Human Prejudice Research Articles

Enhancing Dynagraph Card Classification in Pumping Systems Using Transfer Learning and the Swin Transformer Model

The dynagraph card plays a crucial role in evaluating oilfield pumping systems’ performance. Nevertheless, classifying dynagraph cards can be quite difficult because certain operating conditions may exhibit similar patterns. Conventional classification approaches mainly involve labor-intensive manual analysis of these cards, leading to subjectivity, prolonged processing times, and vulnerability to human prejudices. In response to this challenge, our study introduces a novel approach that leverages transfer learning and the Swin Transformer model for classifying dynagraph cards across various operating conditions in rod pumping systems. Initially, the Swin Transformer model undergoes pre-training using the ImageNet-22k dataset. Subsequently, we fine-tune the model’s weights using actual dynagraph card datasets, facilitating direct classification analysis with dynagraph cards as input variables. The adoption of transfer learning significantly reduces the training time while enhancing the accuracy of condition diagnosis. To assess the effectiveness of our proposed method, we conducted a comparative evaluation against conventional models like ResNet50, DenseNet121, LeNet, and ViT. The findings demonstrate that our approach outperforms other methods, achieving an accuracy of 96%, thereby improving classification accuracy by 3–4%. Therefore, our approach, based on transfer learning and the Swin Transformer model, provides a better solution for practical problems involving similar dynagraph cards. It meets the requirements of oil field operations, enhancing economic benefits and work efficiency.

Deep Learning for Fruit Grading: A State-of-the-Art Review

In the food industry, grading fruit quality is a critical responsibility. Throughout this process, fruits are sorted and categorized in by their quality. Fruit grading can be done using both machine learning and visual assessment. Visual inspection is subjective and can be influenced by human prejudice. Machine learning can produce more accurate and unbiased results. Deep learning-based methods can be used to evaluate the fruit quality by teaching a neural network to recognize various quality parameters like size, color, and defects. Deep learning methodologies for evaluating fruit quality offer further benefits. They are neutral and accurate, and they can manage enormous amounts of data. They can also save labor expenses and improve the efficiency of the grading process. Deep learning methods are useful for evaluating fruit quality, but they have several drawbacks. These include an intricate neural network, overfitting, and a lack of high-quality training data. Addressing these issues is crucial for the success of deep learning in fruit quality evaluation. In this paper, various significant deep-learning methods for evaluating fruit quality are described. The methods' advantages and disadvantages are also discussed. The study gives the researcher pointers on how to improve current strategies or create fresh ones to improve performance in terms of training effectiveness, accuracy, etc.

Apex printing company limited employees’ appraisal

Apex Printing Company Ltd. (APC) performed appraisal on their employee to strategically achieve their organizational goals through staff enhancement. Performance appraisal evaluates personal traits and development for organizational benefits. Questionnaire administration and personal interview were the methodology adopted in this research. APC is made up of 1800 staff, comprising 1450 junior employees, 350 members of management and senior staff. APC performance appraisal system is unproductive in evaluating accurate performance of its staff, because of human prejudice in assessment. From the research, we therefore conclude that performance appraisal in APC is affected by prejudices while performance appraisal machinery in APC is ineffective in evaluating employee performance. Management of APC is recommended to embark on reviewing of the total appraisal system of the company design to achieve the goals of performance appraisal of employees.

Gender equity in hiring: examining the effectiveness of a personality-based algorithm.

Gender biases in hiring decisions remain an issue in the workplace. Also, current gender balancing techniques are scientifically poorly supported and lead to undesirable results, sometimes even contributing to activating stereotypes. While hiring algorithms could bring a solution, they are still often regarded as tools amplifying human prejudices. In this sense, talent specialists tend to prefer recommendations from experts, while candidates question the fairness of such tools, in particular, due to a lack of information and control over the standardized assessment. However, there is evidence that building algorithms based on data that is gender-blind, like personality - which has been shown to be mostly similar between genders, and is also predictive of performance, could help in reducing gender biases in hiring. The goal of this study was, therefore, to test the adverse impact of a personality-based algorithm across a large array of occupations. The study analyzed 208 predictive models designed for 18 employers. These models were tested on a global sample of 273,293 potential candidates for each respective role. Mean weighted impact ratios of 0.91 (Female-Male) and 0.90 (Male-Female) were observed. We found similar results when analyzing impact ratios for 21 different job categories. Our results suggest that personality-based algorithms could help organizations screen candidates in the early stages of the selection process while mitigating the risks of gender discrimination.

