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The importance of pursuing financial inclusion to accelerate economic growth and enhance financial sustainability has been well noted. However, studies have provided few actionable insights into how financial institutions can balance the potential socioeconomic trade-off between profitability and equality. One major challenge arises from a lack of understanding of the impacts of various types of market information available on financial equality beyond economic profitability. Another challenge lies in how the socioeconomic trade-off under a large set of counterfactual policies in a real-world setting can be evaluated. Our motivation for the present study was the emerging sources of digitized user-behavior data (i.e., "alternative data") stemming from the high penetration of mobile devices and internet access. Accordingly, we investigated how alternative data from smartphones and social media can help mitigate potential financial inequality while preserving business profitability in the context of financial credit risk assessment. We partnered with a leading microloan website to design a novel "meta" experiment that allowed us to simulate various real-world field experiments under an exhaustive set of counterfactual policies. Interestingly, we found that profiling user financial risk using smartphone activities is 1.3 times more effective in improving financial inclusion than using online social media information (23.05% better vs. 18.11%), and nearly 1.3 times more effective in improving business profitability (42% better vs. 33%). Surprisingly, we found that using consumers' online shopping activities for credit risk profiling can hurt financial inclusion. Furthermore, we investigated potential explanations for financial inclusion improvements. Our findings suggest that alternative data, especially users' smartphone activities, not only demonstrate higher ubiquity but also appear to be more orthogonal to conventional sensitive demographic attributes. This, in turn, can help mitigate statistical bias driven by the unobserved factors or underrepresentative training samples in machine-based risk assessment processes. [ABSTRACT FROM AUTHOR]

Contributory science—including citizen and community science—allows scientists to leverage participant-generated data while providing an opportunity for engaging with local community members. Data yielded by participant-generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts. The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases. We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation, detectability, sampling and preference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data. Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog.

Despite enthusiasm for big data in the life sciences, challenges arise because of biases and incomplete data. Demographic studies often overlook dormant life stages, which can skew inferences. They also tend to focus on few populations and short time spans. We assessed omissions of seed banks in demographic studies, exploring trends across life forms, climates, and taxonomic groups. We compared 172 species (192 cases) with independent seed bank and demographic studies. Approximately 25% of the demographic studies excluded known seed bank stages. The probability of omissions was lower for annuals and shrubs and higher for perennial herbs. We found no evidence that ecoregion or phylogeny explained these omissions. Modeling choices and study designs may explain patterns of seed bank omissions. Considering more populations reduced the chance of omissions. Omissions raise concerns for ecological analyses using databases. Leveraging large data is important, but we must be careful to understand their biases and limitations. [ABSTRACT FROM AUTHOR]

More and more machine learning is applied to human behavior. Increasingly these algorithms suffer from a hidden—but serious—problem. It arises because they often predict one thing while hoping for another. Take a recommender system: It predicts clicks but hopes to identify preferences. Or take an algorithm that automates a radiologist: It predicts in-the-moment diagnoses while hoping to identify their reflective judgments. Psychology shows us the gaps between the objectives of such prediction tasks and the goals we hope to achieve: People can click mindlessly; experts can get tired and make systematic errors. We argue such situations are ubiquitous and call them "inversion problems": The real goal requires understanding a mental state that is not directly measured in behavioral data but must instead be inverted from the behavior. Identifying and solving these problems require new tools that draw on both behavioral and computational science. [ABSTRACT FROM AUTHOR]

The healthcare industry is undergoing a profound transformation with the integration of artificial intelligence (AI) into various healthcare settings. However, the successful implementation of AI in healthcare is challenged by several factors, particularly the lack of transparency and explainability in current AI systems. These limitations hamper their effectiveness and reduce their adoption in the field. The key issues that require attention encompass accountability, biases, erroneous results, lack of justification of medical decisions, and the erosion of trust. This paper addresses the challenges raised by non-transparent and non-explainable AI and analyses whether these features jeopardise the responsible integration of AI into healthcare.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets. Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied. The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations. This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

