In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields—ideas about how data science and its critics can and should relate. Inspired (...) by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on—which do not paper over them but also do not take them as given. (shrink)

Student performance and its evaluation remain a serious challenge for education systems. Frequently, the recording and processing of students’ scores in a specific curriculum have several flaws for various reasons. In this context, the absence of data from some of the student scores undermines the efficiency of any future analysis carried out in order to reach conclusions. When this is the case, missing data imputation algorithms are needed. These algorithms are capable of substituting, with a high level of accuracy, the (...) missing data for predicted values. This research presents the hybridization of an algorithm previously proposed by the authors called adaptive assignation algorithm, with a well-known technique called multivariate imputation by chained equations. The results show how the suggested methodology outperforms both algorithms. (shrink)

Describing an algorithm can provide a formalization of a specific process. However, different ways of conceptualizing algorithms foreground certain issues while obscuring others. This article attempts to define an algorithm in a broad sense as a cultural activity of key importance to make sense of socio-cognitive structures. It also attempts to develop a sharper account on the interaction between humans and tools, symbols and technologies. Rather than human or machine-centered analyses, I draw upon sociological and anthropological theories that (...) underline social practices to propose expanding our understanding of an algorithm through the notion of ‘collective intentionalities’. To make this term clear, a brief historical review is presented, followed by an argumentation on how to incorporate it in an integral perspective. The article responds to recent debates in critical algorithm studies about the significance of the term. It develops a discussion along the lines of cognitive anthropology and the cognitive sciences, therefore advancing a definition that is grounded in observed practices as well as in modeled descriptions. The benefit of this approach is that it encourages scholars to explore cognitive structures via archaeologies of technological assemblages, where intentionalities play a defining role in understanding socio-structured practices and cognitive ecologies. (shrink)

Interval type-2 fuzzy logic systems have favorable abilities to cope with uncertainties in many applications. While the block type-reduction under the guidance of inference plays the central role in the systems, Karnik-Mendel iterative algorithms are standard algorithms to perform the type-reduction; however, the high computational cost of type-reduction process may hinder them from real applications. The comparison between the KM algorithms and other alternative algorithms is still an open problem. This paper introduces the related theory of interval type-2 fuzzy sets (...) and discusses the blocks of fuzzy reasoning, type-reduction, and defuzzification of interval type-2 fuzzy logic systems by combining the Nagar-Bardini and Nie-Tan noniterative algorithms for solving the centroids of output interval type-2 fuzzy sets. Moreover, the continuous version of NT algorithms is proved to be accurate algorithms for performing the type-reduction. Four computer simulation examples are provided to illustrate and analyze the performances of two kinds of noniterative algorithms. The NB and NT algorithms are superior to the KM algorithms on both calculation accuracy and time, which afford the potential application value for designer and adopters of type-2 fuzzy logic systems. (shrink)

Data-driven algorithms are currently deployed in several fields, leading to a rapid increase in the importance algorithms have in decision-making processes. Over the last years, several instances of discrimination by algorithms were observed. A new branch of research emerged to examine the concept of “algorithmic fairness.” No consensus currently exists on a single operationalization of fairness, although causal-based definitions are arguably more aligned with the human conception of fairness. The aim of this article is to investigate the degree of this (...) alignment in a case study inspired by a recent ruling of an Italian court on the reputational-ranking algorithm used by a food delivery platform. I relied on the documentation of the legal dispute to discuss the applicability, intuitiveness and appropriateness of causal models in evaluating fairness, with a specific focus on a causal-based fairness definition called “counterfactual fairness.” I first describe the details of the dispute and the arguments presented to the court, as well as the court’s final decision, to establish the context of the case study. Then, I translate the dispute into a formal simplified problem using a causal diagram, which represents the main aspects of the data generation process in the case study. I identify the criteria used by the court in ruling that the algorithm was unfair and compare them with the counterfactual fairness definition. The definition of counterfactual fairness was found to be well aligned with the human conception of fairness in this case study, using the court order rationale as a gold standard. (shrink)

