity. Note also that authenticity is not the only philosophical concept that can reasonably be envisioned as multi-dimensional. Happiness is another good example. Imagine, for instance, that Rosa is often in a bad mood but derives an essential sense of purpose and self-worth from her daily work with autistic children. By contrast, Jamal is a happy-go-lucky investment banker who occasionally feels a sense of existential angst when he thinks about spending most of his life making the rich richer. This angst never lasts long and can be easily suppressed with a few games of pickleball with his banker friends. Here, it seems appropriate to say that Jamal is happier than Rosa in some sense, but that, in another sense, he is not. Surely, being joyous daily is relevant to whether one is generally happy, but so is one’s thought that one’s work is meaningful and contributes to the well-being of others.

Just as there are many dimensions of happiness, there are many dimensions of authenticity. In what follows, I describe two of them. Although I hint at what other dimensions might be missing from my minimal account, my remarks will suffice to help us understand how recommender systems affect their users’ capacity to be their authentic selves.

Frankfurt has adequately conceptualized a first dimension of authenticity. Let us call it volitional alignment. Michael Rings (Reference Rings2017: 479) summarizes it as follows:

Such an account of authenticity builds on Frankfurt’s influential distinction between first-order and second-order desires. While first-order desires are unreflective, second-order desires are those that we reflectively endorse. We spontaneously want things (first-order desires), but we also sometimes want to want them (second-order desires). In other words, we often adopt a reflective and evaluative stance toward our spontaneous desires. When we fail to follow our second-order desires, we are unable to realize our most profound aspirations. This is a critical sense in which people fail to be true to themselves.Footnote ¹ Here, a paradigmatic example is that of an addict who attempts but fails to overcome his addiction. As a second example, imagine a frustrated and disengaged lawyer who chose her career merely to please her parents. While the lawyer fulfills her professional obligations, she deeply regrets having chosen this life path; what she truly wants to be is a painter. The lawyer wants to be a lawyer—sufficiently so, at least, to be one every day—but she does not reflectively endorse this desire. Deep down, she knows that good lives are lived by people who dare to resist parental directives and forge their own path. To live an authentic life, the lawyer would have to quit her job and take a chance at painting.

Volitional alignment plays an essential role in self-constitution. As Frankfurt (Reference Frankfurt1988: 170) writes, “The person, in making a decision by which he identifies with a desire, constitutes himself.” When we successfully align our first- and second-order desires, we take steps toward becoming our authentic selves. For instance, we stop smoking, spend less time on our phones, are kinder to administrative staff, quit our unfulfilling jobs, and perhaps even become painters. Of course, when we systematically fail to follow our second-order desires, we also gradually forge our character through what we perceive as bad habits. If we accept volitional alignment as a dimension of authenticity, then we must accept that succumbing to bad habits—even systematically so—is a way of not being ourselves. In other words, volitional alignment makes any philosophical account of authenticity aspirational: the person that “we really are” is constituted by our second-order desires even when such desires fail to direct our will.

The second dimension of authenticity I wish to discuss is epistemic instead of conative. Simply put, authenticity is enhanced by self-understanding. To live authentic lives, we not only need to follow our second-order desires, but we must also understand what these desires are or construct the right ones by gathering self-knowledge. To identify the root of her malaise, the frustrated lawyer first needs to appreciate that she does not reflectively endorse her desire to please her parents, but instead identifies with her desire to forge her own path. Within the history of philosophy, the ideal of self-understanding has given rise to the metaphor of “turning inward.” To apprehend our second-order desires, one thing we can do is engage in introspection. This “inner-sense model of authenticity” is often associated with Rousseau, who envisioned his Confessions and Rêveries as ways to disclose to his readers the authentic self that he had discovered through self-examination (Rings Reference Rings2017: 487). Yet, as Varga and Guignon (Reference Varga and Guignon2023) explain, this introspective view of authenticity has attracted virulent criticism. For instance, Adorno (Reference Adorno1973: 70) contends that it rests on a “liturgy of inwardness,” according to which people are self-transparent subjects whose deepest desires are fixed and can be discovered by the mind’s inner light. In Richard Rorty’s view, Nietzsche also criticized the idea that being authentic amounts to “coming to know a truth which was out there (or in there) all the time.” (Reference Rorty1989: 27). Contrary to Rousseau, philosophical critics of introspection tend to associate authenticity with self-creation more than self-discovery. Their point is that we constitute ourselves by defining (as opposed to identifying) our second-order desires throughout our lifetime.

