This article investigates the metaphor of the “black box” in artificial intelligence, a representation that often suggests that AI is an unfathomable power, politically uncontrollable and shrouded in an aura of opacity. While the concept of the “black box” is legitimate and applicable in deep neural networks due to the in- herent complexity of the process, it has also become a generic pretext for the perception, which we seek to critically analyze, that AI systems are inscrutable and out (...) of control, as well as supposedly endowed with intel- ligence and creativity. To challenge these ideas, we will address what we call the supremacy of patterns and the two significant phenomena that result from it: enchanted determinism and the dictatorship of the past. (shrink)

Without doctors being able to explain medical decisions to patients, I argue their use of black box AIs would erode the effective and respectful care they provide patients. In addition, I argue that physicians should use AI black boxes only for patients in dire straits, or when physicians use AI as a “co-pilot” (analogous to a spellchecker) but can independently confirm its accuracy. I respond to A.J. London’s objection that physicians already prescribe some drugs without knowing why they (...) work. (shrink)

The artificial intelligence models with machine learning that exhibit the best predictive accuracy, and therefore, the most powerful ones, are, paradoxically, those with the most opaque black-box architectures. At the same time, the unstoppable computerization of advanced industrial societies demands the use of these machines in a growing number of domains. The conjunction of both phenomena gives rise to a control problem on AI that in this paper we analyze by dividing the issue into two. First, we carry out (...) an epistemological examination of the AI’s opacity in light of the latest techniques to remedy it. And second, we evaluate the rationality of delegating tasks in opaque agents. (shrink)

The last 5 years have seen a series of remarkable achievements in deep-neural-network-based artificial intelligence research, and some modellers have argued that their performance compares favourably to human cognition. Critics, however, have argued that processing in deep neural networks is unlike human cognition for four reasons: they are (i) data-hungry, (ii) brittle, and (iii) inscrutable black boxes that merely (iv) reward-hack rather than learn real solutions to problems. This article rebuts these criticisms by exposing comparative bias within them, in (...) the process extracting some more general lessons that may also be useful for future debates. (shrink)

One staple of living in our information society is having access to the web. Web-connected devices interpret our queries and retrieve information from the web in response. Today’s web devices even purport to answer our queries directly without requiring us to comb through search results in order to find the information we want. How do we know whether a web device is trustworthy? One way to know is to learn why the device is trustworthy by inspecting its inner workings, 156–170 (...) 1995; Humphreys 2004, Episteme, 6, 221–229 2009). But ordinary users of web devices cannot inspect their inner workings because of their scale, complexity, and the corporate secrecy which enshrouds both the procedures by which the devices operate and the companies that make them. Further piling on this predicament, authors have criticized web technology on the grounds that the invisibility of the web devices’ inner workings prevents users from critically assessing the procedures that produce a given output, in some cases, barring users from fulfilling their epistemic responsibilities, 343–355 2010; Miller and Record Episteme, 10, 117–134 2013). I consider four broad kinds of reasons which we can acquire without inspecting the inner workings of black-boxed technology: individual understanding, expert testimony, testing through experience, and social vetting; and show how each is a viable method of appraising black-boxed technology. By deploying these methods, we can remain responsible inquirers while nonetheless benefitting from today’s epistemic resources on the web. (shrink)

An oversized reliance on big data-driven algorithmic decision-making systems, coupled with a lack of critical inquiry regarding such systems, combine to create the paradoxical “black box” at work. The “black box” simultaneously demands a higher level of transparency from the worker in regard to data collection, while shrouding the decision-making in secrecy, making employer decisions even more opaque to the worker. To access employment, the worker is commanded to divulge highly personal information, and when hired, must submit further (...) still to algorithmic processes of evaluations which will make authoritative claims as to the workers’ productivity. Furthermore, in and out of the workplace, the worker is governed by an invisible data-created leash deploying wearable technology to collect intimate worker data. At all stages, the worker is confronted with a lack of transparency, accountability, or explanation as to the inner workings or even the logic of the “black box” at work. This data revolution of the workplace is alarming for several reasons: the “black box at work” not only serves to conceal disparities in hiring, but could also allow for a level of “data-laundering” that beggars any notion of equal opportunity in employment and there exists, the danger of a “mission creep” attitude to data collection that allows for pervasive surveillance, contributing to the erosion of both the personhood and autonomy of workers. Thus, the “black box at work” not only enables worker domination in the workplace, it deprives the worker of Rawlsian justice. (shrink)

Bricks and boxes -- Between Scylla and Charybdis -- Lessons from the history of science -- Placeholders -- Black-boxing 101 -- History of science 'black-boxing style' -- Diet mechanistic philosophy -- Emergence reframed -- The fuel of scientific progress -- Sailing through the strait.