With the rise of machine learning and "big data," many large yet spurious relationships between variables are discovered, leveraged by marketing communications, and publicized in the media. Thus, consumers are increasingly exposed to many large-magnitude relationships between variables that do not signal causal effects. This exposure may carry a substantial cost. Seven studies demonstrate that the magnitudes of relationships between variables can distort consumers' judgments about whether those relationships reflect causal effects. Specifically, consumers often use a magnitude heuristic : consumers infer that relationships with larger perceived magnitudes are more likely to reflect causal effects, even when this is not true (and even when relationships' correlations are held constant). In many situations, relying on the magnitude heuristic will distort causality judgments, such as when large-magnitude relationships between variables are spurious, or when normatively extraneous factors (e.g. reference points) distort perceptions of magnitudes. Moreover, magnitude-distorted (mis)perceptions of causality, in turn, distort consumers' purchase and consumption decisions. Since consumers often encounter spurious relationships with large magnitudes in the health domain and in other consequential domains, the magnitude heuristic is likely to lead to biases in some of consumers' most important decisions. [ABSTRACT FROM AUTHOR]

Serial dependence has shown seemingly contradictory effects on visual perception and working memory. While serial dependence promotes perpetual and mnemonic stability, it biases behavioral reports toward prior information. The neural mechanisms that drive both biasing and adaptive stabilizing effects are not well understood. We proposed and tested a reactivation and integration mechanism that can account for these contradictory effects. We used multivariate pattern analyses of EEG data (26 human participants, 17 females, 9 males) to examine the reactivation of prior reported orientation during the delay period of a visual working memory task. The reactivation strength of prior reports, but not prior sensory items, was predictive of the magnitude of serial dependency biases. These reactivated representations integrated with the representation of the current memory item and improved the ability to decode the current contents of memory. Overall, our data provide convergent evidence suggesting that prior reports in a visual working memory task are reactivated on the subsequent trial and become integrated with current memory representations. This similarity-dependent reactivation mechanism drives both report biasing and stabilization effects attributed to serial dependence in working memory. [ABSTRACT FROM AUTHOR]

Ecologists often leverage contributory science, also referred to as citizen science, to answer large-scale spatial and temporal biodiversity questions. Contributory science platforms, such as eBird and iNaturalist, provide researchers with incredibly fine-scale data to track biodiversity. However, data generated by these platforms are spatially biased. Research has shown that factors like income, race, and historical redlining can influence spatial patterns of reported eBird and iNaturalist data. However, the role of contemporary residential segregation remains unclear. Additionally, we do not understand how these variables potentially relate to certain Census tracts having more or less biodiversity data than you would expect based on size or population density. To further understand the social factors that may contribute to spatial biases in eBird and iNaturalist data, we focused on three cities within the USA (Oakland, California; St. Louis, Missouri; and Baltimore, Maryland). We specifically investigated how income, race, segregation, and redlining via Home Owners' Loan Corporation grades (grades A = best, B, C, and D = hazardous and "redlined") are associated with the difference between reported and expected observations based on area and human population density. We find that census tracts with higher income and more White people generally have more observations than expected. We only find segregation to influence differences in reported and expected observations in Baltimore, with more segregated Census tracts having more observations than expected. Lastly, we find that grades C and D consistently have fewer data than expected compared with grades A and B for both platforms in each city. Our results show that although each city has distinct societal and ecological features, societal inequity permeates each city to shape the uptake of data for two of the largest sources of biodiversity data. [ABSTRACT FROM AUTHOR]

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities. [ABSTRACT FROM AUTHOR]

Facial recognition is an artificial intelligence-based technology that, like many other forms of artificial intelligence, suffers from an accuracy deficit. This paper focuses on one particular use of facial recognition, namely identification, both as authentication and as recognition. Despite technological advances, facial recognition technology can still produce erroneous identifications. This paper addresses algorithmic identification failures from an upstream perspective by identifying the main causes of misidentifications (in particular, the probabilistic character of this technology, its 'black box' nature and its algorithmic bias) and from a downstream perspective, highlighting the possible legal consequences of such failures in various scenarios (namely liability lawsuits). In addition to presenting the causes and effects of such errors, the paper also presents measures that can be deployed to reduce errors and avoid liabilities. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of transnational challenges and decision-making heuristics and biases on the implementation of risk assessment (RA) process in the context of Critical Infrastructure Protection (CIP). The investigation, which is based on a review of existing literature and a critical analysis of the ISO31000:2018 process, suggests that contemporary RA models and processes fail to adequately consider the domain-based characteristics of transnational Critical Infrastructure (CI) characteristics. They also fail to recognize the full extent of the human decision-making influence on the RA process itself, as well as the relative lack of homogeneity across assets, stakeholders, countries, paradigms, and people in transnational CI environments. The findings from this work provide a theoretical contribution towards a better understanding of the complexities introduced by the transnational nature of CIP and the effect of human decision-making. They can provide the impetus for further research in the fields of CIP and RA. [ABSTRACT FROM AUTHOR]

