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Journal articles on the topic 'Algorithmic'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Bender, Sarah. "Algorithmic Elections." Michigan Law Review, no. 121.3 (2022): 489. http://dx.doi.org/10.36644/mlr.121.3.algorithmic.

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Artificial intelligence (AI) has entered election administration. Across the country, election officials are beginning to use AI systems to purge voter records, verify mail-in ballots, and draw district lines. Already, these technologies are having a profound effect on voting rights and democratic processes. However, they have received relatively little attention from AI experts, advocates, and policymakers. Scholars have sounded the alarm on a variety of “algorithmic harms” resulting from AI’s use in the criminal justice system, employment, healthcare, and other civil rights domains. Many of

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2

Yushchenko, E. L., and G. E. Tseitlin. "Issues of algorithmics and glushkov’s systems of algorithmic algebras." Cybernetics and Systems Analysis 34, no. 4 (1998): 532–39. http://dx.doi.org/10.1007/bf02666997.

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3

Röhl, Tobias, and Matthias Kirchner. "Algorithmische Alchemie – die sozio-technische Reproduktion sozialer Ungleichheit im Bildungssystem." Soziale Probleme 34, no. 2 (2023): 273–89. https://doi.org/10.5281/zenodo.13832371.

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Algorithmische Systeme versprechen, die Bewertung schulischer Leistung zu objektivieren und Bildungsangebote zu individualisieren. Statt subjektiver Urteile von Lehrpersonen sollen Algorithmen entscheiden, wie Freitextaufgaben und Essays zu bewerten sind und wie Aufgaben zugeteilt werden. Doch die Intransparenz der Technik erzeugt unbemerkt neue Formen der Ungleichheit. Die Datensätze und ihre algorithmische Auswertung unterliegen einem <em>algorithmic bias, </em>der bestehende Bildungsungleichheiten nicht bloss reproduziert, sondern sogar verstärkt. Wir diskutieren diesen Umstand an

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4

Bishop, Sophie. "Algorithmic Experts: Selling Algorithmic Lore on YouTube." Social Media + Society 6, no. 1 (2020): 205630511989732. http://dx.doi.org/10.1177/2056305119897323.

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This article considers the growing influence of self-styled algorithmic “experts.” Experts build valuable brands, accumulate notoriety, and piece together careers by selling theorizations of algorithmic visibility on YouTube to aspiring and established creators. They function as intermediaries between sanctioned YouTube industries and the agency of cultural producers. Expertise is developed through research, strategies, and theories to help content creators mitigate platform-specific risks, particularly the risk of algorithmic invisibility. Experts develop entrepreneurial self-brands and posit

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5

Gal, Michal. "Algorithmic Challenges to Autonomous Choice." Michigan Technology Law Review, no. 25.1 (2018): 59. http://dx.doi.org/10.36645/mtlr.25.1.algorithmic.

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We then explore how autonomous algorithmic assistants affect the legal framework. Some issues challenge the very use of algorithmic assistants: Should the law place an age limit on the use of such algorithms? Should legal limits be placed on their use in certain spheres? Other issues arise from the construction of preferences by algorithms. To wit, in a market in which demand is driven by algorithms, can we necessarily regard the choices made as expressions of user preferences that serve our socio-political goals? Even if the answer is positive—does this new mode of user choice fit current leg

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6

Fowler, John W. "Algorithmic Composition." Computer Music Journal 18, no. 3 (1994): 8. http://dx.doi.org/10.2307/3681175.

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7

Monahan, Torin. "Algorithmic Fetishism." Surveillance & Society 16, no. 1 (2018): 1–5. http://dx.doi.org/10.24908/ss.v16i1.10827.

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Surveillance-infused forms of algorithmic discrimination are beginning to capture public and scholarly attention. While this is an encouraging development, this editorial questions the parameters of this emerging discussion and cautions against algorithmic fetishism. I characterize algorithmic fetishism as the pleasurable pursuit of opening the black box, discovering the code hidden inside, exploring its beauty and flaws, and explicating its intricacies. It is a technophilic desire for arcane knowledge that can never be grasped completely, so it continually lures one forward into technical rea

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8

DuBrin, Rosie, and Ashley E. Gorham. "Algorithmic interpellation." Constellations 28, no. 2 (2021): 176–91. http://dx.doi.org/10.1111/1467-8675.12568.

