Algorithms have developed into somewhat of a modern myth. On the one hand, they have been depicted as powerful entities that rule, sort, govern, shape, or otherwise control our lives. On the other hand, their alleged obscurity and inscrutability make it difficult to understand what exactly is at stake. What sustains their image as powerful yet inscrutable entities? And how to think about the politics and governance of something that is so difficult to grasp? This editorial essay provides a critical (...) backdrop for the special issue, treating algorithms not only as computational artifacts but also as sensitizing devices that can help us rethink some entrenched assumptions about agency, transparency, and normativity. (shrink)

Algorithms, or rather algorithmic actions, are seen as problematic because they are inscrutable, automatic, and subsumed in the flow of daily practices. Yet, they are also seen to be playing an important role in organizing opportunities, enacting certain categories, and doing what David Lyon calls “social sorting.” Thus, there is a general concern that this increasingly prevalent mode of ordering and organizing should be governed more explicitly. Some have argued for more transparency and openness, others have argued for more democratic (...) or value-centered design of such actors. In this article, we argue that governing practices—of, and through algorithmic actors—are best understood in terms of what Foucault calls governmentality. Governmentality allows us to consider the performative nature of these governing practices. They allow us to show how practice becomes problematized, how calculative practices are enacted as technologies of governance, how such calculative practices produce domains of knowledge and expertise, and finally, how such domains of knowledge become internalized in order to enact self-governing subjects. In other words, it allows us to show the mutually constitutive nature of problems, domains of knowledge, and subjectivities enacted through governing practices. In order to demonstrate this, we present attempts to govern academic writing with a specific focus on the algorithmic action of Turnitin. (shrink)

Algorithms, once obscure objects of technical art, have lately been subject to considerable popular and scholarly scrutiny. What does it mean to adopt the algorithm as an object of analytic attention? What is in view, and out of view, when we focus on the algorithm? Using Niklaus Wirth's 1975 formulation that “algorithms + data structures = programs” as a launching-off point, this paper examines how an algorithmic lens shapes the way in which we might inquire into contemporary digital culture.

Schedule planning is one of the most crucial issues for any airline company, because the profit of the company directly depends on the efficiency of the schedule. This paper presents a novel scheduling method which solves problems related to time scheduling, fleet assignment and maintenance routing simultaneously by Genetic Algorithms. Every schedule constraint is embeded in the fitness function, which is described as an object oriented model and works as a simulater developing itself over time, and whose solution is executable (...) without human correction. The schedular is able to solve the problems involving several hundred flights in a few hours, and the solutions are superior or equivalent to those by human experts in terms of the estimated profit. (shrink)

This article responds to recent debates in critical algorithm studies about the significance of the term “algorithm.” Where some have suggested that critical scholars should align their use of the term with its common definition in professional computer science, I argue that we should instead approach algorithms as “multiples”—unstable objects that are enacted through the varied practices that people use to engage with them, including the practices of “outsider” researchers. This approach builds on the work of Laura Devendorf, Elizabeth Goodman, (...) and Annemarie Mol. Different ways of enacting algorithms foreground certain issues while occluding others: computer scientists enact algorithms as conceptual objects indifferent to implementation details, while calls for accountability enact algorithms as closed boxes to be opened. I propose that critical researchers might seek to enact algorithms ethnographically, seeing them as heterogeneous and diffuse sociotechnical systems, rather than rigidly constrained and procedural formulas. To do so, I suggest thinking of algorithms not “in” culture, as the event occasioning this essay was titled, but “as” culture: part of broad patterns of meaning and practice that can be engaged with empirically. I offer a set of practical tactics for the ethnographic enactment of algorithmic systems, which do not depend on pinning down a singular “algorithm” or achieving “access,” but which rather work from the partial and mobile position of an outsider. (shrink)