AbstractThe present investigation aims to examine the influence of various facilitators and biases on the behavioral intention (BI), which in turn lead to actual implementation of Artificial Intelligence (AI) in healthcare. Further to get a comprehensive viewpoint, the study attempts to observe the impact of actual implementation on perceived sustainable performance. Data for the current study were collected from 600 healthcare practitioners who use AI tools to treat patients. To analyze the linkages between exogenous and endogenous constructs Structural Equation Modeling (through AMOS software) was performed. The results indicated that out of eleven facilitators, eight significantly influenced BI to adopt AI. The findings for biases depicted that out of six biases three (Historical, Evaluation and Algorithmic) were supported. Furthermore, findings suggest that BI has a direct positive influence on actual implementation of AI which in turn positively contributes to perceived sustainable performance. The observations of this study could be helpful for practitioners and academicians. It may be used to determine which AI-supported approaches should be used for certain designs in the healthcare sector. Though this research provides valuable insights for healthcare professionals, yet it has certain limitations which may be used by researchers to build frameworks. [ABSTRACT FROM AUTHOR]

Eco-acoustic indices allow us to rapidly evaluate habitats and ecosystems and derive information about anthropophonic impacts. However, it is proven that indices' values and trends are not comparable between studies. These incongruences may be caused by the availability on the market of recorders with different characteristics and costs. Thus, there is a need to reduce these biases and incongruences to ensure an accurate analysis and comparison between soundscape ecology studies and habitat assessments. In this study, we propose and validate an audio recording equalization protocol to reduce eco-acoustic indices' biases, by testing three soundscape recorder models: Song Meter Micro, Soundscape Explorer Terrestrial and Audiomoth. The equalization process aligns the signal amplitude and frequency response of the soundscape recorders to those of a type 1 level meter. The adjustment was made in MATLAB R2023a using a filter curve generated comparing a reference signal (white noise); the measurements were performed in an anechoic chamber using 11 audio sensors and a type 1 sound level meter (able to produce a.WAV file). The statistical validation of the procedure was performed on recordings obtained in an urban and Regional Park (Italy) assessing a significant reduction in indices' biases on the Song Meter Micro and Audiomoth. [ABSTRACT FROM AUTHOR]

The shortwave (SW) feedback to El Niño–Southern Oscillation (ENSO) is one of the largest biases in climate models, as the feedback includes atmosphere–ocean interactions and cloud processes. In this study, the performance of SW feedback over tropical Pacific in 19 models from the 6th Coupled Model Intercomparison Project (CMIP6) is evaluated and the biases are attributed using the historical and Atmospheric Model Intercomparison Project (AMIP) runs and two coupled assimilation experiments. The results demonstrate that while superior to CMIP5 counterparts, most CMIP6 models still underestimate the strength of SW feedback. The underestimates of SW feedback arise mainly from the biased feedbacks to El Niño in the four models with relatively better skills, while from both underestimated negative feedbacks to El Niño and overestimated positive feedbacks to La Niña in other models, which reproduce better seasonal variations than corresponding CMIP5 models. Furthermore, the SW feedback bias is connected to weak convective/stratiform rainfall feedback, which is sensitive/insensitive to sea surface temperature (SST) biases during El Niño/La Niña. The total rainfall feedbacks and dynamical feedbacks are underestimated in the historical runs, more than in CMIP5. Finally, the causes and relationships between feedbacks and mean states are investigated. [ABSTRACT FROM AUTHOR]