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9

Kirkpatrick, Keith. "Algorithmic poverty." Communications of the ACM 64, no. 10 (2021): 11–12. http://dx.doi.org/10.1145/3479977.

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10

Davis, Jenny L., Apryl Williams, and Michael W. Yang. "Algorithmic reparation." Big Data & Society 8, no. 2 (2021): 205395172110448. http://dx.doi.org/10.1177/20539517211044808.

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Machine learning algorithms pervade contemporary society. They are integral to social institutions, inform processes of governance, and animate the mundane technologies of daily life. Consistently, the outcomes of machine learning reflect, reproduce, and amplify structural inequalities. The field of fair machine learning has emerged in response, developing mathematical techniques that increase fairness based on anti-classification, classification parity, and calibration standards. In practice, these computational correctives invariably fall short, operating from an algorithmic idealism that do

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Davis, Jenny L., Apryl Williams, and Michael W. Yang. "Algorithmic reparation." Big Data & Society 8, no. 2 (2021): 205395172110448. http://dx.doi.org/10.1177/20539517211044808.

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Machine learning algorithms pervade contemporary society. They are integral to social institutions, inform processes of governance, and animate the mundane technologies of daily life. Consistently, the outcomes of machine learning reflect, reproduce, and amplify structural inequalities. The field of fair machine learning has emerged in response, developing mathematical techniques that increase fairness based on anti-classification, classification parity, and calibration standards. In practice, these computational correctives invariably fall short, operating from an algorithmic idealism that do

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12

Mager, Astrid. "ALGORITHMIC IDEOLOGY." Information, Communication & Society 15, no. 5 (2012): 769–87. http://dx.doi.org/10.1080/1369118x.2012.676056.

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13

Gacs, P., J. T. Tromp, and P. M. B. Vitanyi. "Algorithmic statistics." IEEE Transactions on Information Theory 47, no. 6 (2001): 2443–63. http://dx.doi.org/10.1109/18.945257.

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14

McMullen, Peter. "ALGORITHMIC GEOMETRY." Bulletin of the London Mathematical Society 31, no. 6 (1999): 758–59. http://dx.doi.org/10.1112/blms/31.6.758.

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15

Raley. "Algorithmic Translations." CR: The New Centennial Review 16, no. 1 (2016): 115. http://dx.doi.org/10.14321/crnewcentrevi.16.1.0115.

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16

Zaparanuks, Dmitrijs, and Matthias Hauswirth. "Algorithmic profiling." ACM SIGPLAN Notices 47, no. 6 (2012): 67–76. http://dx.doi.org/10.1145/2345156.2254074.

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17

Bolognesi, Tommaso. "Algorithmic causets." Journal of Physics: Conference Series 306 (July 8, 2011): 012042. http://dx.doi.org/10.1088/1742-6596/306/1/012042.

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18

Rattan, Suresh I. S. "Algorithmic life." EMBO reports 4, no. 7 (2003): 654. http://dx.doi.org/10.1038/sj.embor.embor889.

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19

Diakopoulos, Nicholas. "Algorithmic Accountability." Digital Journalism 3, no. 3 (2014): 398–415. http://dx.doi.org/10.1080/21670811.2014.976411.

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20

Halpern, Joseph Y., and Rafael Pass. "Algorithmic rationality." ACM SIGecom Exchanges 10, no. 2 (2011): 9–15. http://dx.doi.org/10.1145/1998549.1998551.

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21

Shah, Hetan. "Algorithmic accountability." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, no. 2128 (2018): 20170362. http://dx.doi.org/10.1098/rsta.2017.0362.

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There is enormous opportunity for positive social impact from the rise of algorithms and machine learning. But this requires a licence to operate from the public, based on trustworthiness. There are a range of concerns relating to how algorithms might be held to account in areas affecting the public sphere. This paper outlines a number of approaches including greater transparency, monitoring of outcomes and improved governance. It makes a case that public sector bodies that hold datasets should be more confident in negotiating terms with the private sector. It also argues that all regulators (

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22

Nake, Frieder. "Algorithmic Art." Leonardo 47, no. 2 (2014): 108. http://dx.doi.org/10.1162/leon_a_00706.

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23

Faleiros Júnior, José Luiz de Moura. "Algorithmic torts." Brazilian Journal of Law, Technology and Innovation 2, no. 1 (2024): 210–24. http://dx.doi.org/10.59224/bjlti.v2i1.210-224.

