Paper 205: Cultural Studies

Hello everyone this blog is part of an assignment paper 205: Cultural Studies, assignment topic is 'Algorithmic Consumer Culture'

#Academic Details:

Name:- Khushi Goswami

Batch:- M.A.Sem 3 (2024-2026)

Enrollment no:- 5108240001

E-mail Address:- khushigoswami05317@gmail.com

Roll no:- 8

#Assignment Details:

Paper no:205 A

Paper Name: Cultural Studies

Subject Code:22410

Topic: Algorithmic Consumer Culture

Submitted to: Department of English, MKBU

Abstract:

The paper argues that algorithms and machine-learning systems have become central mediators of modern consumption. It introduces the concept of algorithmic articulation to explain how automated systems shape cultural meanings, consumer choices, and market representations. The article identifies four key properties of algorithms — opacity, authority, non-neutrality, and recursivity — and explains how these properties operate at three analytical levels: the individual, the collective, and the market. The assignment summarises the article, explains its framework, gives examples of control and resistance in algorithmic settings, and offers a critical evaluation. Short quotations from the original paper are used to support key points.

Keywords: algorithmic mediation, algorithmic articulation, opacity, recursivity, consumer resistance, platform economy.

Introduction

Introduction:

The rise of digital platforms, big data, and machine learning has significantly changed how people discover, evaluate, and consume goods and cultural products. Airoldi and Rokka (2022) ask a central question: to what extent do algorithms simply assist human choices, and to what extent do they actively shape and constitute those choices? The authors aim to reconcile two seemingly opposite views found in literature: (1) that digitalization liberates consumers, making consumption more fluid and individualized, and (2) that data-driven marketing disempowers consumers by subjecting them to surveillance and manipulation. Airoldi and Rokka propose a middle path: algorithms articulate consumption through a dialectical techno-social process that combines marketer control and consumer agency. This assignment explains their argument in accessible B2-level English, lays out the framework they propose, and evaluates the strengths and limits of the approach.

Abstract and Keywords (from the paper) — short note

1. Rising platform economy and “liquefying” consumer culture

Airoldi and Rokka begin by locating their study in two strands of scholarship. One strand (e.g., Bardhi & Eckhardt) describes consumption as increasingly liquid: access-based, ephemeral, dematerialized, and individualized. In this view, digital platforms supposedly empower consumers by giving them new ways to express identity and access products. The other strand (e.g., Zuboff, Darmody & Zwick) critiques the same processes as enabling surveillance, data extraction, and manipulative marketing. The authors argue that both views are partial. Rather than choosing one side, they suggest the need to understand how algorithmic systems mediate between emancipation and control. In other words, digitalization both creates new freedoms and creates new, often hidden, constraints.

Key idea: digital platforms enable new forms of consumer identity and access, but they also provide companies with tools for surveillance and targeted influence. The tension between empowerment and manipulation is a core puzzle the paper addresses.

2. A short history of algorithms and consumers

The article gives a concise history of algorithms to show how their role changed from simple computational tools to powerful cultural mediators. Early algorithms were mathematical rules and manual procedures. With electronic computers and the Web, algorithmic systems became embedded in search, recommendation, and advertising. The Web 2.0 era produced large quantities of user-generated data that companies used to tailor services and ads. The real turning point, according to the authors, is the rise of advanced machine learning: algorithms that “learn” from data rather than only following prewritten rules. Through smartphones and IoT, algorithms now receive rich data streams and influence everyday life in real time. This historical account helps explain why algorithms now matter not only technically but culturally — they shape how choices appear and how tastes are formed.

3. Algorithms, culture and power: four properties

Airoldi and Rokka identify four main dimensions through which algorithms affect culture. Each dimension helps explain different ways algorithms matter.

3.1 Opacity

Algorithms and their data practices are often hidden. Companies keep code and models secret for commercial reasons, and many machine-learning methods (like deep neural networks) are hard to interpret even for their creators. Opacity means users rarely know why they see certain recommendations or ads. This secrecy makes it hard for consumers and scholars to fully assess how decisions are made and how power is exercised. The authors stress that opacity is not only corporate secrecy but also computational complexity.

3.2 Authority

Algorithms gain authority when their outputs are treated like neutral or expert judgments. For example, recommendation lists or search result rankings are often accepted as objective. This perceived authority allows algorithms to shape what people consider normal or desirable. The authors note that algorithmic authority reconfigures agency: consumers' choices are partly shaped by automated systems that act as powerful non-human actors.

3.3 Non-neutrality

Although algorithms appear objective, they embed cultural assumptions and biases present in their data and design. Training data, human labeling, and design choices all carry values and social inequalities. The paper underlines how these biases can reproduce and amplify stereotypes (for instance, racial or gender biases in search outputs). Thus, algorithms are not neutral tools but sociotechnical assemblages shaped by historical and social contexts.

3.4 Recursivity

Recursivity describes feedback loops where algorithmic outputs influence human behavior, and that behavior becomes future input. For example, recommendation systems suggest content; users consume and generate data; the system learns and amplifies certain patterns. Over repeated iterations the algorithm and users co-shape each other. Recursivity explains how certain cultural forms become normalized by repeated algorithmic reinforcement.