Several biases are often introduced in taxonomic studies due to the transformation or loss of biological information that occurs at different stages from the original depositional ecosystem to the finally selected study sample. We propose the creation of a Taphonomic Alteration Index for Fishes (TAIF) that allows an estimation of the extent of such transformation in the selected sample and its representativeness of the complete record. TAIF is designed as an easily-applicable index that can be used to rapidly assess and compare the taphonomic alteration in different taxa or localities and can also be used as a quantitative variable in other analyses (e.g. morphometrical, statistical). To test TAIF, we have used a sample of Teleostei incertae sedis (N = 190) from the Early Cretaceous of the Las Hoyas site (Cuenca, Spain). Although the obtained TAIF values show some variability, they are in general low, as would be expected for a Konservat-Lagerstätte. The low TAIF values obtained fit with the hypothesis that these fishes inhabited low energy and shallow inland waters, where the effect of anoxia and bacterial sealing would have preserved their remains from degradation. Similar indices could be designed for other taxa based on TAIF. [ABSTRACT FROM AUTHOR]

The Cross‐track Infrared Sounder (CrIS) observations (O) contributed greatly to numerical weather prediction. Further contribution depends on the success of all‐sky data assimilation, which requires a method to produce realistic cloud/rain band structures from background fields (i.e., 6‐hr forecasts), and to remove large biases of all‐sky simulation of brightness temperature (TB) in the presence of clouds. In this study, CrIS all‐sky simulations of brightness temperatures at an arbitrarily selected window channel within Typhoon Hinnamnor (2022) are investigated. The 3‐km Weather Research and Forecasting model with three microphysics schemes were used to produce 6‐hr background forecasts (B). The O − B statistic deviate greatly from Gaussian distribution with large biases in either water clouds, or thin ice clouds, or thick ice clouds within Typhoon Hinnamnor. By developing a linear regression function of three all‐sky simulations of TB from 6‐hr forecasts with three microphysics schemes, the O − B statistics approximate a Gaussian normal distribution in water clouds, thin ice clouds and thick ice clouds. Taking the regression function that is established by a training data set to combine 6‐hr background forecasts at later times, the cloud/rain band structures compared much more favorably with CrIS observations than those from an individual microphysic. Furthermore, the regression coefficients derived from Typhoon Hinnamnor (2022) also work for Typhoon Khanun (2023). The work aims to quantify and remove biases in background fields of TB and generating realistic typhoon cloud/rain band structures in background fields will allow a better description of center position, intensity and size to improve typhoon forecasts. Plain Language Summary: This study focuses on enhancing weather forecasts using the Cross‐track Infrared Sounder (CrIS) by improving cloud and rainband structure predictions, especially in typhoons. We examine Typhoon Hinnamnor from 2022 and use a 3‐km resolution Weather Research and Forecasting model, which employs three different microphysics schemes to produce 6‐hr forecasts. However, these forecasts often differ significantly from CrIS observations, especially in optical thick clouds. To address this discrepancy, we develop a linear regression method to rebuild the forecasts, making them more accurate in simulating the cloud patterns observed by CrIS. This method effectively reduces errors and aligns our model's predictions more closely with CrIS real observations. This work is crucial in refining cloud structure predictions within typhoons, leading to a better understanding of a typhoon's center, strength, and size, thus contributing to more reliable typhoon forecasts. Key Points: This study aims to quantify and remove biases in background fields of brightness temperatures of NOAA‐20 Cross‐track Infrared Sounder (CrIS) window channelsA multiple linear regression is used to combine the all‐sky brightness temperature (TB) simulations of CrIS window channel from three microphysics schemesThis work also aims to generate realistic typhoon cloud/rain band structures in background fields of TB of NOAA‐20 CrIS [ABSTRACT FROM AUTHOR]

In this paper, I explore the concept of corrective trust as a pathway to mitigate biases, and potentially build or restore mutual trust in relationships characterized by power imbalances, particularly within the context of healthcare. Corrective trust takes place when we actively choose to trust others when our initial mistrust or hesitation to trust is due to biases. However, existing accounts of trust as a special form of reliance present challenges to practicing corrective trust. I propose a non-reductive account that construes trust as a basic (i.e. irreducible to more basic concepts such as reliance) but complex (i.e. consists of cognitive, affective, and normative components) attitude, disentangling it from reliance and thus addressing associated challenges. This redefinition enables a deeper understanding of trust and facilitates the application of corrective trust to mitigate biases in healthcare and beyond. [ABSTRACT FROM AUTHOR]

