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Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias in generative AI, drawing on Plato's Allegory of the Cave to frame the issue as a problem of limited and distorted representation. We propose a multifaceted approach combining technical interventions, such as data diversification and culturally aware model constraints, with a deeper engagement with the cultural and philosophical dimensions of the problem. Drawing on theories of extended cognition and situated knowledge, we argue that mitigating AI biases requires a reflexive interrogation of the cultural contexts of AI development and a commitment to empowering marginalized voices and perspectives. We claim that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance. By bridging philosophical reflection with technical innovation, this paper contributes to the growing discourse on responsible and inclusive AI, offering a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has become increasingly prevalent in various domains. However, ML algorithms sometimes give unfair outcomes and discrimination against certain groups. Thereby, bias occurs when our results produce a decision that is systematically incorrect. At various phases of the ML pipeline, such as data collection, pre-processing, model selection, and evaluation, these biases appear. Bias reduction methods for ML have been suggested using a variety of techniques. By changing the data or the model itself, adding more fairness constraints, or both, these methods try to lessen bias. The best technique relies on the particular context and application because each technique has advantages and disadvantages. Therefore, in this paper, we present a comprehensive survey of bias mitigation techniques in machine learning (ML) with a focus on in-depth exploration of methods, including adversarial training. We examine the diverse types of bias that can afflict ML systems, elucidate current research trends, and address future challenges. Our discussion encompasses a detailed analysis of pre-processing, in-processing, and post-processing methods, including their respective pros and cons. Moreover, we go beyond qualitative assessments by quantifying the strategies for bias reduction and providing empirical evidence and performance metrics. This paper serves as an invaluable resource for researchers, practitioners, and policymakers seeking to navigate the intricate landscape of bias in ML, offering both a profound understanding of the issue and actionable insights for responsible and effective bias mitigation. [ABSTRACT FROM AUTHOR]

One can argue that one of the main roles of the subject of statistics is to characterize what the evidence in the collected data says about questions of scientific interest. There are two broad questions that we will refer to as the estimation question and the hypothesis assessment question. For estimation, the evidence in the data should determine a particular value of an object of interest together with a measure of the accuracy of the estimate, while for the hypothesis assessment, the evidence in the data should provide evidence in favor of or against some hypothesized value of the object of interest together with a measure of the strength of the evidence. This will be referred to as the evidential approach to statistical reasoning, which can be contrasted with the behavioristic or decision-theoretic approach where the notion of loss is introduced, and the goal is to minimize expected losses. While the two approaches often lead to similar outcomes, this is not always the case, and it is commonly argued that the evidential approach is more suited to scientific applications. This paper traces the history of the evidential approach and summarizes current developments. [ABSTRACT FROM AUTHOR]

The increasing use of artificial intelligence (AI) in education has raised questions about the implications of ChatGPT for teaching and learning. A systematic literature review was conducted to answer these questions, analyzing 112 scholarly articles to identify the potential benefits and challenges related to ChatGPT use in educational settings. The selection process was thorough to ensure a comprehensive analysis of the current academic discourse on AI tools in education. Our research sheds light on the significant impact of ChatGPT on improving student engagement and accessibility and the critical issues that need to be considered, including concerns about the quality and bias of generated responses, the risk of plagiarism, and the authenticity of educational content. The study aims to summarize the utilizations of ChatGPT in teaching and learning by addressing the identified benefits and challenges through targeted strategies. The authors outlined some recommendations that will ensure that the integration of ChatGPT into educational frameworks enhances learning outcomes while safeguarding academic standards. [ABSTRACT FROM AUTHOR]

The burgeoning landscape of scientific communication, marked by an explosive surge in published articles, journals, and specialized publishers, prompts a critical examination of prevailing assumptions. This article advocates a dispassionate and meticulous analysis to avoid policy decisions grounded in anecdotal evidence or superficial arguments. The discourse surrounding so-called predatory journals has been a focal point within the academic community, with concerns ranging from alleged lack of peer review rigor to exorbitant publication fees. While the consensus often leans towards avoiding such journals, this article challenges the prevailing narrative. It calls for a more nuanced understanding of what constitutes predatory practices and underscores the importance of skeptical inquiry within our daily academic activities. The authors aim to dispel misconceptions and foster a more informed dialogue by scrutinizing APCs, impact factors, and retractions. Furthermore, the authors delve into the evolving landscape of scientific publishing, addressing the generational shifts and emerging trends that challenge traditional notions of prestige and impact. In conclusion, this article serves as a call to action for the scientific community to engage in a comprehensive and nuanced debate on the complex issues surrounding scientific publishing. [ABSTRACT FROM AUTHOR]