In order to improve the translation quality of complex English sentences, this paper investigated unknown words. First, two baseline models, the recurrent neural machine translation (RNMT) model and the transformer model, were briefly introduced. Then, the unknown words were identified and replaced based on WordNet and the semantic environment and input to the neural machine translation (NMT) model for translation. Finally, experiments were conducted on several National Institute of Standards and Technology (NIST) datasets. It was found that the transformer model (...) significantly outperformed the RNMT model, its average bilingual evaluation understudy (BLEU) value was 42.14, which was 6.96 higher than the RNMT model, and its translation error rate (TER) was also smaller. After combining the intelligent algorithm, the BLEU values of both models improved, and the TER became smaller; the average BLEU value of the transformer model combined with the intelligent algorithm was 43.7, and the average TER was 57.68. The experiment verifies that the transformer model combined with the intelligent algorithm is reliable in translating complex sentences and can obtain higher-quality translation results. (shrink)

Growth mixture models are regularly applied in the behavioral and social sciences to identify unknown heterogeneous subpopulations that follow distinct developmental trajectories. Marcoulides and Trinchera recently proposed a mixture modeling approach that examines the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The purpose of this article was to conduct a simulation study that examines the performance of this new approach for determining (...) the number of classes in growth mixture models. The performance of the approach to correctly identify the number of classes is examined under a variety of longitudinal data design conditions. The findings demonstrated that the new approach was a very dependable indicator of classes across all the design conditions considered. (shrink)

In this paper, we study the effects of using an algorithm-based risk assessment instrument (RAI) to support the prediction of risk of violent recidivism upon release. The instrument we used is a machine learning version of RiskCanvi used by the Justice Department of Catalonia, Spain. It was hypothesized that people can improve their performance on defining the risk of recidivism when assisted with a RAI. Also, that professionals can perform better than non-experts on the domain. Participants had to predict (...) whether a person who has been released from prison will commit a new crime leading to re-incarceration, within the next two years. This user study is done with (1) general participants from diverse backgrounds recruited through a crowdsourcing platform, (2) targeted participants who are students and practitioners of data science, criminology, or social work and professionals who work with RisCanvi. We also run focus groups with participants of the targeted study, including people who use RisCanvi in a professional capacity, to interpret the quantitative results. Among other findings, we observe that algorithmic support systematically leads to more accurate predictions from all participants, but that statistically significant gains are only seen in the performance of targeted participants with respect to that of crowdsourced participants. Among other comments, professional participants indicate that they would not foresee using a fully-automated system in criminal risk assessment, but do consider it valuable for training, standardization, and to fine-tune or double-check their predictions on particularly difficult cases. We found that the revised prediction by using a RAI increases the performance of all groups, while professionals show a better performance in general. And, a RAI can be considered for extending professional capacities and skills along their careers. (shrink)

Executives in business and government seek to leverage artificial intelligence (AI), the biggest driver of technological change, to inform decision-making. The intelligence behind AI comes from machine learning (ML) algorithms applied to large datasets. The goal of this research is to investigate the adage that while humans are fallible, computers are impartial with no implicit bias. Toward this purpose, the author used the Delphi research technique to achieve these three objectives: (1) identify and categorize the sources of flaws in (...) class='Hi'>algorithm design; (2) validate a framework for auditing AI-powered systems; and (3) propose strategies for resolving the sources and mitigating the flaws in algorithm design. The paper begins with a concise theoretical framework for algorithm design to familiarize the readers with the science of ML algorithms. Next, the author describes the research methodology including population and sample of the study. The findings of the Delphi study are presented in three sections that correspond with the three objectives of this investigation. A discussion of the findings is enhanced with supporting evidence from existing literature of unintended undesirable societal impacts of flaws in algorithm design in fields like education, healthcare, criminal justice, human resource management, and financial services. The expected outcome is cautious use of AI by decision-makers—employing AI while being aware of its limitations and shortcomings. (shrink)

Algorithmic fairness is a new interdisciplinary field of study focused on how to measure whether a process, or algorithm, may unintentionally produce unfair outcomes, as well as whether or how the potential unfairness of such processes can be mitigated. Statistical discrimination describes a set of informational issues that can induce rational (i.e., Bayesian) decision-making to lead to unfair outcomes even in the absence of discriminatory intent. In this article, we provide overviews of these two related literatures and draw connections (...) between them. The comparison illustrates both the conflict between rationality and fairness and the importance of endogeneity (e.g., “rational expectations” and “self-fulfilling prophecies”) in defining and pursuing fairness. Taken in concert, we argue that the two traditions suggest a value for considering new fairness notions that explicitly account for how the individual characteristics an algorithm intends to measure may change in response to the algorithm. (shrink)