This paper focuses on the ethics of AI and the principles behind it. Prior to the rules governing AI, it is necessary to understand the ethical issues related to the development, implementation and responsible use of AI systems. These ethical foundations are necessary to protect the rights and dignity of individuals, ensure fairness in data access and processing, minimise biases and risks associated with algorithms, promote transferability in automated decisions, and promote trustworthiness in technology, human benefit and social welfare. It is also proposed to study the ethics of AI from an all-encompassing point of view that integrates both applied ethics and metaethics of AI. [ABSTRACT FROM AUTHOR]

House valuation involves forecasting household demand for a neighborhood, whereas commercial property valuation involves forecasting area economic activity. Yet, we show optimistic expectations for both. We investigate further possible disparities due to different cultures of home ownership, short, medium, and long-term investment horizons, and bullish or bearish trends. We use an experimental framework to demonstrate expectations are optimistic under all conditions. Buyers use trends to surmise future prices in bull markets, expect price increases in oscillating markets, and price reversals in bear markets, failing to anticipate price busts. Policy implications are important in preventing property boom-induced financial crises. [ABSTRACT FROM AUTHOR]

Observational studies on COVID-19 vaccine effectiveness (VE) have provided critical real-world data, informing public health policy globally. These studies, primarily using pre-existing data sources, have been indispensable in assessing VE across diverse populations and developing sustainable vaccination strategies. Cohort design is frequently employed in VE research. The rapid implementation of vaccination campaigns during the COVID-19 pandemic introduced differential vaccination influenced by sociodemographic disparities, public policies, perceived risks, health-promoting behaviors, and health status, potentially resulting in biases such as healthy user bias, healthy vaccinee effect, frailty bias, differential depletion of susceptibility bias, and confounding by indication. The overwhelming burden on healthcare systems has escalated the risk of data inaccuracies, leading to outcome misclassifications. Additionally, the extensive array of diagnostic tests used during the pandemic has also contributed to misclassification biases. The urgency to publish quickly may have further influenced these biases or led to their oversight, affecting the validity of the findings. These biases in studies vary considerably depending on the setting, data sources, and analytical methods and are likely more pronounced in low- and middle-income country (LMIC) settings due to inadequate data infrastructure. Addressing and mitigating these biases is essential for accurate VE estimates, guiding public health strategies, and sustaining public trust in vaccination programs. Transparent communication about these biases and rigorous improvement in the design of future observational studies are essential.

IntroductionArtificial Intelligence (AI) is increasingly being integrated into anesthesiology to enhance patient safety, improve efficiency, and streamline various aspects of practice.ObjectiveThis study aims to evaluate whether AI-generated images accurately depict the demographic racial and ethnic diversity observed in the Anesthesia workforce and to identify inherent social biases in these images.MethodsThis cross-sectional analysis was conducted from January to February 2024. Demographic data were collected from the American Society of Anesthesiologists (ASA) and the European Society of Anesthesiology and Intensive Care (ESAIC). Two AI text-to-image models, ChatGPT DALL-E 2 and Midjourney, generated images of anesthesiologists across various subspecialties. Three independent reviewers assessed and categorized each image based on sex, race/ethnicity, age, and emotional traits.ResultsA total of 1,200 images were analyzed. We found significant discrepancies between AI-generated images and actual demographic data. The models predominantly portrayed anesthesiologists as White, with ChatGPT DALL-E2 at 64.2% and Midjourney at 83.0%. Moreover, male gender was highly associated with White ethnicity by ChatGPT DALL-E2 (79.1%) and with non-White ethnicity by Midjourney (87%). Age distribution also varied significantly, with younger anesthesiologists underrepresented. The analysis also revealed predominant traits such as “masculine, ““attractive, “and “trustworthy” across various subspecialties.ConclusionAI models exhibited notable biases in gender, race/ethnicity, and age representation, failing to reflect the actual diversity within the anesthesiologist workforce. These biases highlight the need for more diverse training datasets and strategies to mitigate bias in AI-generated images to ensure accurate and inclusive representations in the medical field.