I see no need to dispute the idea that authenticity involves self-creation. Obviously, we are not born with second-order desires. As psychologists have argued, some preferences are only constructed when elicited (Slovic Reference Slovic1995). We can imagine, for instance, that the unfulfilled lawyer’s frustration with her career only arose when a colleague asked her if she always saw herself working as one. Only by reflecting upon this question did she come to identify and, eventually, reflectively endorse her desire to be a painter. In a sense, her desire to be a painter was “constructed” following her conversation. Still, the Nietzschean critique of self-understanding appears overblown. Surely, authenticity involves a fair amount of soul-searching. Even if we envision desires as constructed, it seems that constructing ones that will lead to a happy life requires understanding important things about myself: what environments are best suited to my personality, with whom I enjoy spending time, what kind of challenges I like to set for myself, etc. This is at least the case if we envision authenticity as an eudaimonic ideal and believe that a person’s second-order desires should derive from a vision of “flourishing that is specifically appropriate to the particular individual in question” (Rings Reference Rings2017: 476). It would be strange to attempt to create out of myself a person better suited to life pursuits that I naturally seek to avoid. As Rings observes, “it would be wrong to banish all epistemic matters of self-knowledge entirely from the realm of authentic self-fulfillment.” That we construct desires or have the power to self-create does not spare us from understanding who we are.

That said, we can do justice to critics of the inner-sense model of authenticity by pointing out that introspection is but one tool that people use to acquire self-knowledge. In what follows, I emphasize that we come to understand who we are in large part through the advice and recommendations of others. For instance, parents often have a perception of their children that, when tactfully communicated, can help the latter make important life decisions. Think of a mother who asks her proud and brash son: “I know that you want to be an oncologist, but how would you feel breaking bad news to patients several times per month?” or “How would you react to your scientific expertise being continuously challenged by skeptical patients?” To accommodate the Nietzschean critique, we can emphasize that authenticity is as relational as it is personal. Specifically, the picture we need to reject is one in which a person’s self-understanding can only be “the product of his presence to himself” (Williams 2022: 178). On the contrary, friends and relatives who know us well are a crucial source of self-knowledge, and listening to their advice can help us reflectively endorse desires that will lead us to flourish. To be our authentic selves, it helps to consider the recommendations of others. As we will see, these recommendations are also most useful when they are informative, that is, when we understand why things are recommended to us.

A comprehensive philosophical account of authenticity likely encompasses additional dimensions beyond volitional alignment and self-understanding. For instance, one could argue that children are authentic in a sense I have not discussed: they spontaneously express their thoughts and desires with little care for social conventions. Unlike many adults, they touchingly inhabit the world without worrying and wondering, “What will they think if I wear, do, or say this?” One could also claim that authenticity has a temporal dimension. To live an authentic life, it seems that a person must commit to particular desires and projects: a person who makes new decisions about who she wants to become every week would probably not strike us as authentic. Lastly, Charles Taylor (Reference Taylor1992) has argued that authenticity can only be envisioned as a moral ideal if the authentic person’s second-order desires relate to publicly shared values. This allows us to make sense of the idea that a life spent providing free education or healthcare to members of marginalized communities is more authentic than one devoted to counting blades of grass. While I find these claims plausible, they will not be at the forefront of my discussion in what follows.

2. Recommender Systems: An Overview

Recommender systems are attention-allocation engines that “help users to find items of interest in situations of information overload” (Jannach et al. Reference Jannach, Manzoor, Cai and Chen2022: 105:1). Considering the technological ease with which users can now create digital content, there is more content to be viewed than viewers available to consume it. As Tim Wu (Reference Wu2018: 548) underlines, in the digital public sphere, “it is no longer speech itself that is scarce, but the attention of listeners.” The primary goal of recommendation algorithms is to solve this problem: instead of drowning in a sea of digital content they would rather not see, users should be presented with speech, media, products, and profiles that are of interest to them. In the information age, recommendation algorithms filter content on a wide array of heavily used digital platforms, including social media sites used by billions of users daily. Considering the sheer magnitude and reach of social media platforms, my focus in what follows will be on the recommender systems that structure social media feeds.

As Narayanan (Reference Narayanan2023) notes, one mistake to avoid when describing social media platforms is to conceive of them as solely powered by recommendation algorithms. Such platforms rely on a suite of algorithms only some of which recommend content to users. Other algorithms process content, for instance, by automatically analyzing text or tagging images. When I speak of “recommendation algorithms,” I primarily have in mind those that deliver (i) user-generated content, (ii) targeted advertisements, and (iii) group or friend recommendations.