AI technology is capturing the African imaginations as a gateway to progress and prosperity. There is a growing interest in AI by different actors across the continent including scientists, researchers, humanitarian and aid organizations, academic institutions, tech start-ups, and media organizations. Several African states are looking to adopt AI technology to capture economic growth and development opportunities. On the other hand, African researchers highlight the gap in regulatory frameworks and policies that govern the development of AI in the continent. They (...) argue that this could lead to AI technology exacerbating problems of inequalities and injustice in the continent. However, most of the literature on AI ethics is biased toward Euro-American perspectives and lack the understanding of how AI development is apprehended in the Global South, and particularly Africa. Drawing on the case study of the first African Master’s in Machine Intelligence program, this paper argues for looking beyond the question of ethics in AI and examining AI governance issues through the analytical lens of the raciality of computing and the political economy of technoscience to understand AI development in Africa. By doing so, this paper seeks a different theorization for AI ethics from the South that is based on lived experiences of those in the margins and avoids the framings of technological futures that simplistically pathologize or celebrate Africa. (shrink)

We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic (...) governance structures are both? This article shares the results of a collective intelligence workshop that addressed exactly this question. The workshop brought together a multidisciplinary group of scholars to consider barriers to legitimate and effective algorithmic governance and the research methods needed to address the nature and impact of specific barriers. An interactive management workshop technique was used to harness the collective intelligence of this multidisciplinary group. This method enabled participants to produce a framework and research agenda for those who are concerned about algorithmic governance. We outline this research agenda below, providing a detailed map of key research themes, questions and methods that our workshop felt ought to be pursued. This builds upon existing work on research agendas for critical algorithm studies in a unique way through the method of collective intelligence. (shrink)

Scholars of critical algorithmic studies, including those from geography, anthropology, Science and Technology Studies and communication studies, have begun to consider how algorithmic devices and platforms facilitate democratic practices. In this article, I draw on a comparative ethnography of two alternative open-source algorithmic platforms – Decide Madrid and vTaiwan – to consider how they are dynamically constituted by differing algorithmic–human relationships. I compare how different algorithmic–human relationships empower citizens to influence political decision-making through proposing, commenting, and voting (...) on the urban issues that should receive political resources in Taipei and Madrid. I argue that algorithmic empowerment is an emerging process in which algorithmic–human relationships orient away from limitations and towards conditions of plurality, actionality, and power decentralisation. This argument frames algorithmic empowerment as bringing about empowering conditions that allow (underrepresented) individuals to shape policy-making and consider plural perspectives for political change and action, not as an outcome-driven, binary assessment (i.e. yes/no). This article contributes a novel, situated, and comparative conceptualisation of algorithmic empowerment that moves beyond technological determinism and universalism. (shrink)

This unique volume is an introduction for computer scientists, including a formal study of theoretical algorithms for Big Data applications, which allows them to work on such algorithms in the future. It also serves as a useful reference guide for the general computer science population, providing a comprehensive overview of the fascinating world of such algorithms. To achieve these goals, the algorithmic results presented have been carefully chosen so that they demonstrate the important techniques and tools used in Big Data (...) algorithms, and yet do not require tedious calculations or a very deep mathematical background. (shrink)

Ethnographic study of the constitution of algorithms.

Aiming at the problem that it is difficult to balance the speed and accuracy of human behaviour recognition, this paper proposes a method of motion recognition based on random projection. Firstly, the optical flow picture and Red, Green, Blue picture obtained by the Lucas-Kanade algorithm are used. Secondly, the data of optical flow pictures and RGB pictures are compressed based on a random projection matrix of compressed sensing, which effectively reduces power consumption. At the same time, based on random (...) projection compression data, it can effectively find the optimal linear representation to reconstruct training samples and test samples. Thirdly, a multichannel 3D convolutional neural network is proposed, and the multiple information extracted by the network is fused to form an output recognizer. Experimental results show that the algorithm in this paper significantly improves the recognition rate of multicategory actions and effectively reduces the computational complexity and running time of the recognition algorithm. (shrink)

There are many algorithm texts that provide lots of well-polished code and proofs of correctness. Instead, this book presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author helps students avoid the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. Part of the (...) goal is to teach the students to think abstractly. Without getting bogged with formal proofs, the book fosters a deeper understanding of how and why each algorithm works. These insights are presented in a slow and clear manner accessible to second- or third-year students of computer science, preparing them to find their own innovative ways to solve problems. (shrink)