Higher education institutions have access to higher volumes and a greater variety and granularity of student data, often in real-time, than ever before. As such, the collection, analysis and use of student data are increasingly crucial in operational and strategic planning, and in delivering appropriate and effective learning experiences to students. Student data – not only in what data is collected, but also how the data is framed and used – has material and discursive effects, both permanent and fleeting. We (...) have to critically engage claims that artificial intelligence and the ever expansive/expanding systems of algorithmic decision-making provide speedy, accessible, revealing, panoramic, prophetic and smart analyses of students' risks, potential and learning needs. We need to pry open the black boxes higher education institutions use to admit, steer, predict and prescribe students’ learning journeys. (shrink)

Mental health apps bring unprecedented benefits and risks to individual and public health. A thorough evaluation of these apps involves considering two aspects that are often neglected: the algorithms they deploy and the functions they perform. We focus on mental health apps based on black box algorithms, explore their forms of opacity, discuss the implications derived from their opacity, and propose how to use their outcomes in mental healthcare, self‐care practices, and research. We argue that there is a relevant (...) distinction between functions performed by algorithms in mental health apps, and we focus on the functions of analysis and generation of advice. When performing analytic functions, such as identifying patterns and making predictions concerning people's emotions, thoughts, and behaviors, black box algorithms can be better than other algorithms to provide information to identify early signs of relapse, support diagnostic processes, and improve research by generating outcomes that lead to a better understanding of mental health. However, when carrying out the function of providing mental health advice, black box algorithms have the potential to deliver unforeseen advice that may harm users. We argue that the outcomes of these apps may be trustworthy as a complementary source of information, but express caution about black box algorithms that give advice directly to users. To reap the benefits of mental health apps based on black box algorithms and avoid unintended consequences, we critically need to know whether these algorithms are fulfilling the function of providing mental health advice. (shrink)

As sophisticated artificial intelligence software becomes more ubiquitously and more intimately integrated within domains of traditionally human endeavor, many are raising questions over how responsibility (be it moral, legal, or causal) can be understood for an AI’s actions or influence on an outcome. So called “responsibility gaps” occur whenever there exists an apparent chasm in the ordinary attribution of moral blame or responsibility when an AI automates physical or cognitive labor otherwise performed by human beings and commits an error. Healthcare (...) administration is an industry ripe for responsibility gaps produced by these kinds of AI. The moral stakes of healthcare are often life and death, and the demand for reducing clinical uncertainty while standardizing care incentivizes the development and integration of AI diagnosticians and prognosticators. In this paper, we argue that (1) responsibility gaps are generated by “black box” healthcare AI, (2) the presence of responsibility gaps (if unaddressed) creates serious moral problems, (3) a suitable solution is for relevant stakeholders to voluntarily responsibilize the gaps, taking on some moral responsibility for things they are not, strictly speaking, blameworthy for, and (4) should this solution be taken, black box healthcare AI will be permissible in the provision of healthcare. (shrink)

In “Artificial Intelligence, Social Media and Depression: A New Concept of Health-Related Digital Autonomy,” Laacke and colleagues posit a revised model of autonomy when using digital algori...

“Black box argument” is a metaphor for modular components of argumentative discussion that are, within a particular discussion, not open to expansion. In public policy debate such as the controversy over abstinence-only sex education, scientific conclusions enter the discourse as black boxes consisting of a result returned from an external and largely impenetrable process. In one way of looking at black box arguments, there is nothing fundamentally new for the argumentation theorist: A black box argument is (...) very like any other appeal to authority, and what might be said about any particular form of black box will turn out to be a particularized version of what might be said about evaluating arguments based on authority. But in another way of looking at black box arguments, they are a constantly evolving technology for coming to conclusions and making these conclusions broadly acceptable. Black boxes are to argumentation what material inventions are to engineering and related sciences. They are anchored in and constrained by fundamental natural processes, but they are also new things that require theoretical explication and practical assessment. (shrink)

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and (...) by explaining that computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence. To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care. (shrink)

