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The role of ideological dimensions in shaping acceptance of facial recognition technology and reactions to algorithm bias

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PUBLIC UNDERSTANDING OF SCIENCE 2023年第2期32卷 190-207页

作者： Peng, Yilang Univ Georgia Dept Financial Planning Housing & Consumer Econ Athens GA 30602 USA

Facial recognition technology has been introduced into various aspects of social life, yet it has raised concerns over its infringement of civil liberties and biases against minorities. This study investigates how three ideological dimensions-social dominance orientation, right-wing authoritarianism, and libertarianism-shape facial recognition acceptance. First, two surveys of crowdsourced workers (N = 891 and 587) demonstrated that the acceptance of facial recognition applications is positively associated with right-wing authoritarianism and negatively with libertarianism, whereas social dominance orientation shows little influence. Second, an experiment (N = 496) exposed participants to information about demographic biases in facial recognition applications. This message produced more opposition to facial recognition and this effect largely was not moderated by the three ideological dimensions. In summary, individuals' endorsement of various ideologies predicts their acceptance of facial recognition technology, but messages about algorithm biases in facial recognition can still shape their attitudes regardless of the preexisting ideologies.

关键词： algorithm bias artificial intelligence communitarianism-individualism facial recognition libertarianism political ideology right-wing authoritarianism social dominance orientation

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Can algorithm Knowledge Stop Women from Being Targeted by algorithm bias? The New Digital Divide on Weibo

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JOURNAL OF BROADCASTING & ELECTRONIC MEDIA 2023年第3期67卷 397-422页

作者： Zhang, Yang Chen, Huashan Tianjin Univ Sch New Media & Commun Tianjin Peoples R China Chinese Acad Social Sci Natl Inst Social Dev Beijing Peoples R China Tianjin Univ Tianjin Peoples R China

algorithm knowledge of users plays a crucial role in avoiding them from algorithm bias in recommendation systems. Gender of users has been found to correlate with algorithm bias, but also leaving behind a question of whether this relationship can be described by algorithm knowledge. By using Weibo as an example system, we clarify the aforementioned question from a digital divide theory perspective. We combine a traditional method (questionnaire) with a deep learning computational method to explain algorithm bias in two sequential studies. Our findings suggest that algorithm knowledge solely works for men while fails to protect women. Who users follow helps determine what information they are exposed to on Weibo, and this renders female users' algorithm knowledge useless. This work provides a valuable perspective on algorithm bias: we view algorithm bias as a new digital divide and contribute to the understanding of gender differences by applying the digital divide perspective. Methodologically, we contribute by integrating traditional and computational methods to explain algorithm bias from a folk theory perspective.

关键词： algorithm bias algorithm Knowledge Digital Divide Personalization Gender divide

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Improving Rumor Detection Performance by Using bias Attributes

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Shi, Dini Zeng, Jianping Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Minist Educ Engn Res Ctr Cyber Secur Auditing & Monitoring Shanghai 200433 Peoples R China

With the rapid advancement of social media, the barriers to sharing information have significantly decreased, leading to the rampant spread of rumors across various platforms. Current natural language processing (NLP) techniques use models trained on datasets to detect rumors. However, these datasets often contain inherent biases, which, as has been demonstrated in other NLP tasks, can negatively impact the accuracy of the results. This study confirms the presence of significant bias in rumor detection datasets. Given the intertwined and complex nature of bias and rumors, both of which play a crucial role in assessing the trustworthiness of online content, the article argues that improving detection models should not only focus on performance but also prioritize detecting biased rumors. Addressing this dual challenge is essential to prevent rumor creators from exploiting biases to enhance the spread of false information. To tackle this issue at the data level, this study proposes integrating bias attributes into the training datasets, thereby improving the ability of models to identify biased rumors. Through experiments conducted across multiple rumor detection models, the approach has been shown to enhance the detection of biased rumors in both Chinese and English datasets without compromising overall detection accuracy. This method might be helpful in reducing the spread of biased rumors online, contributing to a healthier information ecosystem.