Algorithms are powerful because we invest in them the power to do things. With such promise, they can transform the ordinary, say snapshots along a robotic vacuum cleaner’s route, into something much more, such as a clean home. Echoing David Graeber’s revision of fetishism, we argue that this easy slip from technical capabilities to broader claims betrays not the “magic” of algorithms but rather the dynamics of their exchange. Fetishes are not indicators of false thinking, but social contracts in material (...) form. They mediate emerging distributions of power often too nascent, too slippery or too disconcerting to directly acknowledge. Drawing primarily on 2016 ethnographic research with computer vision professionals, we show how faith in what algorithms can do shapes the social encounters and exchanges of their production. By analyzing algorithms through the lens of fetishism, we can see the social and economic investment in some people’s labor over others. We also see everyday opportunities for social creativity and change. We conclude that what is problematic about algorithms is not their fetishization but instead their stabilization into full-fledged gods and demons – the more deserving objects of critique. (shrink)

This comprehensive compendium provides a rigorous framework to tackle the daunting challenges of designing correct and efficient algorithms. It gives a uniform approach to the design, analysis, optimization, and verification of algorithms. The volume also provides essential tools to understand algorithms and their associated data structures. This useful reference text describes a way of thinking that eases the task of proving algorithm correctness. Working through a proof of correctness reveals an algorithm's subtleties in a way that a typical description does (...) not. Algorithm analysis is presented using careful definitions that make the analyses mathematically rigorous. (shrink)

We connect learning algorithms and algorithms automating proof search in propositional proof systems: for every sufficiently strong, well-behaved propositional proof system [Formula: see text], we prove that the following statements are equivalent, (1) Provable learning. [Formula: see text] proves efficiently that p-size circuits are learnable by subexponential-size circuits over the uniform distribution with membership queries. (2) Provable automatability. [Formula: see text] proves efficiently that [Formula: see text] is automatable by non-uniform circuits on propositional formulas expressing p-size circuit lower bounds. Here, (...) [Formula: see text] is sufficiently strong and well-behaved if I.–III. hold: I. [Formula: see text] p-simulates Jeřábek’s system [Formula: see text] (which strengthens the Extended Frege system [Formula: see text] by a surjective weak pigeonhole principle); II. [Formula: see text] satisfies some basic properties of standard proof systems which p-simulate [Formula: see text]; III. [Formula: see text] proves efficiently for some Boolean function [Formula: see text] that [Formula: see text] is hard on average for circuits of subexponential size. For example, if III. holds for [Formula: see text], then Items 1 and 2 are equivalent for [Formula: see text]. We use the following modified notion of automatability in Item 2, the automating circuits output a [Formula: see text]-proof of a given formula (expressing a p-size circuit lower bound for a function [Formula: see text]) in non-uniform p-time in the length of a shortest [Formula: see text]-proof of a closely related but different formula (expressing an average-case subexponential-size circuit lower bound for the same function [Formula: see text]). If there is a function [Formula: see text] which is hard on average for circuits of size [Formula: see text], for each sufficiently big [Formula: see text], then there is an explicit propositional proof system [Formula: see text] satisfying properties I.-III., i.e. the equivalence of Items 1 and 2 holds for [Formula: see text]. (shrink)

Journal of Mathematical Logic, Ahead of Print. We connect learning algorithms and algorithms automating proof search in propositional proof systems: for every sufficiently strong, well-behaved propositional proof system [math], we prove that the following statements are equivalent, (1) Provable learning. [math] proves efficiently that p-size circuits are learnable by subexponential-size circuits over the uniform distribution with membership queries. (2) Provable automatability. [math] proves efficiently that [math] is automatable by non-uniform circuits on propositional formulas expressing p-size circuit lower bounds. Here, [math] (...) is sufficiently strong and well-behaved if I.–III. hold: I. [math] p-simulates Jeřábek’s system [math] (which strengthens the Extended Frege system [math] by a surjective weak pigeonhole principle); II. [math] satisfies some basic properties of standard proof systems which p-simulate [math]; III. [math] proves efficiently for some Boolean function [math] that [math] is hard on average for circuits of subexponential size. For example, if III. holds for [math], then Items 1 and 2 are equivalent for [math]. We use the following modified notion of automatability in Item 2, the automating circuits output a [math]-proof of a given formula (expressing a p-size circuit lower bound for a function [math]) in non-uniform p-time in the length of a shortest [math]-proof of a closely related but different formula (expressing an average-case subexponential-size circuit lower bound for the same function [math]). If there is a function [math] which is hard on average for circuits of size [math], for each sufficiently big [math], then there is an explicit propositional proof system [math] satisfying properties I.-III., i.e. the equivalence of Items 1 and 2 holds for [math]. (shrink)