Thorburn, Bengani, and Stray (Reference Thorburn, Bengani and Stray2022) provide a useful four-stage description of how these algorithms work. The first stage is that of content moderation, in which pieces of content belonging to the total set of items that a user could view are automatically removed or flagged for violating a platform’s policies (also called “community guidelines”). The second stage is candidate generation. For each user, “the full set of items available on the platform (potentially millions) is efficiently filtered to a set of 500 or so that are plausibly of interest to the user.” Candidate generation is followed by ranking, during which each remaining item in the set is given a score based on how engaging it is predicted to be for the user. The last stage is re-ranking and focuses on relationships between items. For instance, if the best-ranked posts for a user all come from the same content creator, the recommender might re-rank them to ensure more diversity (which is typically instrumental to better long-term engagement). In summary, the final ranking of items (or “slate”) is the recommender’s attempt to determine what moderated items are most likely to be engaging for a given user in the present context. According to Thorburn, Bengani, and Stray (Reference Thorburn, Bengani and Stray2022), a simplified representation of the value model used to fulfill this task looks like this:

$$ {\displaystyle \begin{array}{l}\mathrm{Value}=1\hskip0.3em \mathrm{x}\hskip0.3em \Pr \left(\mathrm{like}\right)+3\hskip0.3em \mathrm{x}\hskip0.3em \Pr \left(\mathrm{comment}\right)+10\hskip0.3em \mathrm{x}\hskip0.3em \Pr \left(\mathrm{Share}\right)+4\hskip0.3em \mathrm{x}\hskip0.3em \left(\mathrm{expected}\ \mathrm{dwell}\ \mathrm{time}\right)\\ {}\hskip5em \hbox{--} 6\hskip0.3em \mathrm{x}\hskip0.3em \Pr \left(\mathrm{fake}\ \mathrm{account}\right)\hbox{--} 7\hskip0.3em \mathrm{x}\hskip0.3em \Pr \left(\mathrm{low}\hbox{-} \mathrm{quality}\ \mathrm{news}\right)\hbox{--} 10\hskip0.3em \mathrm{x}\hskip0.3em \Pr \left(\mathrm{clickbait}\right)\end{array}} $$

Here, the “linear combination of engagement predictions” —that is, the set of probabilities that relate to likes, comments, shares, and dwell time—can be described as the core ranking algorithm (Cunningham et al. Reference Cunningham, Pandey, Sigerson, Stray, Allen, Barrilleaux, Iyer, Milli, Kothari and Rezaei2024). In contrast, the full ranking algorithm typically includes probabilities that are not predictions but instead estimate the likelihood that an item belongs to a pre-defined category of content (e.g., fake account, low-quality news, or clickbait). In other words, “no real platform optimizes solely for engagement” (Thorburn, Bengani, and Stray Reference Thorburn, Bengani and Stray2022). Quality control is also part of the equation.

To rank items, most social media recommender systems attempt to answer the following question: “How did users similar to this user engage with posts similar to this post?” (Narayanan Reference Narayanan2023). Moreover, to establish similarity between users, the core ranking algorithm typically considers both demographic attributes (e.g., age, gender, geographical location, language, etc.) and networks (i.e., who follows whom or is friends with whom). That said, behavioral history remains the weightiest factor. In other words, what you view on social media largely depends on what items users categorized as similar to you have already engaged with. Like many other machine-learning algorithms, social media recommenders are thus actuarial in nature (Jorgensen Reference Jorgensen2022; Doyle Reference Doyle2024). The structure of the inferences they make is roughly the following: considering that users with property x have engaged with content of type y, this other user with property x is likely to engage with content of type y. Footnote ² As Doyle (Reference Doyle2024) notes, this means that algorithmic recommendation functions by including “data about other people from relevant reference classes.”

However, the actuarial reasoning used by social media recommenders does not perfectly match that of human actuaries. One main difference is that human actuaries rely on predefined, transparent categories. For example, most people understand that the annual premium for “23-year-old male drivers with one at-fault accident” will be higher than for “48-year-old female drivers with no at-fault accidents.” If they don’t, insurance companies can also explain to them why this is so, for instance, by mentioning that men tend to be involved in more accidents than women. In contrast, recommenders “automatically infer demographics like age, gender, and race as a side effect of looking for patterns in data” (Narayanan, Reference Narayanan2023). Unlike human actuaries, they are not instructed to use pre-defined categories. For example, in 2018, Black Netflix users reported that recommendations seemed intentionally tailored to their race; the movies recommended to them often featured multiple black characters. However, Netflix quickly clarified that it does not ask for its users’ race. In this case, racial targeting was an unintentional emergent effect of algorithmic recommendations: as always, the goal was to engage users by relying on whatever category worked best. If race works well, so be it, but if other, more abstract categories prove more effective at the predictive task, the recommender will use them instead. Consequently, “machine-generated categories may not correspond to any known social representation” (Milano, Taddeo, and Floridi Reference Milano, Taddeo and Floridi2020: 962). For instance, similarity in age or gender might be less relevant to a recommender than the fact that a group of users recently purchased a novelty sweater (962). Lastly, current mainstream recommenders are notoriously opaque: as the categories they use are constructed as a side effect of looking for patterns in data, users and engineers rarely know what these categories are. In the Netflix case (like in all cases), we do not know with certainty on which categories the recommender relied. As we will see, this has the unfortunate effect of keeping users guessing why they are recommended certain content.