The ‘black box’ in Stoic axiology refers to the mysterious connection between the input of Stoic deliberation (reasons generated by the value of indifferents) and the output (appropriate actions). In this paper, I peer into the black box by drawing an analogy between Stoic and Kantian axiology. The value and disvalue of indifferents is intrinsic, but conditional. An extrinsic condition on the value of a token indifferent is that one's selection of that indifferent is sanctioned by context-relative ethical (...) principles. The value of an indifferent that does not meet this condition is normatively silenced, such that it fails to constitute a reason for action. (shrink)

This article draws on contributions from the Sociology of Science and Technology and Science and Technology Studies, the Sociology of Risk and Uncertainty, and the Sociology of Work, focusing on the transformations of employment regarding expanded automation, robotization and informatization. The new work patterns emerging due to the introduction of software and hardware technologies, which are based on artificial intelligence, algorithms, big data gathering and robotic systems are examined closely. This article attempts to “open the black boxes” of the (...) “black carpet” (robotic sorting system) and examine the reorganization of Greek postal services through the introduction of software and hardware technologies, highlighting a) the high risk of flexible, pluralistic, decentralized (under)employment and aspects of the sub-politics of automation, b) the new forms of work organization and c) aspects of labor precariousness as a result of de-specialization or “flexible expertise” interconnected with new forms of inequalities based on AI, algorithms, big data and robots as reflected in the lived experiences of workers. The above are investigated through primary empirical social research in postal service companies in Greece. (shrink)

Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...) philosophy of science, this framework is modeled after accounts of explanation in cognitive science. The framework distinguishes between the explanation-seeking questions that are likely to be asked by different stakeholders, and specifies the general ways in which these questions should be answered so as to allow these stakeholders to perform their roles in the Machine Learning ecosystem. By applying the normative framework to recently developed techniques such as input heatmapping, feature-detector visualization, and diagnostic classification, it is possible to determine whether and to what extent techniques from Explainable Artificial Intelligence can be used to render opaque computing systems transparent and, thus, whether they can be used to solve the Black Box Problem. (shrink)

An empirical procedure is suggested for testing a model that postulates variables that intervene between observed causes and abserved effects against a model that includes no such postulate. The procedure is applied to two experiments in psychology. One involves a conditioning regimen that leads to response generalization; the other concerns the question of whether chimpanzees have a theory of mind.

AI is considered to be very abstract to a range of critics. In this regard, algorithms are referred to regularly as black boxes and divorced from human intervention. A particular philosophical maneuver supports this outcome. The aim of this article is to (1) bring the philosophy to the surface that has contributed to this distance between AI and people and (2) offer an alternative philosophical position that can bring this technology closer to individuals and communities. The overall goal of (...) the analysis in this paper is the humanising of AI by addressing the shortcomings of conceptualising algorithms as black boxes. (shrink)

This article introduces the notion of a kind of inference called black box measurement and argues that it is both historically and philosophically significant. Thinking about certain classic cases of underdetermination using this notion can give us a better understanding of how these cases are resolved. I take the main philosophical problem of black box measurement to be the justification of assumptions that are needed in order to make these measurements. I sketch some ways in which such enabling (...) assumptions might be justified. (shrink)

Although decision‐making algorithms are not new to medicine, the availability of vast stores of medical data, gains in computing power, and breakthroughs in machine learning are accelerating the pace of their development, expanding the range of questions they can address, and increasing their predictive power. In many cases, however, the most powerful machine learning techniques purchase diagnostic or predictive accuracy at the expense of our ability to access “the knowledge within the machine.” Without an explanation in terms of reasons or (...) a rationale for particular decisions in individual cases, some commentators regard ceding medical decision‐making to black box systems as contravening the profound moral responsibilities of clinicians. I argue, however, that opaque decisions are more common in medicine than critics realize. Moreover, as Aristotle noted over two millennia ago, when our knowledge of causal systems is incomplete and precarious—as it often is in medicine—the ability to explain how results are produced can be less important than the ability to produce such results and empirically verify their accuracy. (shrink)

We are repeatedly told that AI will help us to solve some of the world's biggest challenges, from treating chronic diseases and reducing fatality rates in traffic accidents to fighting climate change and anticipating cybersecurity threats. However, the article contends that public discourse on AI systematically avoids considering AI’s environmental costs. Artificial Intelligence- Brevini argues- runs on technology, machines, and infrastructures that deplete scarce resources in their production, consumption, and disposal, thus increasing the amounts of energy in their use, and (...) exacerbate problems of waste and pollution. It also relies on data centers, that demands impressive amounts of energy to compute, analyse, categorize. If we want to stand a chance at tackling the Climate Emergency, then we have to stop avoiding addressing the environmental problems generated by AI. (shrink)