Big Data evangelists often argue that algorithms make decision-making more informed and objective—a promise hotly contested by critics of these technologies. Yet, to date, most of the debate has focused on the instruments themselves, rather than on how they are used. This article addresses this lack by examining the actual practices surrounding algorithmic technologies. Specifically, drawing on multi-sited ethnographic data, I compare how algorithms are used and interpreted in two institutional contexts with markedly different characteristics: web journalism and criminal justice. (...) I find that there are surprising similarities in how web journalists and legal professionals use algorithms in their work. In both cases, I document a gap between the intended and actual effects of algorithms—a process I analyze as “decoupling.” Second, I identify a gamut of buffering strategies used by both web journalists and legal professionals to minimize the impact of algorithms in their daily work. Those include foot-dragging, gaming, and open critique. Of course, these similarities do not exhaust the differences between the two cases, which are explored in the discussion section. I conclude with a call for further ethnographic work on algorithms in practice as an important empirical check against the dominant rhetoric of algorithmic power. (shrink)

In the technical literature of computer science, the concept of an effective procedure is closely associated with the notion of an instruction that precisely specifies an action. Turing machine instructions are held up as providing paragons of instructions that "precisely describe" or "well define" the actions they prescribe. Numerical algorithms and computer programs are judged effective just insofar as they are thought to be translatable into Turing machine programs. Nontechnical procedures (e.g., recipes, methods) are summarily dismissed as ineffective on the (...) grounds that their instructions lack the requisite precision. But despite the pivotal role played by the notion of a precisely specified instruction in classifying procedures as effective and ineffective, little attention has been paid to the manner in which instructions "precisely specify" the actions they prescribe. It is the purpose of this paper to remedy this defect. The results are startling. The reputed exemplary precision of Turing machine instructions turns out to be a myth. Indeed, the most precise specifications of action are provided not by the procedures of theoretical computer science and mathematics (algorithms) but rather by the nontechnical procedures of everyday life. I close with a discussion of some of the rumifications of these conclusions for understanding and designing concrete computers and their programming languages. (shrink)

I discuss the philosophical implications that the rising new science of quantum computing may have on the philosophy of computer science. While quantum algorithms leave the notion of Turing-Computability intact, they may re-describe the abstract space of computational complexity theory hence militate against the autonomous character of some of the concepts and categories of computer science.

The paper examines some of the processes of the closely knit relationship between Google’s ideologies of neutrality and objectivity and global market dominance. Neutrality construction comprises an important element sustaining the company’s economic position and is reflected in constant updates, estimates and changes to utility and relevance of search results. Providing a purely technical solution to these issues proves to be increasingly difficult without a human hand in steering algorithmic solutions. Search relevance fluctuates and shifts through continuous tinkering and tweaking (...) of the search algorithm. The company also uses third parties to hire human raters for performing quality assessments of algorithmic updates and adaptations in linguistically and culturally diverse global markets. The adaptation process contradicts the technical foundations of the company and calculations based on the initial Page Rank algorithm. Annual market reports, Google’s Search Quality Rating Guidelines, and reports from media specialising in search engine optimisation business are analysed. The Search Quality Rating Guidelines document provides a rare glimpse into the internal architecture of search algorithms and the notions of utility and relevance which are presented and structured as neutral and objective. Intertwined layers of ideology, hidden labour of human raters, advertising revenues, market dominance and control are discussed throughout the paper. (shrink)