3. Modelled Preferences and Frustrated Desires

Two distinct bodies of literature can help us see how recommender systems threaten volitional alignment. First, some scholars argue that digital platforms powered by recommendation algorithms are fundamentally manipulative (Susser, Roessler, and Nissenbaum Reference Susser, Roessler and Nissenbaum2019; Benn and Lazar Reference Benn and Lazar2022). According to this perspective, manipulation amounts to a form of hidden influence that exploits users’ vulnerabilities to better steer their behavior towards the manipulators’ ends. To illustrate this point, Susser, Roessler, and Nissenbaum (2018: 6) discuss a leaked internal Facebook strategy document in which one can read that “By monitoring posts, pictures, interactions and internet activity in real-time, Facebook can work out when young people feel ‘stressed’, ‘defeated’, ‘overwhelmed’, ‘anxious’, ‘nervous’, ‘stupid’, ‘silly,’ ‘useless,’ and a ‘failure.’” Using this information, the company could then direct targeted advertisement at them, hoping that their insecurity would translate into clicks. As the authors note, such “manipulative influences thwart people’s capacity to form decisions they can recognize and endorse as their own” (4). Here, the idea is that manipulation results in volitional misalignment as it preys upon first-order desires that are disavowed by one’s second-order desires.

A second body of literature relates to digital addiction. For instance, Allcott, Gentzkow, and Song (Reference Allcott, Gentzkow and Song2022: 2427) contend that using social media platforms leads to self-control problems. According to their research, “people consume more [digital media] today than they would have chosen for themselves in advance.” The authors draw this conclusion from an experiment in which they allowed subjects to activate screen time limits that could not be easily overridden on their smartphones. They then found “clear evidence that people have self-control problems” (2427). Indeed, “78 percent of participants set binding limits and continued using them through the final weeks of the experiment.” They did so even if they were not offered any incentive. Overall, “the experiment reduced screen time by 22 minutes per day (16 percent) over 12 weeks.” Here, the link with the philosophical account of authenticity I have proposed above is quite straightforward. Frankfurt’s paradigmatic example of volitional misalignment is that of an unwilling addict whom he describes as a “passive bystander to the forces that move him” as he fails to control his addiction (1988: 22). Whether this person is addicted to nicotine or to the dopamine hit that comes from viewing one’s post go viral does not matter. All addictions threaten authentic agency by impairing people’s ability to act according to their second-order desires.

Discussions of online manipulation and digital addiction point to a deeper philosophical problem that deserves to be discussed: recommender systems foster volitional misalignment by design as they rely on behavioral signals that poorly correlate with their users’ second-order desires. As we have seen, signals like clicks, likes, comments, and dwell time are the critical drivers of algorithmic recommendation on social media platforms (Stray et al. Reference Stray, Halevy, Assar, Hadfield-Menell, Boutilier, Ashar and Beattie2022). In other words, what users are recommended on such platforms largely depends on what they have done while connected to the internet. However, as many have noted, how people behave is a poor indicator of their second-order desires. In the 1970s, Amartya Sen (Reference Sen1973; Reference Sen1977) famously emphasized this point by arguing that a person’s preferences cannot successfully be inferred from their actions. This is because people’s mental lives are complex and involve unobservable desires. For instance, whether a smoker is peacefully enjoying a well-deserved reward or feeling ashamed to have bought yet another pack is not something that an observer can tell by seeing them light up a cigarette. To infer people’s underlying preferences, we need more than the “mere observation of actual choices” (Sen Reference Sen1977, 342); what we need is “other sources of information, including introspection and discussion.”

Such a critique easily applies to recommender systems. To see this, imagine that I scroll down a social media feed for a few seconds, skipping over video x and then stopping to watch video y for twelve seconds. Both videos were designed to catch my attention, but only y succeeded. As a result, I am likely to be recommended more videos similar to y in the future. Here are some possible descriptions of the situation:

• • I watched y as I generally enjoy watching videos like y, and I would like to be recommended similar videos in the future.

• • I only watched y as it was eye-catching, and I have no second-order preference between videos like x and those like y.