Once upon a time, there were two large black boxes, A and B, connected by a long insulated copper wire. On box A there were two buttons, marked *a* and *b*, and on box B there were three lights, red, green, and amber. Scientists studying the behavior of the boxes had observed that whenever you pushed the *a* button on box A, the red light flashed briefly on box B, and whenever you pushed the *b* button on box A, (...) the green light flashed briefly. The amber light never seemed to flash. They performed a few billion trials, under a very wide variety of conditions, and found no exceptions. There seemed to them to be a causal regularity, which they conveniently summarized thus. (shrink)

The ‘black box problem’ is a long-standing talking point in debates about artificial intelligence. This is a significant point of tension between ethicists, programmers, clinicians and anyone else working on developing AI for healthcare applications. However, the precise definition of these systems are often left undefined, vague, unclear or are assumed to be standardised within AI circles. This leads to situations where individuals working on AI talk over each other and has been invoked in numerous debates between opaque and (...) explainable systems. This paper proposes a coherent and clear definition for the black box problem to assist in future discussions about AI in healthcare. This is accomplished by synthesising various definitions in the literature and examining several criteria that can be extrapolated from these definitions. There are no data in this work. (shrink)

In the ethics of algorithms, a specifically epistemological analysis is rarely undertaken in order to gain a critique (or a defense) of the handling of or trust in medical black box algorithms (BBAs). This article aims to begin to fill this research gap. Specifically, the thesis is examined according to which such algorithms are regarded as epistemic authorities (EAs) and that the results of a medical algorithm must completely replace other convictions that patients have (preemptionism). If this were true, (...) it would be a reason to distrust medical BBAs. First, the author describes what EAs are and why BBAs can be considered EAs. Then, preemptionism will be outlined and criticized as an answer to the question of how to deal with an EA. The discussion leads to some requirements for dealing with a BBA as an EA. (shrink)

This paper addresses the black-box problem in artificial intelligence (AI), and the related problem of explainability of AI in the legal context. We argue, first, that the black box problem is, in fact, a superficial one as it results from an overlap of four different – albeit interconnected – issues: the opacity problem, the strangeness problem, the unpredictability problem, and the justification problem. Thus, we propose a framework for discussing both the black box problem and the explainability (...) of AI. We argue further that contrary to often defended claims the opacity issue is not a genuine problem. We also dismiss the justification problem. Further, we describe the tensions involved in the strangeness and unpredictability problems and suggest some ways to alleviate them. (shrink)

Discipline and Punish analyzes the role of collecting, managing, and operationalizing data in disciplinary institutions. Foucault’s discussion is compared to contemporary forms of surveillance and security practices using algorithmic data processing. The article highlights important similarities and differences regarding the way data processing plays a part in subjectivation. This is also compared to Deleuzian accounts and Foucault’s later discussion in Security, Territory, Population. Using these results, the article argues that the prevailing focus on transparency and accountability in the discussion of (...) algorithmic applications needs to be amended with a perspective on different forms of subjectivity and ensuing power relations. (shrink)

With automation of routine decisions coupled with more intricate and complex information architecture operating this automation, concerns are increasing about the trustworthiness of these systems. These concerns are exacerbated by a class of artificial intelligence that uses deep learning, an algorithmic system of deep neural networks, which on the whole remain opaque or hidden from human comprehension. This situation is commonly referred to as the black box problem in AI. Without understanding how AI reaches its conclusions, it is an (...) open question to what extent we can trust these systems. The question of trust becomes more urgent as we delegate more and more decision-making to and increasingly rely on AI to safeguard significant human goods, such as security, healthcare, and safety. Models that “open the black box” by making the non-linear and complex decision process understandable by human observers are promising solutions to the black box problem in AI but are limited, at least in their current state, in their ability to make these processes less opaque to most observers. A philosophical analysis of trust will show why transparency is a necessary condition for trust and eventually for judging AI to be trustworthy. A more fruitful route for establishing trust in AI is to acknowledge that AI is situated within a socio-technical system that mediates trust, and by increasing the trustworthiness of these systems, we thereby increase trust in AI. (shrink)

Fears of black-box algorithms are multiplying. Black-box algorithms are said to prevent accountability, make it harder to detect bias and so on. Some fears concern the epistemology of black-box algorithms in medicine and the ethical implications of that epistemology. In ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI,' Durán and Jongsma seek to allay such fears. While some of their arguments are compelling, we still see reasons (...) for fear. (shrink)