关键词： Biological system modeling Fake news Social networking (online) Training data Training Feature extraction Data models Accuracy Linguistics Deep learning AI ethics algorithm bias rumor detection

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Reinvention mediates impacts of skin tone bias in algorithms: implications for technology diffusion

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JOURNAL OF COMPUTER-MEDIATED COMMUNICATION 2024年第5期29卷

作者： Overbye-Thompson, Hannah Hamilton, Kristy A. Mastro, Dana Univ Calif Santa Barbara Dept Commun Santa Barbara CA 93106 USA

Two studies examine how skin tone bias in image recognition algorithms impacts users' adoption and usage of image recognition technology. We employed a diffusion of innovations framework to explore perceptions of compatibility, complexity, observability, relative advantage, and reinvention to determine their influence on participants' utilization of image recognition algorithms. Despite being more likely to encounter algorithm bias, individuals with darker skin tones perceived image recognition algorithms as having greater levels of compatibility and relative advantage, being more observable, and less complex and thus used them more extensively compared to those with lighter skin tones. Individuals with darker skin tones also displayed higher levels of reinvention behaviors, suggesting a potential adaptive response to counteract algorithm biases. This study considers how perceptions of technologies that employ image recognition algorithms with known skin tone bias influence how people use these technologies. Across two studies, we found that users with darker skin tones use these technologies more often than those with lighter skin tones. One explanation for these findings may be that those with darker skin tones are required to expend additional effort to reinvent technology so that image recognition features work as effectively as they do for individuals with lighter skin. This may allow individuals with darker skin to develop skills to make facial recognition algorithms suit their needs better. This adjustment process disproportionately burdens those affected by bias, despite enabling them to navigate algorithmic challenges.

关键词： algorithm bias diffusion of innovations technology use skin tone bias survey

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Diversity matters: Robustness of bias measurements in Wikidata 23

Diversity matters: Robustness of bias measurements in Wikida...

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15th ACM Web Science Conference (WebSci)

作者： Das, Paramita Karnam, Sai Keerthana Panda, Anirban Guda, Bhanu Prakash Reddy Sarkar, Soumya Mukherjee, Animesh IIT Kharagpur Dept CSE Kharagpur W Bengal India Carnegie Mellon Univ Pittsburgh PA 15213 USA Microsoft IDC Hyderabad Telangana India

ISBN: (纸本)9798400700897

With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases. Previous works have depicted biases that persist in KGs, as well as employed several metrics for measuring the biases. However, such studies lack in the systematic exploration of the sensitivity of the bias measurements, through varying sources of data, or the embedding algorithms used. To address this research gap, in this work, we present a holistic analysis of bias measurement on the knowledge graph. First, we attempt to reveal data biases that surface in Wikidata for thirteen different demographics selected from seven continents. Next, we attempt to unfold the variance in the detection of biases by two different knowledge graph embedding algorithms - TransE and ComplEx. We conduct our extensive experiments on a large number of occupations sampled from the thirteen demographics with respect to the sensitive attribute, i.e., gender. Our results show that the inherent data bias that persists in KG can be altered by specific algorithm bias as incorporated by KG embedding learning algorithms. Further, we show that the choice of the state-of-the-art KG embedding algorithm has a strong impact on the ranking of biased occupations irrespective of gender. In particular, we find that the embedding algorithm ComplEx is more robust to the choice of demographics compared to TransE. Subsequently, we observe that the similarity of the biased occupations across demographics is minimal which reflects the socio-cultural differences around the globe. This is often overlooked by most of the coarse-grained approaches working at the aggregate level. We believe that this full-scale audit of the bias measurement pipeline will raise awareness among the community while deriving insights related to design choices of data and algorithms both and refrain itself from the popular do