This article proposes an analytical approach to algorithms that stresses operations of folding. The aim of this approach is to broaden the common analytical focus on algorithms as biased and opaque black boxes, and to instead highlight the many relations that algorithms are interwoven with. Our proposed approach thus highlights how algorithms fold heterogeneous things: data, methods and objects with multiple ethical and political effects. We exemplify the utility of our approach by proposing three specific operations of folding—proximation, universalisation and (...) normalisation. The article develops these three operations through four empirical vignettes, drawn from different settings that deal with algorithms in relation to AIDS, Zika and stock markets. In proposing this analytical approach, we wish to highlight the many different attachments and relations that algorithms enfold. The approach thus aims to produce accounts that highlight how algorithms dynamically combine and reconfigure different social and material heterogeneities as well as the ethical, normative and political consequences of these reconfigurations. (shrink)

The tendency towards an increasing integration of the informational web into our daily physical world (in particular in so-called Ambient Intelligent technologies which combine ideas derived from the field of Ubiquitous Computing, Intelligent User Interfaces and Ubiquitous Communication) is likely to make the development of successful profiling and personalization algorithms, like the ones currently used by internet companies such as Amazon, even more important than it is today. I argue that the way in which we experience ourselves necessarily goes through (...) a moment of technical mediation. Because of this algorithmic profiling that thrives on continuous reconfiguration of identification should not be understood as a supplementary process which maps a pre-established identity that exists independently from the profiling practice. In order to clarify how the experience of one’s identity can become affected by such machine-profiling a theoretical exploration of identity is made (including Agamben’s understanding of an apparatus, Ricoeur’s distinction between idem- and ipse-identity, and Stiegler’s notion of a conjunctive–disjunctive relationship towards retentional apparatuses). Although it is clear that no specific predictions about the impact of Ambient Intelligent technologies can be made without taking more particulars into account, the theoretical concepts are used to describe three general scenarios about the way wherein the experience of identity might become affected. To conclude, I argue that the experience of one’s identity may affect whether the cases of unwarranted discrimination resulting from ubiquitous differentiations and identifications within an Ambient Intelligent environment, will become a matter of societal concern. (shrink)

Artificial Intelligence (AI) is predicted to play an increasingly central role in warfare, with weaponized robots taking over more battlefield operations, and military algorithms mediating in, or substituting for, human decision-making in areas such as intelligence collection and analysis, targeting, and strategic decision-making. The primary focus of this article is the potential impact of the widespread use of AI systems on soldiers and military leaders themselves – namely, on their moral character, skills, emotions, and agency – and on how they (...) are perceived by the society which they defend. Increasing the use of AI in the military may undermine the conditions necessary for the cultivation and practice of martial virtues and commitment to the common good. The essay focuses on the three virtues of courage, deep loyalty, and prudence or practical wisdom. It then asks what kinds of “technocratic” vices might be encouraged in warfighters by the use of AI systems. It closes by analyzing the wider social and political implications of an expansive use of AI in the military. The risk is that, as AI takes over more military functions, the military ceases to be seen as a civic and republican institution and instead is seen as a mere extension of technocratic power. (shrink)

This essay addresses a version of Jerome Frank’s question – ‘Are Judges Human?’ – asking instead: are human judges necessary? It begins, in section II, by outlining the technological developments which inform the view that they are not and critically evaluates the juristic position that seemingly endorses it. That position is labelled ‘technological evangelism’ and it consists of three claims about law and adjudication: the certainty, determinacy and partiality claims. Section III shows that these three claims are utterly incompatible with (...) what it calls standard and non-standard views of adjudication and law, while section IV considers some ways in which proponents of technological evangelism might try to reject standard and non-standard views. That section concludes that no plausible efforts have so far been made by technological evangelists to reject standard and non-standard views, and that those views therefore maintain their existing explanatory and normative priority. The overall conclusion of the essay is that technological evangelism is not a critical explanatory and normative engagement with law and adjudication as we know them, but an effort to replace them: not a game-changing intervention, but a game-ending one. (shrink)