News recommender systems (NRS) are crucial for helping users navigate the vast amount of content available online. However, traditional NRS often suffer from biases that lead to a narrow and unfair distribution of exposure across news items. In this paper, we propose a novel approach, the Contextual-Dual Bias Reduction Recommendation System (C-DBRRS), which leverages Long Short-Term Memory (LSTM) networks optimized with a multi-objective function to balance accuracy and diversity. We conducted experiments on two real-world news recommendation datasets and the results indicate that our approach outperforms the baseline methods, and achieves higher accuracy while promoting a fair and balanced distribution of recommendations. This work contributes to the development of a fair and responsible recommendation system. [ABSTRACT FROM AUTHOR]

The rapidly evolving field of machine learning (ML), along with artificial intelligence in a broad sense, is revolutionising many areas of healthcare, including laboratory medicine. The amalgamation of the fields of ML and patient-based real-time quality control (PBRTQC) processes could improve the traditional PBRTQC and error detection algorithms in the laboratory. This narrative review discusses published studies on using ML for the detection of systematic errors, non-systematic errors, and combinations of different types of errors in clinical laboratories. The studies discussed used ML for detecting bias, the requirement for re-calibration, samples contaminated with intravenous fluid or EDTA, delayed sample analysis, wrong-blood-in-tube errors, interference or a combination of different types of errors, by comparing the performance of ML models with human validators or traditional PBRTQC algorithms. Advantages, limitations, the creation of standardised ML models, ethical and regulatory aspects and potential future developments have also been discussed in brief. [ABSTRACT FROM AUTHOR]

Today, the significance of biometrics is more pronounced than ever in accurately allowing access to valuable resources, from personal devices to highly sensitive buildings, as well as classified information. Researchers are pushing forward toward devising robust biometric systems with higher accuracy, fewer false positives and false negatives, and better performance. On the other hand, machine learning (ML) has been shown to play a key role in improving such systems. By constantly learning and adapting to users' changing biometric patterns, ML algorithms can improve accuracy and performance over time. The integration of ML algorithms with biometrics, however, introduces vulnerabilities in such systems. This article investigates the new issues of concern that come about because of the adoption of ML methods in biometric systems. Specifically, techniques to breach biometric systems, namely, data poisoning, model inversion, bias injection, and deepfakes, are discussed. Here, the methodology consisted of conducting a detailed review of the literature in which ML techniques have been adopted in biometrics. In this study, we included all works that have successfully applied ML and reported favorable results after this adoption. These articles not only reported improved numerical results but also provided sound technical justification for this improvement. There were many isolated, unsupported, and unjustified works about the major advantages of ML techniques in improving security, which were excluded from this review. Though briefly mentioned, we did not touch upon encryption/decryption aspects, and, accordingly, cybersecurity was excluded from this study. At the end, recommendations are made to build stronger and more secure systems that benefit from ML adoption while closing the door to adversarial attacks. [ABSTRACT FROM AUTHOR]

The bias of the maximum likelihood estimator can cause a considerable estimation error if the sample size is small. To reduce the bias of the maximum likelihood estimator under the small sample situation, the maximum likelihood and parametric bootstrap bias-correction methods are proposed in this study to obtain more reliable maximum likelihood estimators of the unit exponential distribution parameters. The procedure to implement the bias-corrected maximum likelihood estimation method is derived analytically, and the steps to obtain the bias-corrected bootstrap estimators are presented. The simulation results show that the proposed maximum likelihood bootstrap bias-correction method can significantly reduce the bias and mean squared error of the maximum likelihood estimators for most of the parameter combinations in the simulation study. A soil moisture data set and a numerical example are used for illustration. [ABSTRACT FROM AUTHOR]