Why Human Prejudice is so Persistent: A Predictive Coding Analysis

ABSTRACT Although the relationship between prejudice and predictive coding has attracted more attention recently, many important issues remain to be investigated, such as why prejudice is so persistent and how to accommodate seemingly conflicting studies. In this paper, we offer an integrated framework to explain the functional-computational mechanism of prejudice. We argue that this framework better explains (i) why prejudice is somewhat immune to revision, (ii) how inconsistent processing (e.g. one’s moral belief and biased emotional reaction) may occur, (iii) the dispute over the content of ‘alief’ (unconscious belief), and (iv) conflicting data reported in disinformation studies. Finally, we briefly review the main intervention strategies and offer recommendations.

Removing AI’s sentiment manipulation of personalized news delivery

Artificial intelligence (AI) is empowering personalized online news delivery to accommodate people’s information needs and combat information overload. However, AI models learned from user data are inheriting and amplifying some underlying human prejudice such as the sentiment bias of news reading, which may lead to potential negative societal effects and ethical concerns. Here, substantial evidence shows that AI is manipulating the sentiment orientation of news displayed to users by promoting the presence chance of negative news, even if there is no human interference. To mitigate this manipulation, a sentiment-debiasing method based on a decomposed adversarial learning framework is proposed, which can reduce 97.3% of sentiment bias with only 2.9% accuracy sacrifice. Our work provides the potential in improving AI’s responsibility in many human-centered applications such as online journalism and information spread.

Collaboration among recruiters and artificial intelligence: removing human prejudices in employment.

In the global war for talent, traditional recruiting methods are failing to cope with the talent competition, so employers need the right recruiting tools to fill open positions. First, we explore how talent acquisition has transitioned from digital 1.0 to 3.0 (AI-enabled) as the digital tool redesigns business. The technology of artificial intelligence has facilitated the daily work of recruiters and improved recruitment efficiency. Further, the study analyzes that AI plays an important role in each stage of recruitment, such as recruitment promotion, job search, application, screening, assessment, and coordination. Next, after interviewing with AI recruitment stakeholders (recruiters, managers, and applicants), the study discusses their acceptance criteria for each recruitment stage; stakeholders also raised concerns about AI recruitment. Finally, we suggest that managers need to be concerned about the cost of AI recruitment, legal privacy, recruitment bias, and the possibility of replacing recruiters. Overall, the study answers the following questions: (1) How artificial intelligence is used in various stages of the recruitment process. (2) Stakeholder (applicants, recruiters, managers) perceptions of AI application in recruitment. (3) Suggestions for managers to adopt AI in recruitment. In general, the discussion will contribute to the study of the use of AI in recruitment, as well as providing recommendations for implementing AI recruitment in practice.

Letter from the Editor

Letter from the Editor

AI: Coming of age?

AbstractAI has had many summers and winters. Proponents have overpromised, and there has been hype and disappointment. In recent years, however, we have watched with awe, surprise, and hope at the successes: Better than human capabilities of image-recognition; winning at Go; useful chatbots that seem to understand your needs; recommendation algorithms harvesting the wisdom of crowds. And with this success comes the spectre of danger. Machine behaviours that embed the worst of human prejudice and biases; techniques trying to exploit human weaknesses to skew elections or prompt self-harming behaviours. Are we seeing a perfect storm of social media, sensor technologies, new algorithms and edge computing? With this backdrop: is AI coming of age?

ІЛЮСТРАЦІЇ ЄВРОПЕЙСЬКИХ СВІТСЬКИХ ВИДАНЬ ЯК ЗАСІБ УСЕБІЧНОГО ПРИГНОБЛЕННЯ РОМІВ НА ПОЧАТКУ ХХ СТ.