The existence of a firm can be seen as a necessary condition for the existence of stakeholders. However, in the stakeholder literature, the firm has remained a relatively underdeveloped and fuzzy construct. In this essay, we examine how the firm has been conceptualized (explicitly or implicitly) in earlier research and suggest that, at least in stakeholder thinking, the firm can be considered as having an emergent nature. We elaborate this idea by building on the resent philosophical discussions of emergence and, (...) as a result, propose the emergentist stakeholder model of the firm. (shrink)

Context: The thoroughly second-order cybernetic underpinnings of naturalist theatre have gone almost entirely unremarked in the literature of both theatre studies and cybernetics itself. As a result, rich opportunities for the two fields to draw mutual benefit and break new ground through both theoretical and empirical investigations of these underpinnings have, thus far, gone untapped. Problem: The field of cybernetics continues to remain academically marginalized for, among other things, its alleged lack of experimental rigor. At the same time, the field (...) of theatre studies finds itself at an impasse between post-structuralist semioticians and embodied cognitivists regarding key onto-epistemological issues. A program of research framing and utilizing naturalist theatre as a second-order cybernetic/cybersemiotic laboratory holds much promise in addressing both matters and lending credence to Ross Ashby’s assertion that “the discovery that two fields are related leads to each branch helping in the development of the other.” Method: After establishing the nature of the onto-epistemological deadlock within theatre studies, this article examines the application of cybernetic heuristics within naturalistic theatre, leading to a second-order cybernetic analysis of its processes of production and reception and the outline of an experimental program for exploring these processes further. Results: Foundations for a model of naturalist theatre as a cybersemiotic laboratory grounded in a novel operationalization of Gordon Pask’s conversation theory. Implications: The proposed laboratory could result in the generation of quantitative and qualitative research pertaining to several dimensions of second-order cybernetics; particularly cybersemiotics, which, as a result, may end up better positioned to help dissolve onto-epistemological deadlocks between constructivists and realists of all stripes across the academy and beyond. Constructivist content: I argue that an analysis of naturalistic theatre’s processes of meaning-making filtered through the constructivist ontological agnosticism of second-order cybernetics offers a productive middle way forward for those on both sides of the social constructivist/embodied cognitive realist divide, within and beyond theatre studies. The article draws upon the works of Gregory Bateson, Søren Brier, Ranulph Glanville, Heinz von Foerster, and Niklas Luhmann. (shrink)

My aim in this article is to contribute to the larger project of assessing the relative merits of different theories of substance. An important preliminary step in this project is assessing the explanatory resources of one main theory of substance, the so-called bundle theory. This article works towards such an assessment. I identify and explain three distinct explanatory challenges an adequate bundle theory must meet. Each points to a putative explanatory gap, so I call them the Gap Challenges. I consider (...) three bundle-theoretic strategies for meeting these challenges. I argue that none of them goes very far. The upshot is that, absent other strategies for meeting the challenges, bundle theory involves a significant amount of stipulation. This black box makes bundle theory relatively weak with respect to its explanatory power—unless, of course, rival theories of substance are unable to do better. (shrink)

In the 1950s and 1960s, cyberneticians defined and utilized a concept previously described by electronic engineers: the black box. They were interested in how it might aid them, as both a metaphor and as a physical or mathematical model, in their analysis of complex human-machine systems. The black box evolved as they applied it in new ways, across a range of scientific fields, from an unnamed concept involving inputs and outputs, to digital representations of the human brain, to (...) white boxes that might be used to replicate black boxes. The diversity of understandings of the black box reflected the diversity of scientific perspectives and goals brought under the label of cybernetics. In this paper, I examine how cyberneticians drew upon the black box in their personal writings and publications. My goal is to unpack what the black box meant to these theorists as a starting framework from which we may understand the initial shape of the black box. (shrink)

Robotic systems consisting of a neuron culture grown on a multielectrode array which is connected to a virtual or mechanical robot have been studied for approximately 15 years. It is hoped that these MEA-based robots will be able to address the problem that robots based on conventional computer technology are not very good at adapting to surprising or unusual situations, at least not when compared to biological organisms. It is also hoped that insights gained from MEA-based robotics can have applications (...) within human enhancement and medicine. In this paper, I argue that researchers within this field risk overstating their results by not paying enough attention to fundamental challenges within the field. In particular, I investigate three problems: the coding problem, the embodiment problem and the training problem. I argue that none of these problems have been solved and that they are not likely to be solved within the field. After that, I discuss whether MEA-based robotics should be considered pop science. Finally, I investigate the ethical aspects of this research. (shrink)