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The purpose of this paper is to elucidate the intersection of algorithmic explainability and civil liability, exploring the implications of complex algorithms on legal responsibility. Algorithms, while not inherently intelligent, depend on data to present probabilistic predictions, differing significantly from human intuition. The core of this study lies in examining the limits of liability for damages caused by sophisticated algorithms, considering their inherent unpredictability. By analyzing the accountability framework proposed by scholars like Frank Pasquale, Mark Coeckelbergh, and Giovan

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24

Nuti, Giuseppe, Mahnoosh Mirghaemi, Philip Treleaven, and Chaiyakorn Yingsaeree. "Algorithmic Trading." Computer 44, no. 11 (2011): 61–69. http://dx.doi.org/10.1109/mc.2011.31.

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25

Wright, Alex. "Algorithmic authors." Communications of the ACM 58, no. 11 (2015): 12–14. http://dx.doi.org/10.1145/2820421.

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26

Friedman, Harvey, and Richard Mansfield. "Algorithmic procedures." Transactions of the American Mathematical Society 332, no. 1 (1992): 297–312. http://dx.doi.org/10.1090/s0002-9947-1992-1055807-7.

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27

Charap, John M. "Algorithmic compressibility." Physics World 4, no. 12 (1991): 55–58. http://dx.doi.org/10.1088/2058-7058/4/12/40.

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28

BAEZ, JOHN, and MIKE STAY. "Algorithmic thermodynamics." Mathematical Structures in Computer Science 22, no. 5 (2012): 771–87. http://dx.doi.org/10.1017/s0960129511000521.

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Algorithmic entropy can be viewed as a special case of the entropy studied in statistical mechanics. This viewpoint allows us to apply many techniques developed for use in thermodynamics to the subject of algorithmic information theory. In particular, suppose we fix a universal prefix-free Turing machine and let X be the set of programs that halt for this machine. Then we can regard X as a set of ‘microstates’, and treat any function on X as an ‘observable’. For any collection of observables, we can study the Gibbs ensemble that maximises entropy subject to constraints on the expected values o

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29

Selinger, Evan, and Brett Frischmann. "Algorithmic Bloodhounds." American Journal of Bioethics 18, no. 2 (2018): 24–26. http://dx.doi.org/10.1080/15265161.2017.1409838.

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30

Ames, W. F. "Algorithmic logic." Mathematics and Computers in Simulation 30, no. 4 (1988): 375. http://dx.doi.org/10.1016/s0378-4754(98)90017-9.

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31

Rice, Jeff. "Algorithmic outrage." Computers and Composition 57 (September 2020): 102582. http://dx.doi.org/10.1016/j.compcom.2020.102582.

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32

Creutz, Michael. "Algorithmic alternatives." Nuclear Physics B - Proceedings Supplements 4 (April 1988): 547–56. http://dx.doi.org/10.1016/0920-5632(88)90153-3.

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33

Nugteren, Cedric, Pieter Custers, and Henk Corporaal. "Algorithmic species." ACM Transactions on Architecture and Code Optimization 9, no. 4 (2013): 1–25. http://dx.doi.org/10.1145/2400682.2400699.

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34

Edwards, Michael. "Algorithmic composition." Communications of the ACM 54, no. 7 (2011): 58–67. http://dx.doi.org/10.1145/1965724.1965742.

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35

Sherstov, Alexander A. "Algorithmic Polynomials." SIAM Journal on Computing 49, no. 6 (2020): 1173–231. http://dx.doi.org/10.1137/19m1278831.

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36

Ershov, A. "Algorithmic Language." Soviet Education 28, no. 10-11 (1986): 65–73. http://dx.doi.org/10.2753/res1060-939328101165.

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37

Hutter, Marcus, Shane Legg, and Paul Vitanyi. "Algorithmic probability." Scholarpedia 2, no. 8 (2007): 2572. http://dx.doi.org/10.4249/scholarpedia.2572.

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38

Hutter, Marcus. "Algorithmic complexity." Scholarpedia 3, no. 1 (2008): 2573. http://dx.doi.org/10.4249/scholarpedia.2573.

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39

Downey, Rodney, and Jan Reimann. "Algorithmic randomness." Scholarpedia 2, no. 10 (2007): 2574. http://dx.doi.org/10.4249/scholarpedia.2574.

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40

Kleinberg, Jon, Jens Ludwig, Sendhil Mullainathan, and Ashesh Rambachan. "Algorithmic Fairness." AEA Papers and Proceedings 108 (May 1, 2018): 22–27. http://dx.doi.org/10.1257/pandp.20181018.