Prejudice against the Roma is a social chasm that is already burying itself today. The Roma are the nationality about which there is a lot of misinformation, and even more so, against which an ideological policy of extermination has been deployed, based mostly on audacious and false human prejudices. Public opinion in general sees the representatives of the Gypsy population as negative, pre-programmed qualities, supported not by reality, but by racist prejudices and misinformation from the press. Roma people continue to be mostly victims of false accusations and have been labeled as criminals, liars and thieves for several centuries. The purpose of the work is to analyze the illustrations, the artistic features of which demonstrate a biased attitude towards the coverage of the heterogeneous gypsy life in Europe. Constant reviews and interest of the public in scandalous situations related to the Roma make it possible for the mass media to encourage the reader with increasingly fanciful and sometimes even absurd news with images of the Roma. The subject of the study is the most frequently repeated and widespread illustrative stereotypes that the Roma are a deviant marginal group of people who devote their lives only to crimes. The press and editorial artists at the beginning of the 20th century increasingly represented the gypsy community as a band of barbarians who, in the age of symbolism, were depicted in endless attempts to commit crimes. The scientific problem is contemporary artistic carelessness and stereotypical thinking, which can provoke and encourage brazen prejudice against Roma. The persecution of Gypsies in the press was presented somewhat ironically, with regular mockery, which increased the demand and caused even possible negative consequences for one or another publication on the part of the Roma people, who always kept aloof, and who for decades fell into the cycle of brutal, executed by the society of the time in the Art Nouveau style of illustrations in the popular European press.

No-boundary thinking: a viable solution to ethical data-driven AI in precision medicine

Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.

Theoretical Foundations and Methodological Problems of the Semiotics of History

The article deals with the theoretical and methodological problems of the semiotics of history. Semiotics is the science that studies the sign system. This system is used by people to transfer knowledge and information. Modern governance is semiotic. A separate fact can turn into a sign-symbol. The sign lives its own life in the virtual space, leaving the information space. The modern world is governed by language. The revolution starts with the language. Semiotics changes the epistemological regime. The code provides a communication system. Any code requires decoding. Cultural space codes are ambiguous. The semiotics of history actively interacts with the history of ideologies. The question of the truth of the nationalism doctrine cannot be solved by science, because it belongs to ideology. The name is the essence. In psychiatry, there is the concept of semiotic dementia, when the patient does not understand the meaning of words. Information is distributed among people, therefore it acquires its social significance when it is perceived by people, only under this condition it becomes knowledge. Knowledge helps to act. A prerequisite for this is correct information. Concepts of the information society can be distinguished in the format of semiotics of history. Images have a stronger effect on people than texts. Negative information spreads faster than texts. Virtuality creates the image of the desired world. The images of the future are not neutral. A certain image of the future is set by the present. No one can escape the human prejudice. The semantics of history develops in the general line of the linguistic turn in historical science. In this sense, the methodological and theoretical problems of the semiotics of history are associated with the linguistic problems of consciousness, caused by the imperfection of any language and the ways of its public use

Women’s scarcity in academic governance. Gendered identity or gendered processes?

This article contributes to the empirical evidence for women’s scarcity in academic governance. The study evaluates to what extend women lean towards non-management careers and dismiss opportunities to attain executive roles in Colombian public universities, as well as the support received when they break the paradigm. The purpose was to determine whether gendered practices are ingrained in the designation process or whether women’s scarcity is the outcome of individual attributes/choices and collective perceptions of inadequacy. Data was collected from universities’ proceedings, opinion polls of rectors’ designations, and candidates’ curricula. Findings show low female candidacy rate but high public support for female candidates to the rector’s seat among all universities examined. Also, curricula’s in-depth analyses display women’s preference for male-dominated careers and analogous academic/administrative experience to that of male candidates. Hence, the results challenge explanations presented by human capital and congruity prejudice theories, while leaning towards gendered processes and identities.

