Editor in Chief’s Pick – Engines of Order: A Mechanology of Algorithmic Techniques

Rieder, Bernhard

doi:10.54533/stedstud.vol010.art12

Cited by 20 publications

(32 citation statements)

References 86 publications

(108 reference statements)

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“…In contrast to previous approaches in software studies that have focussed on foundational principles of computation (Fuller, 2008) or the analysis of concrete implementations of algorithms in the form of source code (Marino, 2020), we consider Transformer networks through the lens of algorithmic techniques (Rieder, 2020). The Transformer architecture was indeed developed with the goal to enable a language model to better process longer sentences in a shorter amount of time.…”

Section: Prehistory Of the Transformer Architecturementioning

confidence: 99%

“…In Rieder’s conceptualization, algorithmic techniques are technical schemata employed by programmers to respond to typical, widespread problems such as sorting a list or making a ‘good’ recommendation. From this perspective, the Transformer network architecture can be considered an algorithmic technique responding to a variety of language tasks, which, in its derivative and solidified form as a large language model, enters working systems and applications from diverse contexts: ‘Spelled out, stabilized, and ‘frozen’, algorithmic techniques spread through technical imaginaries and artefacts, and further into application logics and business models.’ (Rieder, 2020: 16). The third section discusses the role of LLMs in the political economy of AI.…”

Section: Introductionmentioning

confidence: 99%

“…This includes the extension of cloud computing platforms like Google Cloud, Amazon’s AWS, and Microsoft Azure. Enhanced by expenditures in data centres, specialized hardware such as processor chips, or undersea cables, these corporate clouds provide top-to-bottom solutions for data management, analytics, dynamic scaling, and machine learning that are ‘increasingly difficult to replicate without considerable investment’ (Rieder, 2020: 119). In a similar vein, Google has open-sourced its machine learning framework TensorFlow and released a powerful open-source language model called Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Second, we consider the historical development of the Transformer through a discussion of the key literature that has informed the invention of the architecture. Here, we loosely follow Rieder’s (2020) concept of algorithmic techniques to formulate ‘an approach to the analysis of software that sits between broad theorizing and the empirical investigation of concrete applications’ (Rieder, 2020: 100). In Rieder’s conceptualization, algorithmic techniques are technical schemata employed by programmers to respond to typical, widespread problems such as sorting a list or making a ‘good’ recommendation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The great Transformer: Examining the role of large language models in the political economy of AI

Luitse

Denkena

2021

Big Data & Society

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In recent years, AI research has become more and more computationally demanding. In natural language processing (NLP), this tendency is reflected in the emergence of large language models (LLMs) like GPT-3. These powerful neural network-based models can be used for a range of NLP tasks and their language generation capacities have become so sophisticated that it can be very difficult to distinguish their outputs from human language. LLMs have raised concerns over their demonstrable biases, heavy environmental footprints, and future social ramifications. In December 2020, critical research on LLMs led Google to fire Timnit Gebru, co-lead of the company’s AI Ethics team, which sparked a major public controversy around LLMs and the growing corporate influence over AI research. This article explores the role LLMs play in the political economy of AI as infrastructural components for AI research and development. Retracing the technical developments that have led to the emergence of LLMs, we point out how they are intertwined with the business model of big tech companies and further shift power relations in their favour. This becomes visible through the Transformer, which is the underlying architecture of most LLMs today and started the race for ever bigger models when it was introduced by Google in 2017. Using the example of GPT-3, we shed light on recent corporate efforts to commodify LLMs through paid API access and exclusive licensing, raising questions around monopolization and dependency in a field that is increasingly divided by access to large-scale computing power.

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Section: Prehistory Of the Transformer Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The great Transformer: Examining the role of large language models in the political economy of AI

Luitse

Denkena

2021

Big Data & Society

View full text Add to dashboard Cite

show abstract

“…However, such techniques don’t just homogenize. They ‘not only imply forms of standardization, homogenization and “commensuration” but also produce their own vectors of differentiation’ (2020: 122). The differences they propagate take real people as their objects, but do so in ways that are extremely difficult to reverse engineer, and, consequently, to resist.…”

Section: Racial Formations As Data Formationsmentioning

confidence: 99%

Racial formations as data formations

Phan

Wark

2021

Big Data & Society

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This commentary uses Paul Gilroy’s controversial claim that new technoscientific processes are instituting an ‘end to race’ as a provocation to discuss the epistemological transformation of race in algorithmic culture. We situate Gilroy’s provocation within the context of an abolitionist agenda against racial-thinking, underscoring the relationship between his post-race polemic and a post-visual discourse. We then discuss the challenges of studying race within regimes of computation, which rely on structures that are, for the most part, opaque; in particular, modes of classification that operate through proxies and abstractions and that figure racialized bodies not as single, coherent subjects, but as shifting clusters of data. We argue that in this new regime, race emerges as an epiphenomenon of processes of classifying and sorting – what we call ‘racial formations as data formations’. This discussion is significant because it raises new theoretical, methodological and political questions for scholars of media and critical algorithmic studies. It asks: how are we supposed to think, to identify and to confront race and racialisation when they vanish into algorithmic systems that are beyond our perception? What becomes of racial formations in post-visual regimes?

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The Algorithmic Imprint

Ehsan¹,

Singh²,

Metcalf³

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

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When algorithmic harms emerge, a reasonable response is to stop using the algorithm to resolve concerns related to fairness, accountability, transparency, and ethics (FATE). However, just because an algorithm is removed does not imply its FATE-related issues cease to exist. In this paper, we introduce the notion of the "algorithmic imprint" to illustrate how merely removing an algorithm does not necessarily undo or mitigate its consequences. We operationalize this concept and its implications through the 2020 events surrounding the algorithmic grading of the General Certificate of Education (GCE) Advanced (A) Level exams, an internationally recognized UKbased high school diploma exam administered in over 160 countries. While the algorithmic standardization was ultimately removed due to global protests, we show how the removal failed to undo the algorithmic imprint on the sociotechnical infrastructures that shape students', teachers', and parents' lives. These events provide a rare chance to analyze the state of the world both with and without algorithmic mediation. We situate our case study in Bangladesh to illustrate how algorithms made in the Global North disproportionately impact stakeholders in the Global South. Chronicling more than a year-long community engagement consisting of 47 interviews, we present the first coherent timeline of "what" happened in Bangladesh, contextualizing "why" and "how" they happened through the lenses of the algorithmic imprint and situated algorithmic fairness. Analyzing these events, we highlight how the contours of the algorithmic imprints can be inferred at the infrastructural, social, and individual levels. We share conceptual and practical implications around how imprint-awareness can (a) broaden the boundaries of how we think about algorithmic impact, (b) inform how we design algorithms, and (c) guide us in AI governance. The imprint-aware design mindset can make the algorithmic development process more human-centered and sociotechnically-informed. CCS CONCEPTS• Human-centered computing → Collaborative and social computing.

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Editor in Chief’s Pick – Engines of Order: A Mechanology of Algorithmic Techniques

Cited by 20 publications

References 86 publications

The great Transformer: Examining the role of large language models in the political economy of AI

The great Transformer: Examining the role of large language models in the political economy of AI

Racial formations as data formations

The Algorithmic Imprint

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