A decision maker (DM) must choose between two projects or decide on no project. The expected benefits of these projects are correlated. The DM seeks advice from an agent with private information about the projects' benefits. However, the agent's divergent preferences for projects and lack of consideration for the DM's implementation costs may introduce two types of biases: project bias, favoring the agent's project, or pandering bias, favoring the project preferred by the DM. Our findings reveal that project correlation leads to these biases countervailing each other, facilitating the transmission of information. The agent typically recommends a project based on private information to dissuade the DM from choosing no project, as this would be detrimental to the agent. Additionally, we explore optimal delegation within organizations. In contrast to the prevailing literature advocating for delegation to biased agents for enhanced information elicitation, our study suggests limited benefits in the context of project correlation. [ABSTRACT FROM AUTHOR]

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward. [ABSTRACT FROM AUTHOR]

The question that the authors of this article are collectively concerned with is as follows: how is it possible to protect children without disempowering them? To this end, the authors work to change adultcentric scholarly and social norms that justify rationales that marginalize children. The article begins with a theoretical overview of childism, in its transformative sense, with special attention to how childism relates to intersectional analyses. In doing so, age is highlighted as an axis of marginalization with reference to adultcentrism. After that, the centrality of analyzing and problematizing adultism in educational research and practice is discussed. The discussion is followed by a presentation of the published results of 'The Adultcentrism Scale' research tool developed at the University of Bergamo and the University of LUMSA-Rome. The research tool is used to evaluate the presence of adultcentric bias in adults in relation to children and can be helpful to understanding the psychological dimensions of educational relationships. Finally, the conclusion offers suggestions for how the research tool might be a useful example to raise awareness of adultcentric bias, promoting reflections that can lead to age-inclusive transformations. Overall, then, the article initiates a pertinent dialogue for advancing children's participation rights in Nordic research and society. [ABSTRACT FROM AUTHOR]

Assessing the usefulness of reviews has been the aim of several research studies. However, results regarding the significance of usefulness determinants are often contradictory, thus decreasing the accuracy of reviews' helpfulness estimation. Also, bias in user reviews attributed to differences, e.g., in gender, nationality, etc., may result in misleading judgments, thus diminishing reviews' usefulness. Research is needed for sentiment analysis algorithms that incorporate bias embedded in reviews, thus improving their usefulness, readability, credibility, etc. This study utilizes fuzzy relations and fuzzy synthetic evaluation (FSE) in order to calculate reviews' usefulness by incorporating users' biases as expressed in terms of reviews' articulacy and sentiment polarity. It selected and analyzed 95,678 hotel user reviews from Tripadvisor, written by users from five specific nationalities. The findings indicate that there are differences among nationalities in terms of the articulacy and sentiment of their reviews. The British are most consistent in their judgments expressed in titles and the main body of reviews. For the British and the Greeks, review titles suffice to convey any negative sentiments. The Dutch use fewer words in their reviews than the other nationalities. This study suggests that fuzzy logic captures subjectivity which is often found in reviews, and it can be used to quantify users' behavioral differences, calculate reviews' usefulness, and provide the means for developing more accurate voting systems. [ABSTRACT FROM AUTHOR]

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect the representation of individuals in synthetic data. This survey study offers a succinct, comprehensive overview of fairness and bias in AI, addressing their sources, impacts, and mitigation strategies. We review sources of bias, such as data, algorithm, and human decision biases—highlighting the emergent issue of generative AI bias, where models may reproduce and amplify societal stereotypes. We assess the societal impact of biased AI systems, focusing on perpetuating inequalities and reinforcing harmful stereotypes, especially as generative AI becomes more prevalent in creating content that influences public perception. We explore various proposed mitigation strategies, discuss the ethical considerations of their implementation, and emphasize the need for interdisciplinary collaboration to ensure effectiveness. Through a systematic literature review spanning multiple academic disciplines, we present definitions of AI bias and its different types, including a detailed look at generative AI bias. We discuss the negative impacts of AI bias on individuals and society and provide an overview of current approaches to mitigate AI bias, including data pre-processing, model selection, and post-processing. We emphasize the unique challenges presented by generative AI models and the importance of strategies specifically tailored to address these. Addressing bias in AI requires a holistic approach involving diverse and representative datasets, enhanced transparency and accountability in AI systems, and the exploration of alternative AI paradigms that prioritize fairness and ethical considerations. This survey contributes to the ongoing discussion on developing fair and unbiased AI systems by providing an overview of the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the emerging field of generative AI. [ABSTRACT FROM AUTHOR]