• • I only watched y as it was eye-catching. I believe watching videos like y is a waste of time, and I would rather be recommended videos like x.

• • I only watched y as it was eye-catching. I believe watching videos like x and y is a waste of time, and I would rather be recommended videos of kind z.

• • I did not watch y. I stopped browsing as my spouse was talking to me.

• • I hate-watched y, and I do not want to be recommended videos like y it in the future.

A recommender system that merely considers behavioral signals cannot distinguish between these possibilities. Furthermore, it makes recommendations based on forms of behaviors that – like watch time or dwell time – are not the result of carefully considered decisions. When a flashy video catches my eye and I spend a few seconds viewing it, I do not reflectively endorse my decision to watch it. Nevertheless, “in an interpretative move inherited from behaviorism,” recommenders take such behavioral signals “as more truthful than users’ explicit ratings” (Seaver Reference Seaver2019: 430).

To sum up, current recommenders cannot track their users’ second-order desires as they primarily rely on behavioral data from which such desires cannot be inferred. As a result, they foster volitional misalignment by design. Schuster and Lazar (Reference Schuster and Lazar2024) forcefully illustrate this objection:

A recommender that optimizes for volitional alignment would need better sources of information than behavioral signals. In section 5, I will discuss several technological changes that could prevent recommenders from fostering volitional misalignment by modelling user preferences in a more sophisticated manner. Specifically, I will contend that an authenticity-enhancing recommender could hardly avoid explicitly asking its users what their recommendation preferences are.Footnote ³ Moreover, as I would now like to suggest, it would also explain to them why they are exposed to the content that they see.

4. Listening to Recommenders

Let me open this section with a hypothetical scenario:

As it turns out, such a scenario is less hypothetical than it appears at first glance. Indeed, many social media users report feeling “seen” by recommendation algorithms. Consider, for instance, the story of BBC journalist Ellie House, who realized that she was bisexual after “getting more and more recommendations for series with lesbian storylines, or bi characters” on Netflix and being suggested a “playlist described as ‘sapphic’ on Spotify.” (House Reference House2023).

Through algorithmic filtering, Hao and House came to understand something important about themselves. Reflecting upon the content displayed to them, they envisioned the recommender’s “testimony” as a valuable source of self-knowledge and used it to construct their social identity. This represents a significant way in which algorithmic filtering can help users become their authentic selves. Remember indeed that the account of authenticity I proposed above is relational, that is, undergirded by the claim that testimony from others is just as valuable as introspection when it comes to self-discovery. My suggestion is that this point also applies to algorithmic recommendations. Certainly, coming to see that I behave similarly to people belonging to category x—say, members of the LGBTQ+ community or people on the ASD spectrum—is a significant way in which a person can come to understand who they are.Footnote ⁴ When recommenders help us do this, they foster self-understanding in a valuable manner. Of course, there are important differences between human recommendations and automated ones. In the scenarios above, the recommender did not provide Hao and House with advice but simply drew their attention to specific pieces of content. It is only by reflecting upon their feed that Hao and House drew conclusions about themselves and decided to explore their bisexuality. Ultimately, the recommendation to behave in this manner came from themselves. Still, it was prompted by information that the recommender delivered to them. Moreover, what our ‘data exhausts’ reveal will likely differ from what we consciously disclose to our friends and relatives. If this is so, then the information that algorithmic filtering provides can complement the recommendations we receive from our close friends. Having a hard talk with your sister or best friend is certainly an excellent way of holding up a mirror and gathering information about the “real you,” but the picture of you that they hold in their mind is likely incomplete. Different people—or systems—see different things in us.

Perhaps I am moving too quickly. Hao and House treated algorithmic filtering as a source of self-knowledge, but is this appropriate? Can recommenders also pose risks to self-understanding? Recently, Casey Doyle (Reference Doyle2024) has argued that there is something wrong with “listening to algorithms.” In his view, “To outsource inquiry about our minds to algorithms is to surrender the job of making up our own minds, which underlies the practice of first-person authority.” To illustrate this claim, Doyle draws our attention to the Amazon Halo health band, which makes predictions about its users’ emotional states based on the tone of their voice. Imagine, for instance, that my Halo band informs me that I am angry. Doyle contends that it would be inappropriate for me to defer to it without any reflection (“I’m angry, I tell you, my band has informed me so.”)

