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The Behavioral Code: Recommender Systems and the Technical Code of Behaviorism

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The Necessity of Critique

Part of the book series: Philosophy of Engineering and Technology ((POET,volume 41))

Abstract

Our lives are increasingly mediated, regulated and produced by algorithmically-driven software; often invisible to the people whose lives it affects. Online, much of the content that we consume is delivered to us through algorithmic recommender systems (“recommenders”). Although the techniques of such recommenders and the specific algorithms that underlie them differ, they share one basic assumption: that individuals are “users” whose preferences can be predicted through past actions and behaviors. While based on a set of assumptions that may be largely unconscious and even uncontroversial, we draw upon Andrew Feenberg’s work to demonstrate that recommenders embody a “formal bias” that has social implications. We argue that this bias stems from the “technical code” of recommenders – which we identify as a form of behaviorism. Studying the assumptions and worldviews that recommenders put forth tells us something about how human beings are understood in a time where algorithmic systems are ubiquitous. Behaviorism, we argue, forms the episteme that grounds the development of recommenders. What we refer to as the “behavioral code” of recommenders promotes an impoverished view of what it means to be human. Leaving this technical code unchallenged prevents us from exploring alternative, perhaps more inclusive and expansive, pathways for understanding individuals and their desires. Furthermore, by problematizing formations that have successfully rooted themselves in technical codes, this chapter extends Feenberg’s critical theory of technology into a domain that is both ubiquitous and undertheorized.

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Notes

  1. 1.

    As such, behaviorists deny the Cartesian mind-body dualism.

  2. 2.

    Skinner preferred to avoid mental concepts, but the underlying idea of (analytical or logical) behaviorism is that a mental state or condition is the idea of a behavioral disposition or family of behavioral tendencies (Graham, 2019). This means that a behaviorist can in principle continue to use mental concepts, but they would refer to a certain behavioral disposition rather than inner states.

  3. 3.

    Even though developers and behaviorists work with different motivations – developers work according to a commercial incentive while behaviorists are motivated by a certain ideal of “real” science – they eventually both aim for the prediction and control of human behavior. These goals have proven to be greatly compatible; the founder of behaviorism, John B. Watson, joined an advertising agency after he left academia and became highly successful in that field (Baars, 1986; Waldrop, 2001). In addition, Skinner’s analysis has been called the psychological equivalent of wage-labor capitalism (Baars, 1986), as the prediction and control of human behavior in order to increase productivity has been a central focus of managerial practices; from “scientific management” to “nudge management” more recently (Ebert & Freibichler, 2017).

  4. 4.

    Apart from the praxis critique, there are roughly three main reasons for the rejection of behaviorism within philosophy (Graham, 2019). First of all, many people were, and still are, sceptical about behaviorism’s commitment to the thesis that behavior can be understood without referring to mental processes. A second reason for the dismissal of behaviorism is the existence of “qualia” (e.g., Place, 2000): behaviorism cannot account for the qualitatively distinctive experience underlying overt behavior. Yet another critique came from Noam Chomsky (1967 [1959]). According to Chomsky, behaviorism cannot account for the fact that language does not seem to be learned through explicit teaching. He pointed out that linguistic performance outstripped individual reinforcement histories.

  5. 5.

    Notice here that behaviorism is a presupposed framework rather than a scientific theory, meaning that it cannot be falsified by any experimental results (Baars, 1986).

  6. 6.

    While the purpose of this chapter is not to explore solutions, there are several interesting proposals and projects underway. For example, academics and developers have called for and experimented with more user-centric recommenders that allow users some degree of control over how they are profiled. One example of user-centric design is gobo.social, a social media news aggregator designed by the MIT Media Lab. This tool offers sliders that users control in order to filter information: The user can explore a range of political perspectives on a continuum from left to right, or “the extent of seriousness, rudeness, gender, and other parameters” (Reviglio & Agosti, 2020, p. 6). In another example, Harambam et al. (2018) provide an interesting proposal to grant users greater “voice” in our algorithmically-driven media ecosystem. The authors propose the creation of algorithmic recommender personae to “allow people instead to demand from [recommenders] to behave in ways that align with their own specific... interests at each single moment” (ibid. p. 4). It is also possible to involve users in the earliest stages of the design and development of recommender algorithms. The benefits of participatory design are not only in creating more user-friendly technologies, but also in making “explicit the critical, and inevitable, presence of values in the system design process” (Suchman, 1993, p. viii). As Feenberg convincingly argues in Questioning Technology, by widening opportunities to intervene, user participation in design serves to limit “the operational autonomy of technical personnel” (Feenberg, 1999, p. 135) who are socialized into the technical codes of the profession (ibid, p. 142).

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Author information

Authors and Affiliations

  1. Philosophy Department, University of Groningen, Groningen, Netherlands

    Marit de Jong

  2. Department of Media Studies and Journalism, University of Groningen, Groningen, Netherlands

    Robert Prey

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  1. Marit de Jong
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Correspondence to Marit de Jong .

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  1. Philosophy Department, Maastricht University, Maastricht, The Netherlands

    Darryl Cressman

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de Jong, M., Prey, R. (2022). The Behavioral Code: Recommender Systems and the Technical Code of Behaviorism. In: Cressman, D. (eds) The Necessity of Critique. Philosophy of Engineering and Technology, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-031-07877-4_8

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