The paper deals with the difference between who and what we are in order to take an ethical perspective on algorithms and their regulation. The present casting of ourselves as homo digitalis implies the possibility of projecting who we are as social beings sharing a world, into the digital medium, thereby engendering what can be called digital whoness, or a digital reification of ourselves. A main ethical challenge for the evolving digital age consists in unveiling this ethical difference, particularly when (...) dealing with algorithms and their regulation in the context of human relationships. The paper addresses by way of example some issues raised by autonomous cars. (shrink)

In this paper we present some algorithms for computing minimal conflicts. First of all we discuss the relationship between minimal conflicts and minimally inconsistent subsets. Then we introduce an algorithm for computing all minimally inconsistent subsets, which is applied to generating all minimal conflicts. Furthermore, an algorithm for computing all minimal conflicts using structured description is introduced, and its correctness is proved; its time complexity is also shown. The algorithm using structured description terminates in polynomial time for some special system, (...) such as the tree–structured systems. The algorithms are also complared with related works. (shrink)

We argue that an essential element of understanding the moral salience of algorithmic systems requires an analysis of the relation between algorithms and agency. We outline six key ways in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making.

We introduce a reciprocity criterion for coalition formation among goal-directed agents, which we call the indecomposable do-ut-des property. It refines an older reciprocity property, called the do-ut-des or give-to-get property by considering the fact that agents prefer to form coalitions whose components cannot be formed independently. A formal description of this property is provided as well as an analysis of algorithms and their complexity. We provide an algorithm to decide whether a coalition has the desired property, and we show that (...) the problem to verify whether a single coalition satisfies the property is tractable. Moreover, we provide an algorithm to search all the sub-coalitions of a given coalition satisfying the new property. Even if this problem is not computationally tractable, we show that in several cases, also the complexity of this problem may decrease considerably. (shrink)

Work in online learning traditionally considered induction-friendly (e.g. stochastic with a fixed distribution) and induction-hostile (adversarial) settings separately. While algorithms like Exp3 that have been developed for the adversarial setting are applicable to the stochastic setting as well, the guarantees that can be obtained are usually worse than those that are available for algorithms that are specifically designed for stochastic settings. Only recently, there is an increasing interest in algorithms that give (near-)optimal guarantees with respect to the underlying setting, even (...) in case its nature is unknown to the learner. In this paper, we review various online learning algorithms that are able to adapt to the hardness of the underlying problem setting. While our focus lies on the application of adaptive algorithms as meta-inductive methods that combine given base methods, concerning theoretical properties we are also interested in guarantees that go beyond a comparison to the best fixed base learner. (shrink)

The ideas of quantum simulation and advances in quantum algorithms to solve quantum chemistry problems have been discussed. Theoretical proposals and experimental investigations both have been studied to gauge the extent to which quantum computation has been applied to solve quantum chemical problems till date. The distinctive features and limitations of the application of quantum simulation on chemical systems and current approaches to define and improve upon standard quantum algorithms have been studied in detail. The possibility and consequences of designing (...) an efficient quantum computer that can address chemical problems have been assessed. The experimental realization of quantum supremacy defies the conventional belief of chemists, that millions of qubits would be required to solve fundamental chemistry problems. It is predicted that quantum simulation of quantum chemistry problems will radically revolutionize this field. (shrink)

Two definitions of algorithm, their uses, and their implied models of computing in society, are reviewed. The first, termed the structural programming definition, aligns more with usage in computer science, and as the name suggests, the intellectual project of structured programming. The second, termed the systemic definition, is more informal and emerges from ethnographic observations of discussions of software in both professional and everyday settings. Specific examples of locating algorithms within modern codebases are shared, as well as code directly impacting (...) social and ethical concerns. The structural distinction between algorithms and social concerns is explained as mirroring the engineering construct of algorithms and data structures. It is proposed that, rather than this separation being an attempt to enforce a professional boundary and evade social responsibility, it is a crucial technical distinction within code which makes it clearer and more transparent. The power structures reinforced by the broader, cultural interpretations of algorithm are reconsidered, along with what it would mean for software to have an inclusive design culture. (shrink)