Here is the reasoning that undergirds Doyle’s argument. As we have seen, machine-learning algorithms make actuarial generalizations about groups to which users belong. However, “to rely on actuarial reasoning about one’s own or another’s mind fails to respect the fact that it is up to the subject herself what she believes, desires, and intends to do” (Doyle Reference Doyle2024). To make his case, Doyle draws from the work of scholars who worry that using predictive algorithms in criminal law amounts to failing to treat people as free autonomous agents.Footnote ⁵ Again, this is because such algorithms rely on actuarial reasoning: “The worry is that decisions based on this sort of evidence amount to ‘guilt by membership in a reference class.’” According to Doyle, a similar worry applies in the Halo band case. As he puts it, “In both cases, we feel entitled to be treated as an exception to generalizations about groups of which we are members.”

Yet, there are relevant dissimilarities between the three cases at hand: (i) Hao and House’s reliance on social media feeds to gather self-knowledge, (ii) the Amazon Halo band, and (iii) the use of algorithmic predictions in criminal law. First, neither Hao nor House defers to a recommender system without reflection. They do not believe that they are bisexual because the algorithm has told them so. Instead, they use algorithmic recommendations to raise questions that the recommender cannot answer for them: “Could I be bisexual?”; “Am I more similar to other members of the LGBTQ+ community than I thought?”; “Should I explore my bisexuality?” etc.

This difference matters. As Doyle (Reference Doyle2024) notes, his argument only applies to “cases of pure deference” in which a person takes an algorithmic recommendation or decision “at face value, by contrast with taking it into consideration along with the rest of one’s evidence.” Second, contrary to Doyle, contemporary decision theorists have argued that engaging in actuarial reasoning to gather information about oneself and make important life decisions is a rational way to behave (Pettigrew Reference Pettigrew2019: 136-138). For instance, to decide whether I should have a child, it seems appropriate to gather information about whether people whose lives and personalities share many similarities with my own have a positive experience of parenthood.Footnote ⁶ If this is so, then similar cases of actuarial reasoning are arguably not inappropriate, and the fact that recommenders rely on such reasoning is not a sufficient reason to believe that they cannot facilitate self-discovery. Hao and House can go ahead and think that, based on the behavioral history they share with members of the LGBTQ+ community, they might enjoy exploring their sexuality.

There is, however, a second reason to worry that recommenders might prevent Hao and House from gathering knowledge about themselves. Their reconstruction of why they are recommended LGBTQ+ content might be a figment of their imagination. Consider Hao’s case more closely. Certainly, the recommender’s ranking of items on his social media feed might be driven by the behavioral history he shares with other members of the LGBTQ+ community, but there is no way for him to tell. As we have seen in the case of Black movie recommendations on Netflix, racial or LGBTQ+ targeting is, at best, a non-intentional emergent effect of algorithmic recommendation optimized for engagement. In fact, the recommender system might be recommending LGBTQ+ content to him for reasons that have little to do with his own presumed queer behavior. Perhaps, for instance, the recommender “knows” that Hao and his wife live in the Castro—a San Francisco neighborhood densely populated by LGBTQ+ people—and displays LGBTQ+ content because of their geographical location. As Schuster and Lazar (Reference Schuster and Lazar2024) note, “recommender systems use deep learning with artificial neural networks to discover highly complex patterns among users’ behavior, features of content, and many other factors, which elude human analysis.” As a result, there is no way for Hao to understand why he is being recommended LGBTQ+ content, and his belief that he behaves like a member of the LGBTQ+ community might be false.

Will algorithmic recommendations necessarily prevent users from understanding who they are because it is fundamentally opaque? Perhaps not. After all, Hao already knows that he is bisexual and merely uses algorithmic recommendations to reflect upon what it means for him to be bisexual. He can also answer this question even if it turns out that he was primarily recommended LGBTQ+ content because of his geographical location. Yet, this should not lead us to understate how valuable it is for people to have access to informative recommendations when they attempt to make decisions that will lead them to flourish. People not only want to receive recommendations but also understand what motivates them. Imagine, for instance, that my partner asks me whether she should accept a new job. “You should,” I respond without elaborating. My recommendation is valuable to some extent. I have known my partner for fourteen years, and we have reasons to take each other’s advice seriously. Still, it would be better for me to respond, “You should; this is a managerial position, and I have always thought you have the qualities needed to be a great manager,” or “You should; you’ll cut your commute time in half.” When I make recommendations and explain why, my partner is in a better position to evaluate such recommendations and make decisions accordingly. That I see her flourishing in a managerial position might help her form the second-order desire to become one. By comparison, she might see my comment about her commute time as insignificant. In general, we are better positioned to gather self-knowledge when we understand why certain things are recommended to us, and this knowledge is useful when we must make important decisions.