Pelvic fractures significantly impact young individuals, with a prevalence of 20 per 100,000, leading to long-term complications such as chronic pain and genitourinary dysfunction. Notably, women with a history of pelvic fractures face increased cesarean section (C-sections) rates during childbirth. This editorial investigates the factors contributing to higher C-section rates in these women, including provider assumptions about delivery complications and systemic hospital biases. Despite these trends, evidence suggests that vaginal delivery can be successful, especially when considering factors like pelvic displacement and the timing of delivery post-fracture. We advocate for education programs to challenge provider biases, transparent patient communication, and evidence-based practices prioritizing patient-centered care. Addressing these issues can enhance maternal and fetal outcomes, supporting women in making informed decisions about their delivery options. [ABSTRACT FROM AUTHOR]

Abstract Contributory science—including citizen and community science—allows scientists to leverage participant‐generated data while providing an opportunity for engaging with local community members. Data yielded by participant‐generated biodiversity platforms allow professional scientists to answer ecological and evolutionary questions across both geographic and temporal scales, which is incredibly valuable for conservation efforts. The data reported to contributory biodiversity platforms, such as eBird and iNaturalist, can be driven by social and ecological variables, leading to biased data. Though empirical work has highlighted the biases in contributory data, little work has articulated how biases arise in contributory data and the societal consequences of these biases. We present a conceptual framework illustrating how social and ecological variables create bias in contributory science data. In this framework, we present four filters—participation, detectability, sampling and preference—that ultimately shape the type and location of contributory biodiversity data. We leverage this framework to examine data from the largest contributory science platforms—eBird and iNaturalist—in St. Louis, Missouri, the United States, and discuss the potential consequences of biased data. Lastly, we conclude by providing several recommendations for researchers and institutions to move towards a more inclusive field. With these recommendations, we provide opportunities to ameliorate biases in contributory data and an opportunity to practice equitable biodiversity conservation. Read the free Plain Language Summary for this article on the Journal blog.

Shravani Durbhakula,1 Tony Y Wang,2 Kara G Segna,2 Gerard R Limerick,2,3 Mustafa Y Broachwala,3 Michael E Schatman,4 Munfarid A Zaidi,5 Ingharan James Siddarthan,6 Serkan Toy7 1Department of Anesthesiology, Division of Pain Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; 2Department of Anesthesiology & Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA; 3Department of Physical Medicine & Rehabilitation, Johns Hopkins School of Medicine, Baltimore, MD, USA; 4Department of Anesthesiology, Perioperative Care, and Pain Medicine, New York University Grossman School of Medicine, New York, NY, USA; 5Department of Anesthesiology, Baylor College of Medicine, Houston, TX, USA; 6Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA; 7Departments of Basic Science Education and Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, VA, USACorrespondence: Shravani Durbhakula, Department of Anesthesiology, Division of Pain Medicine, Vanderbilt University Medical Center, Nashville, TN, USA, Email s.durbhakula@vumc.orgObjective: To examine the immediate effects of a comprehensive pain course on medical students’ pre-existing perceptions and attitudes toward pain patients and opioid management.Methods: First-year medical students at a major academic medical center enrolled in a required pre-clerkship pain course in June 2020 and completed pre- and post-course online surveys with Likert-scale questions about their attitudes toward pain management and opioid-related issues. Additionally, the surveys included a free-text question where the students listed the first five words that came to mind when hearing the word “opioids”. These words were categorized as “professional” or “lay” words and further as having “positive”, “negative”, or “neutral” connotations. Data analyses included descriptive statistics, as well as non-parametric and parametric tests.Results: Fifty-four of the 119 students responded to pretest and posttest surveys and were included in paired analyses. There was a significant difference between the number of professional words used before (M=1.21, SD=0.97) and after the course (M=2.40 SD=1.33); t(52)=− 6.39, P< 0.001. Students also used more lay-positive words after the course (M=0.81, SD=0.63) than they used pre-course (M=0.23, SD=0.43); t(51)=− 5.98, P< 0.001. Students’ post-course responses to several key Likert-scale questions showed significant shifts toward more positive attitudes about caring for patients with pain. For example, students acknowledged greater comfort in providing opioids for chronic pain (P< 0.001) where appropriate, and enhanced interest in handling complex pain cases (P< 0.001).Conclusion: Results showed that a comprehensive, multi-disciplinary pain course could greatly enhance first-year medical students’ attitudes toward pain management, chronic pain patients, and the complex issues surrounding opioids.Keywords: medical education, opioids, stigma, epidemic, biases, pain

