Knowing Algorithms

Seaver, Nick

doi:10.2307/j.ctvc77mp9.30

Cited by 135 publications

(125 citation statements)

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“…Accountability becomes difficult to trace in these situations. As Nick Seaver puts it, these systems are 'not standalone little boxes, but massive, networked ones with hundreds of hands reaching into them' [68]. Accountable systems should to be internally accountable, else it would appear to be difficult for external accountability to either make sense or be sustained.…”

Section: 'The Probability Of Being Correct Tanked': Data Changesmentioning

confidence: 99%

Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

Veale

Kleek

Binns

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

297

223

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Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions-like taxation, justice, and child protection-are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning-absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the 'street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.

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Section: 'The Probability Of Being Correct Tanked': Data Changesmentioning

confidence: 99%

Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

Veale

Kleek

Binns

2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

297

223

View full text Add to dashboard Cite

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“…the idea of algorithms as embodying values that might lead to bias and discrimination (Kraemer et al, 2010). A key concern across these fields is thus an epistemic one with ''knowing algorithms'' (Seaver, 2013). Understanding what exactly algorithms do, the story goes, will help us better understand their implications, open up the black box of control, and respond to injustices that have been coded into them.…”

Section: Travelling Algorithmsmentioning

confidence: 99%

A not quite random walk: Experimenting with the ethnomethods of the algorithm

Ziewitz

2017

Big Data & Society

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Algorithms have become a widespread trope for making sense of social life. Science, finance, journalism, warfare, and policing-there is hardly anything these days that has not been specified as ''algorithmic.'' Yet, although the trope has brought together a variety of audiences, it is not quite clear what kind of work it does. Often portrayed as powerful yet inscrutable entities, algorithms maintain an air of mystery that makes them both interesting and difficult to understand. This article takes on this problem and examines the role of algorithms not as techno-scientific objects to be known, but as a figure that is used for making sense of observations. Following in the footsteps of Harold Garfinkel's tutorial cases, I shall illustrate the implications of this view through an experiment with algorithmic navigation. Challenging participants to go on a walk, guided not by maps or GPS but by an algorithm developed on the spot, I highlight a number of dynamics typical of reasoning with running code, including the ongoing respecification of rules and observations, the stickiness of the procedure, and the selective invocation of the algorithm as an intelligible object. The materials thus provide an opportunity to rethink key issues at the intersection of the social sciences and the computational, including popular concerns with transparency, accountability, and ethics.We watch an ant making his laborious way across a wind-and wave-molded beach. He moves ahead, angles to the right to ease his climb up a steep dunelet, detours around a pebble, stops for a moment to exchange information with a compatriot. Thus he makes his weaving, halting way back to his home. So as not to anthropomorphize about his purposes, I sketch the path on a piece of paper. It is a sequence of irregular, angular segments-not quite a random walk, for it has an underlying sense of direction, of aiming toward a goal. (Simon, 1996: 51)

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“…There is a growing body of scholarship aligning under the title of ''critical algorithms studies'' (Gillespie and Seaver, 2016;Seaver, 2013) to investigate these issues. Furthermore, concerns about the role of software in the governance of online communities has been a core topic of the software studies field for decades, best encapsulated in Lawrence Lessig's (1999) famous declaration that ''code is law,'' speaking to the governmental role that programmers have in designing and developing systems.…”

Section: Algorithms As Culture/algorithms In Actionmentioning

confidence: 99%

“…Yet Seaver (2013) notes how most scholarship in the emerging field of ''critical algorithms studies'' continually struggles with the inability to ''see inside the black box'' of algorithmic systems, which makes it difficult to see such systems ''in the making.'' To see a system ''in the making'' is the classic denaturalization move continually made by ethnographers and historians of science and technology who investigate the messy work and competing perspectives that are often obscured when a product goes to market or a paper goes to a scientific journal.…”

Section: Telling Stories About Contextual Factorsmentioning

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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Knowing Algorithms

Cited by 135 publications

References 0 publications

Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

A not quite random walk: Experimenting with the ethnomethods of the algorithm

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

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