4. The framework: algorithmic articulation and the circuit of culture

To bring these ideas together, Airoldi and Rokka adapt the circuit of culture (du Gay et al.) — a model that examines production, representation, consumption, identity, and regulation — and insert algorithms as non-human articulators operating between production and consumption. They propose the new term algorithmic articulation: a dialectical techno-social process in which algorithms assemble, order, and make visible certain cultural items, meanings, and consumer choices. This process is dynamic: algorithms are fed by consumer data, produce outputs (recommendations, rankings, ads), and are updated, creating recursive loops. The authors present two central claims: Algorithmic articulation explains both marketer control and spaces of consumer resistance, without reducing the situation to either full control or full emancipation.

The circuit model is still useful but needs to account for the speed, opacity, and non-human role of algorithms in articulating meaning.

Airoldi and Rokka provide a diagram (Figure 1 in the paper) showing the recursive loop: consumer inputs → algorithmic black box → algorithmic outputs (nudges, recommendations) → consumer actions → new data inputs. The loop is continuous and accelerates cultural articulation.

5. Algorithmic articulation in practice: Control and resistance at three levels

The authors analyze algorithmic articulation at three analytical levels: individual, collective, and market. For each level they highlight how algorithms can control and how consumers can resist.

5.1 Individual level — encoding / decoding

At the individual level, algorithms personalize content and ads based on data. Marketing practices like hypernudges use deep personalization to steer choices. The authors call attention to how algorithms can encode a person’s predicted identity and preferences, shaping their experience silently. Yet consumers are not helpless: many attempt to decode or make sense of algorithms, for example by noticing patterns, using ad blockers, or changing settings. Still, such resistance often requires digital skills and awareness, which are uneven across populations. The article notes that even small acts (skipping a song or hiding an ad) add feedback that can change algorithmic behavior later.

5.2 Collective level — programming / hijacking

At the collective level, algorithms shape social visibility and interactions. Platform logics pre-filter who we see and what topics trend. These dynamics can lead to echo chambers or programmed sociality. However, groups can hijack algorithmic systems, using platform mechanics to amplify certain content (for example, fandoms coordinating hashtags to make content trend). The authors mention real cases — such as coordinated fan actions on Twitter — showing that collective action can exploit algorithmic recursivity to produce outcomes that oppose commercial aims. The GameStop subreddit movement (cited in the paper) is another example where a distributed crowd temporarily overturned algorithmic expectations in financial markets. The collective level thus contains both programming and possibilities for bottom-up resistance. 

5.3 Market level — representing / contesting

On the market level, algorithms classify and rank cultural representations, shaping consumer imaginaries. Search engines, recommendation systems, and ranking metrics influence what becomes visible or invisible. The paper references Algorithms of Oppression (Noble) to show how search results can reproduce racial and gendered biases. Consumers and activists can contest market-level algorithmic representations — for example, using SEO and public campaigns to change search results (the “World White Web” project is an example mentioned). Market-level contestation requires different resources and tactics than individual resistance, but it shows how algorithmic systems can be politically engaged.

6. Contributions, implications, and future research directions Airoldi and Rokka make several contributions:

Conceptual clarity: They offer a clear term — algorithmic articulation — to describe how algorithms mediate cultural processes.

Dialectical framing: The framework resists simple binaries (empowerment vs control) by showing how both occur together through recursive loops.

Analytical levels: Breaking down effects into individual, collective, and market levels provides a useful map for future empirical work.

The authors call for more empirical studies, ethnographies of algorithms, and methods that can capture opaque and dynamic systems.

Implications include the need to study algorithms as cultural actors, to examine the unequal distribution of algorithmic awareness, and to investigate how algorithmic practices transform concepts like taste, identity, and solidarity. The paper also raises ethical and policy questions about transparency, accountability, and bias in platform systems.

Critical Evaluation (strengths and limitations)

Strengths

Balanced perspective: The major strength of this article is its balanced approach. It neither uncritically praises digital platforms nor simply condemns them; instead, it offers a nuanced account that captures both constraints and opportunities for resistance.

Clear framework: The idea of algorithmic articulation builds on established cultural theory (the circuit of culture) and updates it for the algorithmic age. This makes the concept familiar and theoretically grounded while being original.

Conclusion

Airoldi and Rokka (2022) offer an important conceptual contribution to the study of contemporary consumption. By introducing algorithmic articulation, they help bridge the gap between optimistic accounts of digital empowerment and critical accounts of surveillance and control. The four properties they identify — opacity, authority, non-neutrality, and recursivity — provide strong analytical tools for understanding how algorithms shape taste, visibility, and behavior. The three-level analysis (individual, collective, market) clarifies where control and resistance occur and suggests directions for future empirical work. Although mainly conceptual, the article is a timely and useful map for scholars who want to study the cultural effects of algorithms and platform infrastructures. It shows that algorithms are not mere tools but active participants in the circulation of cultural meanings.

References:

Airoldi, M., & Rokka, J. (2022). Algorithmic consumer culture. Consumption Markets & Culture, 25(5), 411–428. https://doi.org/10.1080/10253866.2022.2084726