Artificial intelligence has become a powerful tool of innovation that has significantly impacted various areas of business, including human resource management. This study aims to analyse how artificial intelligence has been adopted and contributes to improving the recruitment and selection process in companies in Timișoara, Brașov and București. We investigate how AI is reshaping the job market by altering demand for certain skills and creating new professional roles, and the implications of AI on personal data protection within the recruitment and selection domain. By employing semi-structured interviews with HR and AI professionals, the study captures nuanced insights into the practical implications of AI in the workforce. Results indicate that AI significantly streamlines the recruitment process, enhancing efficiency and potentially reducing biases. However, concerns regarding data privacy and the depersonalization of the hiring process have been noted. Conclusions suggest that while AI can revolutionize recruitment and selection, a balance must be struck between technological benefits and human-centric approaches to workforce management. The study advocates for ongoing dialogue between HR and AI fields to navigate the ethical and practical complexities of AI in recruitment. [ABSTRACT FROM AUTHOR]

Medium Enterprises (MEs) are significant contributors to global economic development. Integrating sustainability practices in their business can support MEs worldwide to become more sustainable, improving companies' performance and stakeholders' expectations. Nevertheless, few MEs adopt sustainable practices. Following Behavioral Decision Theory and Behavioral Strategy literature, we argue that this can be associated with their managers' decision-making processes – apart from not possessing considerable resources like large companies. Via a mixed-method research design involving 277 Italian ME managers, we investigate the cognitive biases that hinder the development of a sustainable performance management system (SPMS) in MEs. We found the most prominent biases influencing SPMS development. Then, we developed a 'SPMS de-biasing funnel' framework. We propose some corrective actions to reduce the impact of the most critical cognitive biases that influence SPMS development, allowing related beneficial potential outcomes. [ABSTRACT FROM AUTHOR]

This article reviews the theory of fairness in AI-from machine learning to federated learning, where the constraints on precision AI fairness and perspective solutions are also discussed. For a reliable and quantitative evaluation of AI fairness, many associated concepts have been proposed, formulated and classified. However, the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure fairness. The privacy worries induce the data unfairness and hence, the biases in the datasets for evaluating AI fairness are unavoidable. The imbalance between algorithms' utility and humanization has further reinforced such worries. Even for federated learning systems, these constraints on precisionAI fairness still exist.Aperspective solution is to reconcile the federated learning processes and reduce biases and imbalances accordingly. [ABSTRACT FROM AUTHOR]

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AbstractBackgroundMethodsResultsDiscussionAll individuals and groups have blind spots that can create problems if unaddressed. The goal of this study was to examine blind spots in medical education from international perspectives.From December 2022 to March 2023, we distributed an electronic survey through international networks of medical students, postgraduate trainees, and medical educators. Respondents named blind spots affecting their medical education system and then rated nine blind spot domains from a study of U.S. medical education along five-point Likert-type scales (1 = much less attention needed; 5 = much more attention needed). We tested for differences between blind spot ratings by respondent groups. We also analyzed the blind spots that respondents identified to determine those not previously described and performed content analysis on open-ended responses about blind spot domains.There were 356 respondents from 88 countries, including 127 (44%) educators, 80 (28%) medical students, and 33 (11%) postgraduate trainees. At least 80% of respondents rated each blind spot domain as needing ‘more’ or ‘much more’ attention; the highest was 88% for ‘Patient perspectives and voices that are not heard, valued, or understood.’ In analyses by gender, role in medical education, World Bank country income level, and region, a mean difference of 0.5 was seen in only five of the possible 279 statistical comparisons. Of 885 blind spots documented, new blind spot areas related to issues that crossed national boundaries (e.g. international standards) and the sufficiency of resources to support medical education. Comments about the nine blind spot domains illustrated that cultural, health system, and governmental elements influenced how blind spots are manifested across different settings.There may be general agreement throughout the world about blind spots in medical education that deserve more attention. This could establish a basis for coordinated international effort to allocate resources and tailor interventions that advance medical education. [ABSTRACT FROM AUTHOR]

Information disorder is becoming a scourge affecting the authenticity and integrity of information among the populace, particularly undergraduates. This study assessed the prevalence of information disorders among undergraduates of Kwara State University, Malete. The study adopted the descriptive survey design. The study population consisted of 25,277 Kwara State University undergraduate students in Malete. A multi-stage sampling technique was used to select respondents from the population. The questionnaire was the instrument used to collect data. The study's findings revealed that lack of awareness of information disorders and the desire to be respected by colleagues were the major causes of information disorders among students. The prevalent information disorders among students were clickbaits, hoaxes, biases and imposter websites. Fake reviews, rumours, hoaxes, and propaganda were the highest information disorders rampant among students, and some of the strategies to reduce the prevalence of information disorders are identifying the sources of information and those that can be victimised by such information. The study concluded that information disorders are prevalent among the undergraduates of Kwara State University and recommends, among others, that the management of Kwara State University and other stakeholders should orientate undergraduates on information disorders and their challenges. [ABSTRACT FROM AUTHOR]