In this paper, I argue that accurate indexical representations have been crucial for the survival and reproduction of homo sapiens sapiens. Specifically, I want to suggest that reliable processes have been selected for because of their indirect, but close, connection to true belief during the Pleistocene hunter-gatherer period of our ancestral history. True beliefs are not heritable, reliable processes are heritable. Those reliable processes connected with reasoning take the form of Darwinian Algorithms: a plethora of specialized, domain-specific inference rules designed (...) to solve specific, recurrent, adaptive problems in social exchange contexts. Humans do not reason logically, but adaptively. (shrink)

Part of understanding the meaning and power of algorithms means asking what new demands they might make of ethical frameworks, and how they might be held accountable to ethical standards. I develop a definition of networked information algorithms as assemblages of institutionally situated code, practices, and norms with the power to create, sustain, and signify relationships among people and data through minimally observable, semiautonomous action. Starting from Merrill’s prompt to see ethics as the study of “what we ought to do,” (...) I examine ethical dimensions of contemporary NIAs. Specifically, in an effort to sketch an empirically grounded, pragmatic ethics of algorithms, I trace an algorithmic assemblage’s power to convene constituents, suggest actions based on perceived similarity and probability, and govern the timing and timeframes of ethical action. (shrink)

Recently, certain philosophers of mathematics (Fallis [1997]; Womack and Farach [(1997]) have argued that there are no epistemic considerations that should stop mathematicians from using probabilistic methods to establish that mathematical propositions are true. However, mathematicians clearly should not use methods that are unreliable. Unfortunately, due to the fact that randomized algorithms are not really random in practice, there is reason to doubt their reliability. In this paper, I analyze the prospects for establishing that randomized algorithms are reliable. I end (...) by arguing that it would be inconsistent for mathematicians to suspend judgement on the truth of mathematical propositions on the basis of worries about the reliability of randomized algorithms. (shrink)

The power of algorithms has become a familiar topic in society, media, and the social sciences. It is increasingly common to argue that, for instance, algorithms automate inequality, that they are biased black boxes that reproduce racism, or that they control our money and information. Implicit in many of these discussions is that algorithms are permeated with normativities, and that these normativities shape society. The aim of this editorial is double: First, it contributes to a more nuanced discussion about algorithms (...) by discussing how we, as social scientists, think about algorithms in relation to five theoretical ideal types. For instance, what does it mean to go under the hood of the algorithm and what does it mean to stay above it? Second, it introduces the contributions to this special theme by situating them in relation to these five ideal types. By doing this, the editorial aims to contribute to an increased analytical awareness of how algorithms are theorized in society and culture. The articles in the special theme deal with algorithms in different settings, ranging from farming, schools, and self-tracking to AIDS, nuclear power plants, and surveillance. The contributions thus explore, both theoretically and empirically, different settings where algorithms are intertwined with normativities. (shrink)

Algorithms play an increasing role within the process of digitalization of society. They are used for descriptions and predictions of consumers’ or citizens’ behavior. Sociologist Elena ….

Description logics are a family of knowledge representation formalisms that are descended from semantic networks and frames via the system Kl-one. During the last decade, it has been shown that the important reasoning problems (like subsumption and satisfiability) in a great variety of description logics can be decided using tableau-like algorithms. This is not very surprising since description logics have turned out to be closely related to propositional modal logics and logics of programs (such as propositional dynamic logic), for which (...) tableau procedures have been quite successful.Nevertheless, due to different underlying institutions and applications, most description logics differ significantly from run-of-the-mill modal and program logics. Consequently, the research on tableau algorithms in description logics led to new techniques and results, which are, however, also of interest for modal logicians. In this article, we will focus on three features that play an important rôle in description logics (number restrictions, terminological axioms, and role constructors), and show how they can be taken into account by tableau algorithms. (shrink)