Based on a deliberative democracy, this book uses a hermeneutic-critical methodology to study bibliographical sources and practical issues in order to analyse the possibilities, limits and consequences of the digital transformation of democracy. Drawing on a two-way democracy, the aim of this book is intended as an aid for thinking through viable alternatives to the current state of democracy with regard to its ethical foundations and the moral knowledge implicit in or assumed by the way we perceive and understand democracy. (...) It is intended to stimulate reflection and discussion on the basis that, by addressing what we understand as democracy, we can inevitably influence the reality known as democracy. Democracy’s evident regression in today’s world makes this all too apparent: it has become a hostage to all kinds of autocracies and technopopulisms, which are supported to a greater or lesser extent by the current algorithmic revolution. (shrink)

Problem solving is an essential part of every scientific discipline. It has two components: (1) problem identification and formulation, and (2) the solution to the formulated problem. One can solve a problem on its own using ad hoc techniques or by following techniques that have produced efficient solutions to similar problems. This requires the understanding of various algorithm design techniques, how and when to use them to formulate solutions, and the context appropriate for each of them. Algorithms: Design Techniques (...) and Analysis advocates the study of algorithm design by presenting the most useful techniques and illustrating them with numerous examples -- emphasizing on design techniques in problem solving rather than algorithms topics like searching and sorting. Algorithmic analysis in connection with example algorithms are explored in detail. Each technique or strategy is covered in its own chapter through numerous examples of problems and their algorithms. Readers will be equipped with problem solving tools needed in advanced courses or research in science and engineering. (shrink)

The algorithmic accountability literature to date has primarily focused on procedural tools to govern automated decision-making systems. That prescriptive literature elides a fundamentally empirical question: whether and under what circumstances, if any, is the use of algorithmic systems to make public policy decisions perceived as legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the relative importance of the type of decision, the procedural governance, the input data used, and outcome errors on perceptions (...) of the legitimacy of algorithmic public policy decisions as compared to similar human decisions. Among other findings, we find that the type of decision—low importance versus high importance—impacts the perceived legitimacy of automated decisions. We find that human governance of algorithmic systems increases perceptions of the legitimacy of algorithmic decision-making systems, even when those decisions are likely to result in significant errors. Notably, we also find the penalty to perceived legitimacy is greater when human decision-makers make mistakes than when algorithmic systems make the same errors. The positive impact on perceived legitimacy from governance—such as human-in-the-loop—is greatest for highly pivotal decisions such as parole, policing, and healthcare. After discussing the study’s limitations, we outline avenues for future research. (shrink)

The danger of human operators devolving responsibility to machines and failing to detect cases where they fail has been recognised for many years by industrial psychologists and engineers studying the human operators of complex machines. We call it “the control problem”, understood as the tendency of the human within a human–machine control loop to become complacent, over-reliant or unduly diffident when faced with the outputs of a reliable autonomous system. While the control problem has been investigated for some time, up (...) to this point its manifestation in machine learning contexts has not received serious attention. This paper aims to fill that gap. We argue that, except in certain special circumstances, algorithmic decision tools should not be used in high-stakes or safety-critical decisions unless the systems concerned are significantly “better than human” in the relevant domain or subdomain of decision-making. More concretely, we recommend three strategies to address the control problem, the most promising of which involves a complementary coupling between highly proficient algorithmic tools and human agents working alongside one another. We also identify six key principles which all such human–machine systems should reflect in their design. These can serve as a framework both for assessing the viability of any such human–machine system as well as guiding the design and implementation of such systems generally. (shrink)

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, (...) lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries. (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)

Recently, a multinomial process tree model was developed to measure an agent’s consequence sensitivity, norm sensitivity, and generalized inaction/action preferences when making moral decisions (CNI model). However, the CNI model presupposed that an agent considersconsequences—norms—generalizedinaction/actionpreferences sequentially, which is untenable based on recent evidence. Besides, the CNI model generates parameters at the group level based on binary categorical data. Hence, theC/N/Iparameters cannot be used for correlation analyses or other conventional research designs. To solve these limitations, we developed the CAN algorithm (...) to computenormandconsequencesensitivities and overallaction/inactionpreferences algebraically in a parallel manner. We re-analyzed the raw data of the original CNI model to test the methodological predictions. Our results demonstrate that: (1) theCparameter is approximately equal between the CNI model and CAN algorithm; (2) theNparameter under the CNI model approximately equalsN/(1 −C) under the CAN algorithm; (3) theIparameter andAparameter are reversed around 0.5 – the larger theIparameter, the more the generalized inactionversusaction preference and the larger theAparameter, the more overall actionversusinaction preference; (4) tests of differences in parameters between groups with the CNI model and CAN algorithm led to almost the same statistical conclusion; (5) parameters from the CAN algorithm can be used for correlational analyses and multiple comparisons, and this is an advantage over the parameters from the CNI model. The theoretical and methodological implications of our study were also discussed. (shrink)