Racial prejudice and the modern traditional family: the psychology of institutionalized racism

The examination of racism in sociology involves a study of the social development of ‘racial’ groups and racial inequalities. Described as the racial group supremacy ideology that explains or demands a racial dominance or exploitation framework, race retains the attitudes and activities of individuals and the institutions in which they are established. Racism is often referred to as human prejudice, yet racism is certainly structural, expressing itself in the benefits and drawbacks of cultural objects, ideological discourse and institutional realities that co-exist with human prejudice. In this study we seek for instances of historical beliefs and cultural conduct that contribute to the racial disparities of today. The researchers are producing three important findings on racial psychology obtained via the application of a psychological and cultural paradigm. To start with, racism may be seen in our daily environment. Secondly, via their choices and actions, researchers strengthen racist settings in their daily activities. Thirdly, they interact with cultural settings that encourage prejudiced approaches to the world. This approach distances treatment efforts from individual choices and changes the mental and environmental conditions which reflect and maintain racial superiority. Researchers may investigate the return of overt racism in future studies and also critically examine subtle types of racism, which would be a profound issue in society.

Computational phonogram archiving

A general approach to analyzing audio files makes it possible to re-create the sound of ancient instruments, identify cross-cultural musical properties, and more.

O tema sexualidade humana nos livros didáticos de Biologia mais distribuídos pelo Programa Nacional do Livro Didático 2015

The restriction of the approach to the issue of sexuality in schools and in the textbook may be problematic, since by not debating on contents related to human sexuality, important doubts about health in relation to the sexual sphere may not be clarified. By not debating about the social issues of human sexuality, prejudices and discriminations against homosexuals can be generated. The objective of this study was to investigate how the two collections of biology textbooks most distributed in Brazil in 2015, by the PNLD, address the theme, in addition to checking the collections of the same authors in 2018. Content analysis was used to analyze the four collections. It was verified that the language present in the textbooks still presents a heteronormative pattern, not addressing the diversity of human sexuality, focusing mainly on sexuality as a synonym and form of reproduction. Thus, the discussion of sexuality issues within the classroom should cover more topics within the content, in order to deepen the discussion and then be able to meet the criteria for building a democratic society. It is hoped that this study will contribute to the demystification of the taboos that involve human sexuality; as well as help in the elaboration of textbooks with a more social view of the sexuality theme, with the intention of deconstructing gender prejudices and sexual orientation.

British terrorism preemption: Subjectivity and disjuncture in Channel "de-radicalization" interventions.

This article examines Channel "de-radicalization" interventions, which take place on individuals suspected of having the potential to commit terrorist crimes. Situated within critical security studies, the article explores the British Prevent programme by utilizing primary interviews with hard-to-reach Channel mentors and senior Prevent officials. Following the work of anticipatory risk-governance scholarship, this research illuminates the three processes of risk-visibilization (how an individual becomes sufficiently "seen" as harbouring risk that they are offered Channel mentorship), risk-calculation (how practitioners negotiate supposed riskiness), and risk-knowing (how practitioners "know" risks they observe). It demonstrates how the practice of preemptive counter-terrorism is subsumed inherently by-even relies upon-subjectivity and human prejudice, and fundamental disagreements between practitioners. Through substantial empirical contribution on the phenomenon of Channel interventions, the discussion highlights ultimately that the algorithmic rationale of preemptive risk-spotting normalizes the suspicion of banal and everyday behaviors, precisely because such interventions are ultimately deployed through worst-case imaginations.

Border Spaces: Visualizing the U.S.-Mexico Frontera ed. by Katherine G. Morrisey and John-Michael Warner