Although medical education is affected by numerous blind spots, there is limited evidence to determine which blind spots to prioritize. In summer 2022, we surveyed stakeholders from U.S. medical education who had identified 9 domains and 72 subdomains of blind spots. Respondents used 4-point Likert-type scales to rate the extent and magnitude of problems caused for each domain and subdomain. Respondents also provided comments for which we did content analysis. A total of 23/27 (85%) stakeholders responded. The majority of respondents rated each blind spot domain as moderate-major in both extent and problems they cause. Patient perspectives and voices that are not heard, valued, or understood was the domain with the most stakeholders rating extent (n = 20, 87%) and problems caused (n = 23, 100%) as moderate or major. Admitting and selecting learners likely to practice in settings of highest need was the subdomain with the most stakeholders rating extent (n = 21, 91%) and problems caused (n = 22, 96%) as moderate or major. Respondents' comments suggested blind spots may depend on context and persist because of hierarchies and tradition. We found blind spots differed in relative importance. These data may inform further research and direct interventions to improve medical education. [ABSTRACT FROM AUTHOR]

Critics of empathy argue that empathy is exhausting, easily manipulated, exacerbates rather than relieves conflict, and is too focused on individual experiences. Apparently, empathy not only fails to stop negative acts like sadism, bullying, and terrorism, it motivates and promotes such acts. These scholars argue that empathy will not save us from partisanship and division. In fact, it might make us worse off. I will argue that empathy exhibits bias in the ways critics describe because empathy is motivated. Conceiving of empathy as motivated leads to surprising conclusions about our tools for moral decision‐making. [ABSTRACT FROM AUTHOR]

In the present era of techs and machines, various machine learning models including Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Neural Networks are examined in detail, highlighting their strengths, weaknesses, and suitability for sentiment analysis tasks. The objective of this study is to assess the effectiveness of different machine learning techniques in capturing nuances of sentiment expressed in linguistic texts. It is aimed to examine the performance of these approaches in handling challenges such as sarcasm, irony, and context-dependent sentiment. Through a comprehensive review and analysis of existing literature, the goal is to identify key research gaps and propose potential avenues for future research. In data procedure the extraction of textual data from websites, forums, social media platforms, and other online sources is known as data scraping. In data analysis performance evaluation to assessed the effectiveness of the model, compute a number of evaluation measures, including recall, accuracy, precision, F1-score, and confusion matrix. Analysed the model's predictions qualitatively by looking at samples that were incorrectly classified and detecting any biases or tendencies. In the results strong open-source NLP system BERT (Bidirectional Encoder Representations from Transformers) is excellent at deciphering context and confusing words. The findings show that the selected method had a considerable impact on how well sentiment analysis models performed. The findings contribute to the advancement of sentiment analysis methodologies tailored specifically for linguistic data. [ABSTRACT FROM AUTHOR]

Dental microwear is a common and wellestablished technique which allows the short-term reconstruction of the dietary behaviour in extinct and extant vertebrates, allowing inferences about daily, seasonal, or regional variations in diets. However, the use of this method may be limited because taphonomic processes can affect enamel surfaces and modify or obliterate dietary microwear features. Considering the substantial number of agents which can impact the archaeological record, dental microwear alteration processes are poorly known, producing a potential bias in dietary interpretations. In this study, the effect of trampling on dental occlusal surfaces, one of the most common processes recorded in archaeological assemblages, has been experimentally investigated for the first time. The results allowed us to (1) distinguish taphonomic and dietary marks; (2) assess the impacts of trampling on occlusal surfaces; and (3) infer the agents which modified the dental microwear from the teeth obtained from the archaeological sites. The importance of this work lies in the specific guidelines it offers to discriminate trampling marks from microwear features, improving the reliability of dietary interpretations. [ABSTRACT FROM AUTHOR]