When Artificial Intelligence (AI) is applied in decision-making that affects people’s lives, it is now well established that the outcomes can be biased or discriminatory. The question of whether algorithms themselves can be among the sources of bias has been the subject of recent debate among Artificial Intelligence researchers, and scholars who study the social impact of technology. There has been a tendency to focus on examples, where the data set used to train the AI is biased, and denial on (...) the part of some researchers that algorithms can also be biased. Here we illustrate the point that algorithms themselves can be the source of bias with the example of collaborative filtering algorithms for recommendation and search. These algorithms are known to suffer from cold-start, popularity, and homogenizing biases, among others. While these are typically described as statistical biases rather than biases of moral import; in this paper we show that these statistical biases can lead directly to discriminatory outcomes. The intuitive idea is that data points on the margins of distributions of human data tend to correspond to marginalized people. The statistical biases described here have the effect of further marginalizing the already marginal. Biased algorithms for applications such as media recommendations can have significant impact on individuals’ and communities’ access to information and culturally-relevant resources. This source of bias warrants serious attention given the ubiquity of algorithmic decision-making. (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)

A crucial question for artificial cognition systems is what meaning is and how it arises. In pursuit of that question, this paper extends earlier work in which we show that emergence of simple signaling in biologically inspired models using arrays of locally interactive agents. Communities of "communicators" develop in an environment of wandering food sources and predators using any of a variety of mechanisms: imitation of successful neighbors, localized genetic algorithms and partial neural net training on successful neighbors. Here we (...) focus on environmental variability, comparing results for environments with (a) constant resources, (b) random resources, and (c) cycles of "boom and bust." In both simple and complex models across all three mechanisms of strategy change, the emergence of communication is strongly favored by cycles of "boom and bust." These results are particularly intriguing given the importance of environmental variability in fields as diverse as psychology, ecology and cultural anthropology. (shrink)

This study mainly analyzed the keyword extraction of English text. First, two commonly used algorithms, the term frequency–inverse document frequency (TF–IDF) algorithm and the keyphrase extraction algorithm (KEA), were introduced. Then, an improved TF–IDF algorithm was designed, which improved the calculation of word frequency, and it was combined with the position weight to improve the performance of keyword extraction. Finally, 100 English literature was selected from the British Academic Written English Corpus for the analysis experiment. The results (...) showed that the improved TF–IDF algorithm had the shortest running time and took only 4.93 s in processing 100 texts; the precision of the algorithms decreased with the increase of the number of extracted keywords. The comparison between the two algorithms demonstrated that the improved TF–IDF algorithm had the best performance, with a precision rate of 71.2%, a recall rate of 52.98%, and an F 1 score of 60.75%, when five keywords were extracted from each article. The experimental results show that the improved TF–IDF algorithm is effective in extracting English text keywords, which can be further promoted and applied in practice. (shrink)

Dehumanization by algorithms raises important issues for business and society. Yet, these issues remain poorly understood due to the fragmented nature of the evolving dehumanization literature across disciplines, originating from colonialism, industrialization, post-colonialism studies, contemporary ethics, and technology studies. This article systematically reviews the literature on algorithms and dehumanization (n = 180 articles) and maps existing knowledge across several clusters that reveal its underlying characteristics. Based on the review, we find that algorithmic dehumanization is particularly problematic for human (...) resource management and the future of work, managerial decision-making, consumer ethics, hard- and soft-law regulation, and basic values, including privacy and consumer rights. From the literature synthesis, we also derive the following definition of algorithmic dehumanization: the act of using algorithms and data in a way that results in the intentional or unintentional treatment of individuals and/or groups as less than fully human, thereby violating human rights, including liberty, equality, and dignity. Ultimately, we present a dehumanization avoidance model that serves as a structured research agenda and practical guide to address the challenges raised by algorithmic dehumanization. Thus, the model indicates promising pathways for future research at the intersection of AI and society and facilitates reflection on organizational processes that support improving corporate capabilities and managerial decision-making to avoid algorithmic dehumanization. (shrink)