Reviewed by: Border Spaces: Visualizing the U.S.-Mexico Frontera ed. by Katherine G. Morrisey and John-Michael Warner Henry Way Border Spaces: Visualizing the U.S.-Mexico Frontera. Katherine G. Morrisey and John-Michael Warner, eds. Tucson: University of Arizona Press, 2018. Pp. viii+239, photographs, notes. $35.00, paperback, ISBN 978-0-8165-3946-8. When the editors of this engaging collection of essays on the US-Mexico borderlands talk in their introduction of "interdisciplinary conversation," the phrase captures well the scholarship and the spirit of this book. This is a highly accessible compilation of interpretive work on borderland (especially historical) visualization, capturing a range of disciplinary approaches from a variety of authors. It also offers a truly inter-disciplinary model, with a thoughtful introduction and excellent introductory section essays, transcribing a meaningful dialogue between two scholars. This edited book is a model of how interdisciplinary scholarship can be done, and—given the subject matter—how relevant and appropriate that approach can be. As geographers we should explore works such as these that push our natural inclination for open-minded engagement toward further-reaching scholarly terrains such as the more avowedly artistic (as the second half of this book does). Historical geographers will find the subject of this collection—the ways the Mexico/US frontier has been visualized in history and the [End Page 167] present—interesting and important. The book is divided into two sections. The first considers the broader histories of the built environment in these borderlands, with an excellent dialogue of an introduction ("A Conversation on Border Landscapes through Time") and three focused chapters. The second presents some of the art histories—including examinations of art installations or performance art—of the borderland. In both cases this is a thought-provoking reminder of the symbolic, creative, emotional power of the border, beyond its more traditional (and, as the authors rightly point out, remote) political or economic conception. The border—including, and beyond, the "wall"—is a material phenomenon in a landscape. This book vividly directs our attention to the ways that materiality has been visualized and interpreted. While the second section opens geographers' eyes to the artistic and creative interrogations of this frontier in interesting ways (with essays on the border wall as canvas, on a film project, and a GPS-powered "transborder immigrant tool," for instance), the first section contains work of particular historical value. A couple of chapters point to how worthwhile it can be to take an explicitly "visual" approach to historical geography. A discussion by Mary Mendoza ("Fencing the Line: Race, Environment, and the Changing Visual Landscape at the U.S.-Mexico Divide") connects the fences and fortifications constructed in the early years of the twentieth century aimed at checking the spread of cattle ticks with the demarcation and construction of racial difference. "The story of tick eradication at the U.S.-Mexico border," she writes, "reveals how nature, at its most fundamental level, can be seen at the center of racial discourse" generating, and supported by, the "construction of a racialized and imposing border control apparatus" (68). This fusion of nature, landscape, and human prejudice around the material built practices at this moment in border history is brilliantly described and effectively illustrated. Katherine Morrissey's essay, "Monuments, Photographs, and Maps: Visualizing the U.S.-Mexico Border in the 1890s," in the same section will also spark geographers' interest. In a fascinating account she shows the use of photography in the 1890s to record the establishment of border "monuments"—obelisks built to (hopefully) fix the (hopefully) accurate location of the line, at various points. This work is an effective discussion of the International Boundary Commission and the efforts at demarcation in that period. It illustrates another way in which the border [End Page 168] is actually created and made physical. It also illustrates the assumed power of the "visual," both in terms of the monuments themselves and the official use of photographic records to check and support border location claims. But Morrissey takes the analysis further and compares images taken by the Mexican section photographers with those by the American photographers, giving a thoughtful account of the differences of perspective—a...

Constructs and Roles of Culture in National and Other Social Formations and Imperial Soft-Power Projections

ABSTRACTThis paper discusses the imperative for Marxist Theory ←→ Praxis to develop and respond to/counter ongoing even urgent imperial efforts to develop scientific approaches to the study of the nature and evolution of human consciousness, cognition, learning, perception, susceptibility to soft and hard forms of power, propaganda and imperial social systems engineering. Specifically how do shifting socio-historical-economic-political definitions, constructs and realities of culture, tribe, nation, ethnicity, race, nationality, interpenetrate with and shape human cognition, perception, prejudices, and class consciousness as central to but not all of overall consciousness? How are these constructs used in concrete ways to foster imperial penetrations, co-optations, control and subversion? What are some emerging issues and areas in need of research as well as concrete ways that constructs like culture can be and are being used to promote false consciousness, reactionary tribalism and forms of “patriotism,” exceptionalism, triumphalism and neo-Cold War posturing. What are the implications of sub-cultures within alleged broader homogenous national cultures, imperial promotion of forms of supposed exceptionalism and triumphalism trumping class-based cultures and their inherent antagonisms vs. socialist social formations intent on reducing class-based cultures and their antagonisms with no imperatives or intent for domination and hegemony?

Meaningful Explanations of Black Box AI Decision Systems

Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box.