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A New Vernacular of Algorithms

Towards a critical definition of algorithms that incorporates both their computational, and social understandings into a new vernacular. Peer review carried out by Dr Tanya Kant.

Published onJul 10, 2023
A New Vernacular of Algorithms
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What is an Algorithm?

One of the challenges that I have faced through the course of my research into migration algorithms is finding a consistent definition of algorithms. Asking workshop participants or audience members at my talks what an algorithm is, they usually respond with the idea of a personalised program that learns their preferences and adapts online content and experiences to their unique tastes. This means that for a majority of people, algorithms are proprietary machine learning (ML) software connected to major technology companies. A YouTuber, for instance, might say ‘please like my video and subscribe to my channel for the YouTube algorithm to push my content’. The use of the word ‘algorithm’ in this context, is a personalised recommendation program that can be hacked through calculated engagement. This understanding of an algorithm bears paradoxes of user agency. The contradiction here is that although individuals are supposedly responsible for the recommendations they see, the commercial proprietors of the applications deploy a set of rules that prioritises specific content. In this blog, I want to think aloud about a critical definition of algorithms that incorporates both their computational, and social understandings into a new vernacular.

Computational Definitions

To keep the definition accessible to the public, I often use the definition of an algorithm as a food recipe. This metaphor of ‘algorithm as recipe’ is common in public education resources such as (BBC Bitesize, no date). The free study support for pre-Higher-Education learners compares the process of cooking to the process of programming. Similar to a how a chef would list out ingredients and follow sequential actions needed to cook a dish, a programmer would list out a sequence of instructions to a computer. With this metaphor of algorithm as recipe, there is a consolidation of the idea that algorithms are “step-by-step process for solving a problem” (BBC Bitesize, no date, para 1). Algorithms are the minutia of computation. They are designed to search files, to compress images, to calculate the best route to location. They are not limited to machine learning or social media.

While the aforementioned definition is useful for a person with no programming experience, it is very different from what a computer science student might know as an algorithm. CS50, the popular Computer Science course run by Harvard introduces students to algorithms by focusing on key problems in computing (Malan, 2022). Algorithms in this class are established solutions within the discipline—bubble sort, merge sort, linear search—to the computational problem of sorting and searching. As with disciplines in the Arts and Humanities, the discipline of Computer Science comes with its own core set of knowledge and practice. In Computer Science, understanding an algorithm means to understand the most efficient way to solve a problem. In this sense, the question of “what is an algorithm” combines both the knowledge of computational rules and efficiency.

9 panel of Kronk from the cartoon, The Emperor's New Groove. Organised in blocks of  3 by 3, each panel in the gif starts off with a pixel distortion that animates into a final legible image of Kronk with a cheeky smile. Above each panel is the name of the sorting algorithms. Listed out by top left as first and bottom right as last—Selection, Insertion, Heap, Bubble, Cocktail, Circle, Merge, Quick, Shell.

A programmer meme demonstrating different sorting algorithms

Critical and Social Definitions

While computing has its definitions of algorithms, in the social sciences and humanities the word takes on a different meaning. As the American Sociologist, Ruha Benjamin says:

[algorithms are] a set of instructions, rules, and calculations designed to solve problems. …Thus, even just deciding what problem needs solving requires a host of judgements; and yet we are expected to pay no attention to the man behind the screen (Benjamin, 2019, p. 6).

The field of Critical Algorithm Studies, as outlined by (Seaver and Gillespie 2015, para. 1), focuses on “algorithms as social concerns”. Critical Algorithm Studies is an interdisciplinary field with scholarship from sociology, media studies, communication studies, anthropology, history, critical and creative practice and more. One singular definition for such a wide range of disciplines would be limiting. For instance, as a creative practitioner, I am not only interested in the algorithm as a sequence of instructions but I am also interested in the visual representation of algorithms using flowcharts. In my most recent conference presentation, I demonstrate ways visual representations of algorithms map and render computational futures (Fubara-Manuel, 2023). I focus on algorithms used in migration. My approach is different from a policy standpoint that might audit the algorithm to see its everyday impact on migrants. While I may define algorithms as mapped out plans for computer programs, a policy expert may define an algorithm as an open process with recordable inputs and outputs. Both approaches investigate the impact of algorithms in migration but one investigates the inputs and outputs while the other explores a wider practice of design and iteration. In examining the wider practice of design, my work explores the everyday meaning-making and the power of algorithms to organize life. Due to the vast definitions of algorithms Bucher (2018) calls for an embrace of the multiplicity—the manyfoldedness—of algorithms.

A New Vernacular

Simply put, algorithms have multiple meanings. In embracing this multiplicity, I want to encourage an added usage and meaning of the term. I want to encourage a use of the word outside of social media contexts that acknowledges that algorithms are manyfold, affecting finances, housing, healthcare, transportation and so on. I call for everyday use of the word ‘algorithm’ to normalise a statement such as “this immigration algorithm is keeping my family down” as much as one could refer to the “Instagram Algorithm”. Such a vernacular usage of the word would reflect the ways “much of our lives [inside and outside of social media] are organized by algorithms” (Onuoha and Nucera, 2018, p. 31). It would reflect the ways automated decision-making, Machine Learning, and AI influence mundane life from visa processing to banking to connections we make on social media. It would bring algorithms to the forefront for interrogation and discussion at dinner tables where those who are most affected by them might build solidarity and challenge the social power of everyday algorithms.

The flowchart has a start node that leads to a "sum up points" node. Under the "sum up points" nodes is another node that keeps score, waiting for greater than or equal to 70 points. Under the ">= 70 points" node are other nodes listing criteria that adds 20 points based on a job offer, 20 points if the job is on the shortage list, 10 points if the visa applicant is an English speaker, 10 or 20 points if their salary is greater than £23,040 or £25,600, and 10 points for PhD education or 20 points if their PhD is in STEM. On the left of the job and language criteria node is link that checks for not eligible. If   the applicant does not speak English and does not have a job offer, they are immediately not eligible, which leads to a "do not apply" node which leads to a stop node that ends the flowchart. If the points are greater than or equal 70, they may return to the corresponding node, that leads them to an "eligible" box and an "apply" node that leads to the stop, closing the flow.

A flowchart I designed representing the UK Points-Based Immigration System as an algorithm.

CC BY-NC

References

BBC Bitesize (no date) “What is an algorithm? - Introducing algorithms - GCSE Computer Science Revision,” Bbc bitesize [Preprint]. https://www.bbc.co.uk/bitesize/guides/z22wwmn/revision/1.

Benjamin, R. (2019) Race after technology: abolitionist tools for the new jim code. Medford, MA: Polity.

Bucher, T. (2018) If.then: algorithmic power and politics. New York, NY: Oxford University Press (Oxford studies in digital politics).

Fubara-Manuel, I. (2023) “AI before the fact: Pre-Visualisation as Sociotechnical Worlding,” in Transforming Collections, Rewinding Internationalism. Van Abbemuseum, Eindhoven, 20-21 April.

Malan, D.J. (2022) “Week 3 - CS50,” This is CS50. Available at: https://cs50.harvard.edu/college/2022/spring/weeks/3/ .

Onuoha, M. and Nucera, D. (2018) A people’s guide to AI: artificial intelligence. Michigan: AMP.

Seaver, N. and Gillespie, T. (2015) “Critical Algorithm Studies: A Reading List,” Social media collective. Available at: https://socialmediacollective.org/reading-lists/critical-algorithm-studies/.

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