A fixed-parameter is an algorithm that provides an optimal solution to a combinatorial problem. This research-level text is an application-oriented introduction to the growing and highly topical area of the development and analysis of efficient fixed-parameter algorithms for hard problems. The book is divided into three parts: a broad introduction that provides the general philosophy and motivation; followed by coverage of algorithmic methods developed over the years in fixed-parameter algorithmics forming the core of the book; and a discussion of (...) the essential from parameterized hardness theory with a focus on W [1]-hardness, which parallels NP-hardness, then stating some relations to polynomial-time approximation algorithms, and finishing up with a list of selected case studies to show the wide range of applicability of the presented methodology. Aimed at graduate and research mathematicians, programmers, algorithm designers and computer scientists, the book introduces the basic techniques and results and provides a fresh view on this highly innovative field of algorithmic research. (shrink)

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using these (...) case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core. (shrink)

Although overarching if not foundational conceptualizations of digital governance in the field of critical data studies aptly account for and explain subjection, calculated resistance is left conceptually unattended despite case studies that document instances of resistance. I ask at the outset why conceptualizations of digital governance are so bleak, and I argue that all are underscored implicitly by a Deleuzian theory of desire that overlooks agency, defined here in Foucauldian terms. I subsequently conceptualize digital governance as encompassing subjection (...) as well as resistance, and I cast the two in relational perspective by making use of the concepts “affordance” and “assemblage” in conjunction with multiple subjectivities and Foucault's view of power as productive as well as his view of resistance as an “antagonism of strategies” in his late scholarship on resistance, ethics, and subjectivity. I offer examples of salient modes of what I call “productive” resistance, and from a Foucauldian perspective I explain how each mode targets and subverts technologies of repressive power to produce new elements of the digital environment and construct new truths. I conclude by recognizing the agency embodied in resistance as an end in itself, but I also consider that modes of productive resistance can have extrinsic value as they affect the fluid interaction among elements of the digital environment, potentially disrupting the presumed structure of dominance and dependence, and opening our conceptualization of algorithmic life to hopeful possibilities for change. (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)

This paper contributes to our understanding of farm data value chains with assistance from 54 semi-structured interviews and field notes from participant observations. Methodologically, it includes individuals, such as farmers, who hold well-known positionalities within digital agriculture spaces—platforms that include precision farming techniques, farm equipment built on machine learning architecture and algorithms, and robotics—while also including less visible elements and practices. The actors interviewed and materialities and performances observed thus came from spaces and places inhabited by, for example, farmers, crop (...) scientists, statisticians, programmers, and senior leadership in firms located in the U.S. and Canada. The stability of “the” artifacts followed for this project proved challenging, which led to me rethinking how to approach the subject conceptually. The paper is animated by a posthumanist commitment, drawing heavily from assemblage thinking and critical data scholarship coming out of Science and Technology Studies. The argument’s understanding of “chains” therefore lies on an alternative conceptual plane relative to most commodity chain scholarship. To speak of a data value chain is to foreground an orchestrating set of relations among humans, non-humans, products, spaces, places, and practices. The paper’s principle contribution involves interrogating lock-in tendencies at different “points” along the digital farm platform assemblage while pushing for a varied understanding of governance depending on the roles of the actors and actants involved. (shrink)

This contribution aims at proposing a framework for articulating different kinds of “normativities” that are and can be attributed to “algorithmic systems.” The technical normativity manifests itself through the lineage of technical objects. The norm expresses a technical scheme’s becoming as it mutates through, but also resists, inventions. The genealogy of neural networks shall provide a powerful illustration of this dynamic by engaging with their concrete functioning as well as their unsuspected potentialities. The socio-technical normativity accounts for the manners in (...) which engineers, as actors folded into socio-technical networks, willingly or unwittingly, infuse technical objects with values materialized in the system. Surveillance systems’ design will serve here to instantiate the ongoing mediation through which algorithmic systems are endowed with specific capacities. The behavioral normativity is the normative activity, in which both organic and mechanical behaviors are actively participating, undoing the identification of machines with “norm following,” and organisms with “norminstitution”. This proposition productively accounts for the singularity of machine learning algorithms, explored here through the case of recommender systems. The paper will provide substantial discussions of the notions of “normative” by cutting across history and philosophy of science, legal, and critical theory, as well as “algorithmics,” and by confronting our studies led in engineering laboratories with critical algorithm studies. (shrink)