Again, my suggestion is that this point applies to both human and algorithmic recommendations. To see this, consider the following variation on the Hao case:

As we did for Hao, we can easily picture a situation where Farah’s explanation of why she is recommended conservative content is a figment of her imagination. Let us say, for instance, that she lives in Whitechapel—a neighborhood in London heavily populated with Muslims—and geographical location is the primary reason why she is recommended such content. Like Hao, Farah might still flourish by redoubling her efforts to surround herself with progressive Muslims. Yet, such efforts might also make her unhappy. Regardless of their outcome, they are motivated by the wrong reasons: there is no point in attempting to overcome a part of yourself you dislike if that part is not there in the first place. Suppose Farah understood that the conservative content she saw was recommended to her based on her location rather than her behavior. In that case, she might not have prioritized her family relationships over her friendships. While Hao’s case illustrates the emancipatory potential of algorithmic filtering, Farah’s example helps us see that only explainable recommendations can fully realize this potential. Algorithmic filtering naturally makes users wonder why they are recommended what they see, but obscure recommendations risk misleading them into entertaining delusions about themselves. This claim is supported by the scientific literature on algorithmic folk theories, which emphasizes that users spontaneously develop their own, often inaccurate, theories of how opaque recommenders work to make sense of their social media experiences (Eslami et al. Reference Eslami, Karahalios, Sandvig, Vaccaro, Rickman, Hamilton and Kirlik2016; DeVito et al. Reference DeVito, Birnholtz, Hancock, French and Liu2018; Karizat et al. Reference Karizat, Delmonaco, Eslami and Andalibi2021). Simply put, users badly want to understand why they are recommended particular content, and they often make mistakes when they attempt to explain why they are. While algorithmic filtering can facilitate self-understanding, opacity remains a significant obstacle.

5. Optimizing for Authenticity

The practical upshot of my reflection is that we need better recommenders to be our authentic selves. First, recommender systems could facilitate volitional alignment by offering users more control over what they see. Proposals in this direction range from minor adjustments to the attention economy to more ambitious reforms enabled by large language models. Regarding minor changes, digital platforms could solicit more feedback from users by asking them to complete surveys (“Would you like to see more diverse content?” “Are you using social media to keep up with the news?” etc.) (Cunningham et al. Reference Cunningham, Pandey, Sigerson, Stray, Allen, Barrilleaux, Iyer, Milli, Kothari and Rezaei2024). They could then use survey responses to personalize filtering. Of course, the effectiveness of such measures will depend on several factors, including whether users are being asked the right questions, how often they respond to surveys, and to what degree engagement optimization is modified to reflect users’ explicit preferences. Alternatively, users could have a better opportunity to choose between different algorithmic feeds. For instance, Fukuyama (Reference Fukuyama2021) has recently defended the implementation of “middleware,” that is, content-curation services that would offer users “feeds curated according to alternate ranking, labelling, or content-moderation rules” (Keller Reference Keller2021: 168). Here, the goal is to create a new market of digital services that users could “plug in” their social media accounts to personalize their algorithmic feed: “In place of a nontransparent algorithm built into the platform, you could decide to use a filter provided by a nonprofit coalition of universities that would vouch for the reliability of data sources” (Fukuyama Reference Fukuyama2021: 42). A more radical proposal would be to replace current recommendation algorithms with conversational recommenders, that is, software systems that support their “users in achieving recommendation-related goals through a multi-turn dialogue” (Jannach et al. Reference Jannach, Manzoor, Cai and Chen2022: 105:3). In broad strokes, the idea is to integrate algorithmic recommendations with large language models, enabling users to continuously interact with their recommender (“Could you make it so that I see more LGBTQ+ content, please?”). Relatedly, Schuster and Lazar (Reference Schuster and Lazar2024) favor the creation of LLM-powered “generative agents” that would function “as the executive control center of a multifaceted system” and serve as “digital content sommelier or attention guardian.” Digital assistants could be built into the operating systems of personal computers, gather information about the kind of experience users seek, and eliminate the need to log in to social media by directly providing them with content that matches their explicit preferences. However, one central challenge recommender controls face is that users often do not wish to express their preferences. As Stray and his collaborators explain (2022), “Even when controls are provided, many users […] simply don’t see the value in engaging with them.” As a result, a passive experience of algorithmic recommendation “remains the default.” That said, it is worth noting that this empirical finding only applies to controls currently implemented by social media platforms (e.g., “See fewer posts like this”), not to more ambitious proposals, such as middleware, conversational recommenders, or generative agents.