关键词： societal biases knowledge graphs Wikidata data bias algorithm bias knowledge graph embedding

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Why we need biased AI: How including cognitive biases can enhance AI systems

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2024年第8期36卷 1885-1898页

作者： Hagendorff, Thilo Fabi, Sarah Univ Tubingen Cluster Excellence Machine Learning New Perspect Tubingen Germany Univ Calif San Diego Dept Cognit Sci San Diego CA 92103 USA

This paper stresses the importance of biases in the field of artificial intelligence (AI). To foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the implementation of human cognitive biases in learning algorithms. We use insights from cognitive science and apply them to the AI field, combining theoretical considerations with tangible examples depicting promising bias implementation scenarios. Ultimately, this paper is the first tentative step to explicitly putting the idea forth to implement cognitive biases into machines.

关键词： algorithm bias cognitive bias training data cognitive science machine learning

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Challenges in enabling user control over algorithm-based services

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AI & SOCIETY 2024年第1期39卷 195-205页

作者： Koenig, Pascal D. TU Kaiserslautern Dept Social Sci Bldg 57POB 3049 D-67653 Kaiserslautern Germany

algorithmic systems that provide services to people by supporting or replacing human decision-making promise greater convenience in various areas. The opacity of these applications, however, means that it is not clear how much they truly serve their users. A promising way to address the issue of possible undesired biases consists in giving users control by letting them configure a system and aligning its performance with users' own preferences. However, as the present paper argues, this form of control over an algorithmic system demands an algorithmic literacy that also entails a certain way of making oneself knowable: users must interrogate their own dispositions and see how these can be formalized such that they can be translated into the algorithmic system. This may, however, extend already existing practices through which people are monitored and probed and means that exerting such control requires users to direct a computational mode of thinking at themselves.

关键词： algorithmic decision-making algorithm bias algorithmic literacy Surveillance

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The Limitations of Artificial Intelligence in Head and Neck Oncology

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ADVANCES IN THERAPY 2025年第6期42卷 2559-2568页

作者： Rao, Karthik N. Fernandez-Alvarez, Veronica Guntinas-Lichius, Orlando Sreeram, M. P. de Bree, Remco Kowalski, Luiz P. Forastiere, Arlene Pace-Asciak, Pia Rodrigo, Juan P. Saba, Nabil F. Ronen, Ohad Florek, Ewa Randolph, Gregory W. Sanabria, Alvaro Vermorken, Jan B. Hanna, Ehab Y. Ferlito, Alfio Sri Shankara Canc Fdn Dept Head & Neck Oncol Bangalore India Hosp Univ Cent Asturias Dept Vasc & Endovasc Surg Oviedo Spain Jena Univ Hosp Dept Otorhinolaryngol Jena Germany Univ Med Ctr Utrecht Dept Head & Neck Surg Oncol Utrecht Netherlands A C Camargo Canc Ctr Head & Neck Surg & Otorhinolaryngol Dept Sao Paulo Brazil Univ Sao Paulo Fac Med Head & Neck Surg Sao Paulo Brazil Sidney Kimmel Comprehens Canc Ctr Johns Hopkins Baltimore MD USA Univ Toronto Dept Otolaryngol Head & Neck Surg Toronto ON Canada Univ Oviedo Inst Univ Oncol Principado Asturias Oviedo Spain Hosp Univ Cent Asturias Dept Otolaryngol Oviedo Spain CIBERONC Madrid Spain Emory Univ Sch Med Winship Canc Inst Dept Hematol & Med Oncol Atlanta GA USA Bar Ilan Univ Azrieli Fac Med Galilee Med Ctr Dept Otolaryngol Head & Neck Surg Safed Israel Poznan Univ Med Sci Dept Toxicol Lab Environm Res PL-60631 Poznan Poland Harvard Med Sch Massachusetts Gen Hosp Massachusetts Eye & Ear Infirm Boston MA USA Univ Antioquia Hosp Univ San Vicente Fdn CEXCA Ctr Excelencia Enfermedades Cabeza & Cuello Sch MedDept Surg Medellin Colombia Antwerp Univ Hosp Dept Med Oncol Edegem Belgium Univ Antwerp Fac Med & Hlth Sci Antwerp Belgium Univ Texas MD Anderson Canc Ctr Dept Head & Neck Surg Houston TX USA Int Head & Neck Sci Grp I-35100 Padua Italy