The central goal of this paper is to investigate the injustices that can occur with the use of new technologies, especially Artificial Intelligence (AI), focusing on the issues concerning respect to human rights and the protection of victims and the most vulnerable. We aim to study the impacts of AI in daily life and the possible threats to human dignity imposed by it, such as discrimination based on prejudices, identity-oriented stereotypes, and unequal access to health services. We characterize such cases (...) as algorithmic injustices. In the final part of the text, we propose some strategies to confront this specific type of injustice. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on (...) the difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers (...) of information about the world. Since, on algorithmic information theory, a string is algorithmically random just if it is incompressible, the thesis entails that empirical data sets consist of algorithmically random strings of digits. Rather than constituting compressions of empirical data, scientific laws and theories pick out patterns that data sets exhibit with a certain noise. (shrink)

Drawing on a qualitative, multi-case study of three kinds of geographically tethered gig work—ride-hailing, delivery, and domestic services platforms—in the United States, I examine how workers anticipate the influences of metrics, live with metrics, and cope with algorithmic precarity. Data for this project include in-depth interviews with 50 gig workers about their efforts to interpret and manage metrics as part of their everyday work practices. The analysis reveals that participants were anxious about metrics primarily because of the disciplinary outcomes, that (...) are, the threat of job loss and the valued job features. It also directs attention to how workers felt and experienced customer-sourced ratings and system-generated behavioral metrics variously across platforms. Information asymmetries and the perceived lack of control also intensified a sense of powerlessness among participants. While participants articulated strategies that aimed at managing the uncertainty of customer-sourced ratings—and more precisely, the work-related uncertainty created by “difficult customers”—throughout service interactions, their feelings of anxiety could not be resolved. Furthermore, the (in)visibility of metrics, the settings of platform-mediated worker–customer interactions, and workers’ platform dependence contributed to the varying disciplinary power of metrics. The study contributes to understanding how metrics as affective measures mediate the trilateral relationship between platforms, workers, and customers in the gig economy. (shrink)

Building on critical approaches that understand algorithms in terms of communication, culture and organization, this paper offers the supplementary conceptualization of algorithms as organizational figuration, defined as material and meaningful sociotechnical arrangements that develop in spatiotemporal processes and are shaped by multiple enactments of affordance–agency relations. We develop this conceptualization through a case study of a Danish fintech start-up that uses machine learning to create opportunities for sustainable pensions investments. By way of ethnographic and literary methodology, we provide an in-depth (...) analysis of the dynamic trajectory in and through which the organization gives shape to and takes shape from its key algorithmic tool, mapping the shifting sociotechnical arrangements of the start-up, from its initial search for a viable business model through the development of the algorithm to the public launch of its product. On this basis, we argue that conceptualizing algorithms as organizational figuration enables us to detail not only what algorithms do but also what they are. (shrink)

Digital platforms and application software have changed how people work in a range of industries. Empirical studies of the gig economy have raised concerns about new systems of algorithmic management exercised over workers and how these alter the structural conditions of their work. Drawing on the republican literature, we offer a theoretical account of algorithmic domination and a framework for understanding how it can be applied to ride hail and food delivery services in the on-demand economy. We argue that (...) certain algorithms can facilitate new relationships of domination by sustaining a socio-technical system in which the owners and managers of a company dominate workers. This analysis has implications for the growing use of algorithms throughout the gig economy and broader labor market. (shrink)

