Algorithms as culture: Some tactics for the ethnography of algorithmic systems

Seaver, Nick

doi:10.1177/2053951717738104

Cited by 566 publications

(447 citation statements)

References 26 publications

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“…If AnomieBot was using advanced neural network classifiers to identify edit requests to be aggregated (rather than simple pattern matching), there would be an additional layer of algorithmic complexity at work, but my discussion would still be how an organizational task is abstracted such that it can be ''clerked'' by an automated software agent. I also take from Seaver's (2017) call to study ''algorithms as culture'' rather than ''algorithms in culture''-focusing on the co-construction of culture through a variety of means, including but not limited to algorithmic systems. My goal in this article is to relate a more empirically situated and constructivist approach to studying algorithmic systems ''in action'' or ''in the wild,'' much as scholars in the field of science and technology studies have investigated science and technology (e.g., Hutchins, 1996;Latour, 1987).…”

Section: Algorithms As Culture/algorithms In Actionmentioning

confidence: 99%

Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture

Geiger¹

2017

Big Data & Society

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Scholars and practitioners across domains are increasingly concerned with algorithmic transparency and opacity, interrogating the values and assumptions embedded in automated, black-boxed systems, particularly in user-generated content platforms. I report from an ethnography of infrastructure in Wikipedia to discuss an often understudied aspect of this topic: the local, contextual, learned expertise involved in participating in a highly automated social-technical environment. Today, the organizational culture of Wikipedia is deeply intertwined with various data-driven algorithmic systems, which Wikipedians rely on to help manage and govern the ''anyone can edit'' encyclopedia at a massive scale. These bots, scripts, tools, plugins, and dashboards make Wikipedia more efficient for those who know how to work with them, but like all organizational culture, newcomers must learn them if they want to fully participate. I illustrate how cultural and organizational expertise is enacted around algorithmic agents by discussing two autoethnographic vignettes, which relate my personal experience as a veteran in Wikipedia. I present thick descriptions of how governance and gatekeeping practices are articulated through and in alignment with these automated infrastructures. Over the past 15 years, Wikipedian veterans and administrators have made specific decisions to support administrative and editorial workflows with automation in particular ways and not others. I use these cases of Wikipedia's bot-supported bureaucracy to discuss several issues in the fields of critical algorithms studies; critical data studies; and fairness, accountability, and transparency in machine learning-most principally arguing that scholarship and practice must go beyond trying to ''open up the black box'' of such systems and also examine sociocultural processes like newcomer socialization.

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Section: Algorithms As Culture/algorithms In Actionmentioning

confidence: 99%

Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture

Geiger¹

2017

Big Data & Society

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“…As perhaps best defined by the most cited textbook on algorithms, an algorithm is a sequence of computational steps that transform inputs into outputs-similar to a recipe (Cormen 2009). Algorithms are viewed as maximizing efficiency or accuracy; computer scientists are, therefore, responsible for ensuring efficiency and accuracy (Seaver 2017).…”

Section: Ethical Implications Of Algorithmsmentioning

confidence: 99%

Ethical Implications and Accountability of Algorithms

2018

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Algorithms silently structure our lives. Algorithms can determine whether someone is hired, promoted, offered a loan, or provided housing as well as determine which political ads and news articles consumers see. Yet, the responsibility for algorithms in these important decisions is not clear. This article identifies whether developers have a responsibility for their algorithms later in use, what those firms are responsible for, and the normative grounding for that responsibility. I conceptualize algorithms as value-laden, rather than neutral, in that algorithms create moral consequences, reinforce or undercut ethical principles, and enable or diminish stakeholder rights and dignity. In addition, algorithms are an important actor in ethical decisions and influence the delegation of roles and responsibilities within these decisions. As such, firms should be responsible not only for the value-laden-ness of an algorithm but also for designing who-does-what within the algorithmic decision. As such, firms developing algorithms are accountable for designing how large a role individual will be permitted to take in the subsequent algorithmic decision. Counter to current arguments, I find that if an algorithm is designed to preclude individuals from taking responsibility within a decision, then the designer of the algorithm should be held accountable for the ethical implications of the algorithm in use. Keywords Algorithms · AI · Ethics · Fairness · Artificial intelligence · Big Data · Accountability · STS · TechnologyRodríguez was just sixteen at the time of his arrest, and was convicted of second-degree murder for his role in an armed robbery of a car dealership that left an employee dead. Now, twenty-six years later, he was a model of rehabilitation. He had requested a transfer to Eastern, a maximum-security prison, in order to take college classes. He had spent four and a half years training service dogs for wounded veterans and eleven volunteering for a youth program. A job and a place to stay were waiting for him outside. And he had not had a single disciplinary infraction for the past decade… Yet, last July, the parole board hit him with a denial. It might have turned out differently but, the board explained, a computer system called COM-PAS had ranked him "high risk." Neither he nor the board had any idea how this risk score was calculated; Northpointe, the for-profit company that sells COM-

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“…To understand the implications of these assemblages to the human, we have to better understand the nonhumans. The anthropological project around post-human This requires experimentation new ethnographic techniques (Seaver 2017).…”

Section: Resultsmentioning

confidence: 99%

A.I. Among Us: Agency in a World of Cameras and Recognition Systems

Anderson

Bezaitis

DiSalvo

et al. 2019

Ethnographic Praxis

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This paper reports on the use and perceptions of deployed A.I. and recognition social-material assemblages in China and the USA. A kaleidoscope of "boutique" instantiations is presented to show how meanings are emerging around A.I. and recognition. A model is presented to highlight that not all recognitions are the same. We conclude by noting A.I. and recognition systems challenge current practices for the EPIC community and the field of anthropology.

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Algorithms as culture: Some tactics for the ethnography of algorithmic systems

Cited by 566 publications

References 26 publications

Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture

Beyond opening up the black box: Investigating the role of algorithmic systems in Wikipedian organizational culture

Ethical Implications and Accountability of Algorithms

A.I. Among Us: Agency in a World of Cameras and Recognition Systems

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