Given that some suspected perpetrators were wrongly convicted, a defective bitemark analysis is comparable to dentists' most crucial clinical decisions regarding assessment. Bias affects human bitemark analysis beyond the limitation of the evidence itself. The aim of this study was to explore the potential for different types of bias in bitemark analysis and the methods involved in that analysis by conducting a scoping review. Results showed that the 14 articles that explore the topic of bias in bitemark analysis were published from 2006-2022. Publications were from the USA mainly (n = 7), followed by the UK (n = 3), Australia (n = 2), New Zealand, (n = 1) and the Netherlands (n = 1). Of these publications, 36% addressed contextual bias, while 57% acknowledged cognitive bias. According to the findings, preventive measures consist of limiting the availability of unrelated data during research, employing several comparison samples for a more impartial assessment, and repeating the analysis while being blind to past findings. Nevertheless, the physical limitations of the evidence such as distortions are still strongly present. [ABSTRACT FROM AUTHOR]

In recent years, deep learning models have led to improved accuracy in industrial defect detection, often using variants of YOLO (You Only Look Once), due to its high performance at a low cost. However, the generalizability, fairness and bias of their outcomes have not been examined, which may lead to overconfident predictions. Additionally, the complexity added by co-occurring defects, single and multi-class defects, and the effect on training, is not taken into consideration. This study addresses these critical gaps by introducing new methodologies for analyzing dataset complexity and evaluating model fairness. It introduces the novel approach of co-occurrence impact analysis, examining how the co-occurrence of defects in sample images affects performance, and introducing new dimensions to dataset preparation and training. Its aim is to increase model robustness in the face of real-world scenarios where multiple defects often appear together. Our study also innovates in the evaluation of model fairness by adapting the disparate impact ratio (DIR) to consider the true positive rate (TPR) across different groups and modifying the predictive parity difference (PPD) metric to focus on biases present in industrial quality control. Experiments demonstrate by cross-validation that the model trained on combined datasets significantly outperforms others in accuracy without overfitting and results in increased fairness, as validated by our novel fairness metrics. Explainability also provides valuable insights on the effects of different training regimes, notably absent in prior works. This work not only advances the field of deep learning for defect detection but also provides a strategic framework for future advancements, emphasizing the need for balanced datasets and considerations of ethics, fairness, bias and generalizability in the deployment of artificial intelligence in industry. [ABSTRACT FROM AUTHOR]

The complexity of migration flows across the world has led to a redefinition of psychological and social services users. The access of migrants from different cultural backgrounds to clinical services or social health services has diversified the demand for concomitant help. Biases and misinterpretations have been created by unaccustomed professionals in this field, which could lead to serious consequences and invalidate diagnostic and treatment procedures. The purpose of this study is to summarize the evidence about errors or prejudices observed in clinical practices regarding the provision of social health services to people from different cultural backgrounds. Results show three main types of biases: racial stereotype activation, ethnocentrism and micro-aggressions. Some implications on the clinical setting were discussed, as being aware of these biases can help mental health professionals manage communication more consciously with users. [ABSTRACT FROM AUTHOR]

Understanding the public's diverse linguistic expressions about rainfall and flood provides a basis for flood disaster studies and enhances linguistic and cultural awareness. However, existing research tends to overlook linguistic complexity, potentially leading to bias. In this study, we introduce a novel algorithm capturing rainfall and flood-related expressions, considering the relationship between precipitation observations and linguistics expressions. Analyzing 210 million social media microblogs from 2017, we identified 594 keywords, 20 times more than usual manually created bag-of-words. Utilizing Large Language Model, we categorized these keywords into rainfall, flood, and other related terms. Semantic features of these keywords were analyzed from the viewpoint of popularity, credibility, time delay, and part-of-speech, finding rainfall-related terms most common-used, flood-related keywords often more time delayed than precipitation, and notable differences in part-of-speech across categories. We also assessed spatial characteristics from keyword and city-centric perspectives, revealing that 49.5% of the keywords have significant spatial correlation with differing median centers, reflecting regional variations. Large and disaster-impacted cities show the richest expression diversity for rainfall and flood-related terms. [ABSTRACT FROM AUTHOR]