This dissertation addresses a variety of foundational issues pertaining to the notion of algorithm employed in mathematics and computer science. In these settings, an algorithm is taken to be an effective mathematical procedure for solving a previously stated mathematical problem. Procedures of this sort comprise the notional subject matter of the subfield of computer science known as algorithmic analysis. In this context, algorithms are referred to via proper names of which computational properties are directly predicated )). Moreover, many (...) formal results are traditionally stated by explicitly quantifying over algorithms. These observations motivate the view that algorithms are themselves abstract mathematical objects -- a view I refer to as algorithmic realism. The status of this view is clearly related to that of Church's Thesis -- i.e. the claim that a function is computable by an algorithm just in case it is partial recursive. But whereas Church's Thesis is widely accepted on the basis of several well-known mathematical analyses of algorithmic computability, there is presently no consensus on how we can uniformly identify individual algorithms with mathematical objects. In the first chapter of this work, I attempt to illustrate the theoretical significance of algorithmic realism. I suggest that this view is not only of foundational significance to computer science, but also worth highlighting due to the role algorithms play in the fixation of mathematical knowledge and their relationship to intensional entities such as propositions and properties. Chapter Two presents a formal framework for reducing computational discourse to mathematical discourse modeled on contemporary discussion of mathematical nominalism. Chapter Three is centered on a case study intended to illustrate the technical exigencies involved with defending algorithmic realism. Chapter Four explores various ways in which algorithms might be identified with models of computation. And finally, Chapter Five argues that no such identification can uniformly satisfy all statements of algorithmic identity and non-identity affirmed by computational practice. I suggest that the technical crux of algorithmic realism lies in the necessity of reducing recursive specifications of algorithms to iterative models of computation in a manner which preserves algorithmic identity. (shrink)

This article proposes a new problem which is called the Stochastic Travelling Advisor Problem in network optimization, and it is defined for an advisory group who wants to choose a subset of candidate workplaces comprising the most profitable route within the time limit of day working hours. A nonlinear binary mathematical model is formulated and a real application case study in the occupational health and safety field is presented. The problem has a stochastic nature in travelling and advising times since (...) the deterministic models are not appropriate for such real-life problems. The STAP is handled by proposing suitable probability distributions for the time parameters and simulating the problem under such conditions. Many application problems like this one are formulated as nonlinear binary programming models which are hard to be solved using exact algorithms especially in large dimensions. A novel binary version of the recently developed gaining-sharing knowledge-based optimization algorithm to solve binary optimization problems is given. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. The binary version of GSK depends mainly on two stages that enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. The generated simulation runs of the example are solved using the BGSK, and the output histograms and the best-fitted distributions for the total profit and for the route length are obtained. (shrink)

In computer science, an algorithm is an unambiguous specification of how to solve a class of problems. Algorithms can perform calculation, data processing and automated reasoning tasks. As an effective method, an algorithm can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined (...) successive states, eventually producing "output" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input. This book introduces a set of concepts in solving problems computationally such as Growth of Functions; Backtracking; Divide and Conquer; Greedy Algorithms; Dynamic Programming; Elementary Graph Algorithms; Minimal Spanning Tree; Single-Source Shortest Paths; All Pairs Shortest Paths; Flow Networks; Polynomial Multiplication, to ways of solving NP-Complete Problems, supported with comprehensive, and detailed problems and solutions, making it an ideal resource to those studying computer science, computer engineering and information technology. (shrink)

Live algorithms are an ideal concept: computational systems able to collaborate proactively with humans in the creation of group-based improvised music. The challenge is to achieve equivalence between human and computer collaborators, both in formal terms and in practice. The fundamental question is the capacity for computational processes to exhibit “creativity.” The problems inherent in computer music performance are considered, in which computers are quasi-instruments or act in proxy for another musician. Theories from social psychology and pragmatics are explored to (...) help understand live music-making as a special case of social organization; namely, Kelley’s covariation model of Attribution Theory and Grice’s Maxims of Cooperation. This chapter outlines a description of how human beings and computers might engage on an equivalent basis and proposes how social psychology theories, rendered in formal language, can point to new horizons in human-computer performance practice. (shrink)

Platform companies use techniques of algorithmic management to control their users. Though digital marketplaces vary in their use of these techniques, few studies have asked why. This question is theoretically consequential. Economic sociology has traditionally focused on the embedded activities of market actors to explain competitive and valuation dynamics in markets. But restrictive platforms can leave little autonomy to market actors. Whether or not the analytical focus on their interactions makes sense thus depends on how restrictive the platform is, (...) turning the question into a first order analytical concern. The paper argues that we can explain why platforms adopt more and less restrictive architectures by focusing on the design logic that informs their construction. Platforms treat markets as search algorithms that blend software computation with human interactions. If the algorithm requires actors to follow narrow scripts of behavior, the platform should become more restrictive. This depends on the need for centralized computation, the degree to which required inputs can be standardized, and the misalignment of interests between users. The paper discusses how these criteria can be mobilized to explain the architectures of four illustrative cases. (shrink)