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Concerns that algorithms may discriminate against certain groups have led to numerous efforts to ‘blind’ the algorithm to race. We argue that this intuitive perspective is misleading and may do harm. Our primary result is exceedingly simple, yet often overlooked. A preference for fairness should not change the choice of estimator. Equity preferences can change how the estimated prediction function is used (e.g., different threshold for different groups) but the function itself should not change. We show in an empirical example for college admissions that the inclusion of variables such as race

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41

Striphas, Ted. "Algorithmic culture." European Journal of Cultural Studies 18, no. 4-5 (2015): 395–412. http://dx.doi.org/10.1177/1367549415577392.

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42

Downey, Rod, and Denis R. Hirschfeldt. "Algorithmic randomness." Communications of the ACM 62, no. 5 (2019): 70–80. http://dx.doi.org/10.1145/3319408.

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43

Li, Tiffany C. "Algorithmic Destruction." SMU Law Review 75, no. 3 (2022): 479. http://dx.doi.org/10.25172/smulr.75.3.2.

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Contemporary privacy law does not go far enough to protect our privacy interests, particularly where artificial intelligence and machine learning are concerned. While many have written on problems of algorithmic bias and data deletion, this Article introduces the novel concept of the “algorithmic shadow” and explains the new privacy remedy of “algorithmic destruction,” also known as algorithmic disgorgement or machine unlearning. The algorithmic shadow describes the persistent imprint of training data that has been fed into a machine learning model and used to refine that machine learning syst

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44

Prox, Ryan. "Algorithmic Technology." Journal of Intelligence, Conflict, and Warfare 5, no. 2 (2022): 194–99. http://dx.doi.org/10.21810/jicw.v5i2.5056.

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On October 20, 2022, Dr. Ryan Prox, S/Constable-in-Charge of the Crime Analytics Advisory & Developmental Unit at the Vancouver Police Department and Adjunct Professor at Simon Fraser University, presented on Algorithmic Technology: Fueling AI in an Ethical & Transparent Way at the October Digital Roundtable event hosted by the Canadian Association for Security and Intelligence Studies (CASIS)-Vancouver. The key points discussed were algorithmic technology and its implementation within police services; governance, accountability, and public perceptions of AI; and mitigating risk when i

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45

Prautzsch, H. "Algorithmic Blending." Journal of Approximation Theory 72, no. 1 (1993): 87–102. http://dx.doi.org/10.1006/jath.1993.1008.

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46

Bessa, Maria. "Algorithmic Design." Architectural Design 79, no. 1 (2009): 120–23. http://dx.doi.org/10.1002/ad.831.

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47

Bajohr, Hannes. "Algorithmic Empathy." Basel Media Culture and Cultural Techniques Working Papers, no. 004 (November 1, 2020): 1–32. http://dx.doi.org/10.12685/bmcct.2020.004.

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48

Ball, Andrew J. "Algorithmic Aesthetics." Screen Bodies 7, no. 2 (2022): v—vi. http://dx.doi.org/10.3167/screen.2022.070201.

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Though the authors in this general issue of Screen Bodies engage with a wide array of media, they express a shared group of concerns. Namely, how recent technological advancements and the big data cultures of the Information Age are altering social norms concerning the body, the subject, and intimacy. The first two articles focus on increasingly data-oriented cultures that have given rise to aesthetics derived from quantification and mathematics. In “Qualities Over Quantities: Metric and Narrative Identities in Dataveillant Art Practice,” Amy Christmas examines the “surveillant aesthetic” pres

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49

Willis, Paul. "Algorithmic Visions." Aerospace Testing International 2024, no. 4 (2025): 44–50. https://doi.org/10.12968/s1478-2774(25)50100-x.

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50

Behnam Shad, Klaus. "Algorithmic Hegemony." Journal of Sociocybernetics 20, no. 1 (2025): 13–41. https://doi.org/10.26754/ojs_jos/jos.2025111599.

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This article examines the transformative role of AI-mediated communication systems (AIMCS) in shaping public discourse and political outcomes, with a specific focus on the 2025 German electoral campaign. By integrating insights from sociocybernetics, neurobiology, and anthropology, this study offers a multidisciplinary analysis of how AIMCS have evolved from passive conduits of information into active agents of governance. The article investigates how advanced generative models and engagement-driven algorithms restructure political communication by embedding hegemonic power structures into dig

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