Note also that there is a potential tension between the quest for self-understanding and the desire for users to exercise greater control over recommender systems. In section 1, I suggested that authenticity not only requires alignment between our current first- and second-order desires, but also the discovery of what our second-order desires are. Is there not a risk that offering users more control over recommenders will hinder self-understanding by preventing them from identifying their second-order desires? Imagine a world in which Ellie House exercises so much control over her algorithmic feeds that she is never recommended the “sapphic” playlists that sparked a reflection on her sexual orientation. Perhaps, for instance, redesigned recommenders never suggest a playlist before explicitly asking her what she would like to listen to, in which case she is unlikely to request sapphic music. In general, will implementing controls not diminish the likelihood of encounters that prompt users to question or revise their current desires?Footnote ⁷

The risk is real, but it can be mitigated. In House’s case, what sparked self-understanding is the realization that a recommender system had categorized her as behaving like members of a particular group. In general, user controls only risk hindering self-understanding if they render categorizations that involve the construction of similarity relationships obsolete or invisible. For instance, if I tell a conversational recommender that I want to be shown ten random cooking articles from The New York Times daily, the recommender does not need to construct similarity relationships to accomplish this task. That said, we can design user controls that (i) do not eliminate the construction of similarity relationships and (ii) make them explicit. For instance, survey questions could be phrased to help users see what similarity relationships recommenders construct (“We have noticed that you often engage with content popular with the LGBTQ+ community. Would you like to see more content of this kind?”). Furthermore, conversational recommenders could be programmed to construct such relationships and provide users with information about them (“Would you like to know more about the characteristics and interests of users who, like you, enjoy The New York Times cooking articles?”). In other words, at least some user controls can provide users with opportunities for self-discovery. Yet, the potential tension between volitional alignment and self-understanding should not be dismissed too quickly. If we forget that recognizing who we behave like can facilitate self-understanding, there is a risk that we will design controls that prevent users from reaching this kind of realization.

As we have seen, volitional alignment is also not all there is to authenticity. The challenge for Farah was not that algorithmic recommendation frustrated her second-order desires, but rather that it misled her into making a wrongful assumption about herself. A straightforward solution to this problem is the implementation of explainable recommenders, that is, recommendation engines that explain to users the motivations behind their recommendations (Zhang and Chen Reference Zhang and Chen2020). Such recommenders are based on “explainability-aware ML techniques” and can be categorized into two main groups (Marconi, Matamoros A., and Epifania, Reference Marconi, Ricardo A. Matamoros and Epifania2022). First, some black box recommenders still provide users with post hoc explanations of their output. Yet, they do so without offering “an in-depth understanding of the underlying algorithm.” Second, so-called white box recommenders directly incorporate interpretable machine learning models into algorithmic filtering. Often called “explainability-by-design,” this second approach avoids the challenges of interpreting opaque recommenders already in use (Stray et al. Reference Stray, Halevy, Assar, Hadfield-Menell, Boutilier, Ashar and Beattie2022).

Unfortunately, explainable recommenders face many challenges. First, discussions of explainable AI tend to focus on predictions that lead to the most straightforward exercise of algorithmic power over individuals (Vredenburgh Reference Vredenburgh2022). Think, for instance, of credit-scoring algorithms (Hurlin, Pérignon, and Saurin Reference Hurlin, Pérignon and Saurin2022) or risk predictions used to justify the denial of pre-trial release or parole (Angwin et al. Reference Angwin, Larson, Mattu and Kirchner2016). As these predictions relate to the distribution of societal benefits and burdens, they pose a clear risk to fairness. By way of contrast, recommendation algorithms impact their users in more subtle ways. As a result, they are less frequently the object of philosophical reflections that focus on transparency.Footnote ⁸ Second, the technical means necessary to explain the most sophisticated recommender systems are still missing: “With the rise of increasingly complex machine learning models, it has also become increasingly difficult to give intuitive and understandable explanations of why a user received a specific recommendation” (Stray et al. Reference Stray, Halevy, Assar, Hadfield-Menell, Boutilier, Ashar and Beattie2022). A third challenge to developing explainable recommenders is that there is no explicit agreement on (i) the conditions that must be met for an AI model to count as explainable and (ii) why explainability is valuable (especially when no apparent risk to fairness is at play). The preceding discussion offers elements of an answer to these questions. Understanding why people or algorithms recommend things to us can help us form a more accurate picture of who we are. Narayanan’s (Reference Narayanan2023) characterization of social media recommenders also provides a partial answer to the question of what explainable recommendations should consist of. To reach a higher level of self-understanding, it should be explained what is meant by “similar” in expressions like “similar users” and “similar content.” For Hao, an explanation such as “You’re viewing LGBTQ+ content as users like you have engaged with similar content” is confusing. To calibrate his reaction to the recommender’s testimony, he must know whether the expression “users like you” is understood in behavioral or geographical terms (or, if both factors are relevant, how weighty each of them is). To foster self-understanding, explainable recommenders must allow the average user to comprehend which similarity relationships the model constructs.