Hospital ethics committees (HECs) and ethicists generally describe themselves as engaged in four domains of practice: case consultation, research, education, and policy work. Despite the increasing attention to quality indicators, practice standards, and evaluation methods for the other domains, comparatively little is known or published about the policy work of HECs or ethicists. This article attempts to open the ?black box? of this health care ethics practice by providing two detailed case examples of ethics policy reviews. We also describe (...) the development and application of an evaluation strategy to assess the quality of ethics policy review work, and to enable continuous improvement of ethics policy review processes. Given the potential for policy work to impact entire patient populations and organizational systems, it is imperative that HECs and ethicists develop clearer roles, responsibilities, procedural standards, and evaluation methods to ensure the delivery of consistent, relevant, and high-quality ethics policy reviews. (shrink)

Economists have long treated technological phenomena as events transpiring inside a black box and, on the whole, have adhered rather strictly to a self-imposed ordinance not to inquire too seriously into what transpires inside that box. The purpose of Professor Rosenberg's work is to break open and examine the contents of the black box. In so doing, a number of important economic problems be powerfully illuminated. The author clearly shows how specific features of individual technologies have shaped a (...) number of variables of great concern to economists: the rate of productivity improvement, the nature of learning processes underlying technological change itself, the speed of technology transfer, and the effectiveness of government policies that are intended to influence technologies in particular ways. The separate chapters of this book reflect a primary concern with some of the distinctive aspects of industrial technologies in the twentieth century, such as the increasing reliance upon science, but also the considerable subtlety and complexity of the dialectic between science and technology. Other concerns include the rapid growth in the development of costs associated with new technologies as well as the difficulty of predicting the eventual performance characteristics of newly emerging technologies. (shrink)

Epidemiological explanation often has a “black box” character, meaning the intermediate steps between cause and effect are unknown. Filling in black boxes is thought to improve causal inferences by making them intelligible. I argue that adding information about intermediate causes to a black box explanation is an unreliable guide to pragmatic intelligibility because it may mislead us about the stability of a cause. I diagnose a problem that I call wishful intelligibility, which occurs when scientists misjudge the (...) limitations of certain features of an explanation. Wishful intelligibility gives us a new reason to prefer black box explanations in some contexts. (shrink)

This paper argues that judges assessing the scientific validity and the legal admissibility of forensic science techniques ought to privilege testing over explanation. Their evaluation of reliability should be more concerned with whether the technique has been adequately validated by appropriate empirical testing than with whether the expert can offer an adequate description of the methods she uses, or satisfactorily explain her methodology or the theory from which her claims derive. This paper explores these issues within two specific contexts: latent (...) fingerprint examination and the use of breath tests for the detection of alcohol. Especially in the forensic science arena, I suggest courts have often been seduced by superficially plausible explanations and descriptions of a technique or method, and permitted these to serve as a substitute for empirical testing. Thinking through these two examples illustrates both why evaluating the extent of testing should be the most important method by which courts assess reliability, and why, when other forms of explanatory evidence are readily available, we may nonetheless elect to make use of them. This paper suggests that these descriptions and explanations may at times usefully supplement evidence of testing, but should not generally be substituted for it. Finally, this paper embraces a kind of evidentiary pragmatism, in which the quantum of evidence required to establish legal reliability is determined not in the abstract, but in relation to the evidence that is, or ought to be, available as a result of reasonable research and investigation. (shrink)

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. (shrink)

The article comments on Peetz’ concept of relational mechanisms. This concept is an alternative to mechanistical explanations of analytical sociology, conceptualized as based on human agents. Peetz criticises this foundation, juxtaposing it with the idea of the analytical primacy of relations. This perspective does not necessarily presuppose agents but can explain their emergence. To demonstrate the efficiency of his concept, he presents an explanation of a concrete mechanism. The analysis of this explanation shows that a crucial point is missing from (...) the concept of relational mechanisms: the steps that produce a social process are never spelt out. Peetz thus presents a black box explanation, which is contrary to the demands of mechanistical explanations. His preference for black box argumentation is owed to his concepts. Unlike an enlightened methodological individualist, he is not in a position to explain the productions necessary for the formation of mechanistical processes. (shrink)