Algorithms and data-driven technologies are increasingly being embraced by a variety of different sectors and institutions. This paper examines how algorithms and data-driven technologies, enacted by an organization like Facebook, can induce similarity across an industry. Using theories from organizational sociology and neoinstitutionalism, this paper traces the bureaucratic roots of Big Data and algorithms to examine the institutional dependencies that emerge and are mediated through data-driven and algorithmic logics. This type of analysis sheds light on how organizational contexts are embedded (...) into algorithms, which can then become embedded within other organizational and individual practices. By investigating technical practices as organizational and bureaucratic, discussions about accountability and decision-making can be reframed. (shrink)

In science and technology studies today, there is a troubling tendency to portray actors in the biosciences as “cultural dopes” and technology as having monolithic qualities with predetermined outcomes. To remedy this analytical impasse, this article introduces the concept styles of valuation to analyze how actors struggle with valuing technology in practice. Empirically, this article examines how actors in a bioscientific laboratory struggle with valuing the properties and qualities of algorithms in a high-throughput setting and identifies the copresence of several (...) different styles. The question that the actors struggle with is what different configurations of algorithms, devices, and humans are “good bioscience,” that is, what do the actors perform as a good distribution of agency between algorithms and humans? A key finding is that algorithms, robots, and humans are valued in multiple ways in the same setting. For the actors, it is not apparent which configuration of agency and devices is more authoritative nor is it obvious which skills and functions should be redistributed to the algorithms. Thus, rather than tying algorithms to one set of values, such as “speed,” “precision,” or “automation,” this article demonstrates the broad utility of attending to the multivalence of algorithms and technology in practice. (shrink)

Institutions are increasingly employing algorithms to provide performance feedback to individuals by tracking productivity, conducting performance appraisals, and developing improvement plans, compared to traditional human managers. However, this shift has provoked considerable debate over the effectiveness and fairness of algorithmic feedback. This study investigates the effects of negative performance feedback (NPF) on the attitudes, cognition and behavior of medical researchers, comparing NPF from algorithms versus humans. Two scenario-based experimental studies were conducted with a total sample of 660 medical researchers (algorithm (...) group: N1 = 411; human group: N2 = 249). Study 1 analyzes the differences in scientific misconduct, moral disengagement, and algorithmic attitudes between the two sources of NPF. The findings reveal that NPF from algorithms shows higher levels of moral disengagement, scientific misconduct, and negative attitudes towards algorithms compared to NPF from humans. Study 2, grounded in trait activation theory, investigates how NPF from algorithms triggers individual’s egoism and algorithm aversion, potentially leading to moral disengagement and scientific misconduct. Results indicate that algorithm aversion triggers individuals’ egoism, and their interaction enhances moral disengagement, which in turn leads to increased scientific misconduct among researchers. This relationship is also moderated by algorithmic transparency. The study concludes that while algorithms can streamline performance evaluations, they pose significant risks to scientific misconduct of researchers if not properly designed. These findings extend our understanding of NPF by highlighting the emotional and cognitive challenges algorithms face in decision-making processes, while also underscoring the importance of balancing technological efficiency with moral considerations to promote a healthy research environment. Moreover, managerial implications include integrating human oversight in algorithmic NPF processes and enhancing transparency and fairness to mitigate negative impacts on medical researchers’ attitudes and behaviors. (shrink)

In "The Status and Future of the Turing Test" (Moor, 2001), which appeared in an earlier issue of this journal, James Moor remarks on my paper "Turing's Two Tests for Intelligence." In my paper I had claimed that, whatever Turing may or may not have thought, the test described in the opening section of Turing's now legendary 1950 paper "Computing Machinery and Intelligence" is not equivalent to, and in fact is superior to, the test described in a passage that occurs (...) much later in Turing's paper (i.e., in Section 5 of Turing, 1950). I'm pleased Moor chose to give such prominence to my point, and very happy to see that he recognized that my claim was a normative one about the superiority of one test over another, rather than a claim about Turing's intentions. However, as I think the way he describes my point could lead to misunderstandings, I'd like to clarify the points I made. One major clari?cation is which two tests I am contrasting; another is that the difference in overall structure of the two tests is of philosophical signi?cance. (shrink)