Artificial intelligence (AI) is revolutionizing head and neck oncology, offering innovations in tumor detection, treatment planning, and patient management. However, its integration into clinical practice is hindered by several limitations. These include clinician mistrust due to a lack of understanding of AI mechanisms, biases in algorithm development, and the potential over-reliance on technology, which may undermine clinical expertise. Data-related challenges, such as inconsistent quality and limited representativeness of datasets, further complicate AI's application. Ethical, legal, and privacy concerns also pose significant barriers. Addressing these issues through transparent AI systems, clinician education, and clear regulations is essential for ensuring responsible, equitable use in head and neck oncology. This manuscript explores the limitations of AI in head and neck oncology.

关键词： Artificial intelligence Head and neck neoplasms Limitations algorithm bias Clinical decision support systems

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Seeing the Forest from the Trees: Unveiling the Landscape of Generative AI for Education Through Six Evaluation Dimensions 19th

Seeing the Forest from the Trees: Unveiling the Landscape of...

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42nd Conference and Exposition on Structural Dynamics (IMAC)

作者： Feldman-Maggor, Yael Cerratto-Pargman, Teresa Viberg, Olga KTH Royal Inst Technol Stockholm Sweden Stockholm Univ Digital Futures Stockholm Sweden KTH Royal Inst Technol Digital Futures Stockholm Sweden

ISBN: (纸本)9783031723117;9783031723124

Artificial intelligence (AI) holds significant promise as a technology that may improve the quality of educational practices. This includes specialized AI-powered technologies tailored for education and general AI-based technologies, including recently popular generative AI tools that stakeholders are increasingly adapting for teaching and learning. Integrating AI tools into educational settings holds numerous potential pedagogical benefits, such as assisting teachers in planning lessons, promoting personalization, and enhancing student autonomy. However, concerns about bias and discrimination linked to the use of these technologies have rapidly emerged. Today, standardized evaluation criteria to assess the potential contribution of such tools to education and their reliability within the learning and teaching context are lacking. To address this gap, we build on an existing taxonomy for the evaluation of open educational resources (OER) to better suit the unique features of generative AI. The result is a six-dimensional evaluation approach that includes descriptive, pedagogical, representational, communication, scientific content, as well as the ethical and transparency dimension. We then apply this approach to examine the educational potential and ethical concerns around 30 AI tools. The analysis facilitates a critical mapping of the potential and risks of AI-powered technologies in education settings.

关键词： Generative AI algorithm bias Open Educational Resource (OER)

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The Applied Research on Internet of Things Economic Behavior of Generative AI and Its Social and Economic Impacts 7

The Applied Research on Internet of Things Economic Behavior...

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7th International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Chen, Han Natl Def Univ Joint Operat Coll Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9798350385113;9798350385106

The study comprehensively evaluates the economic characteristics and social effects of the behavior of generative AI models by constructing a theoretical model based on behavioral economics and integrating statistical analysis methods applicable to machine learning. Through the analysis of empirical data from different industries and fields, it reveals the positive effects of generative AI grand models in promoting industrial upgrading, optimizing resource allocation, and enhancing the accuracy of market forecasts, the significant advantages of generative AI models in optimizing decision-making, reducing costs, and playing an important role in product and service personalization. In addition, the ethical risks and countermeasure suggestions in the economic application of generative AI are explored, and the importance of policy and regulation construction in guiding the healthy development of AI technology is analyzed. This study not only provides a new perspective for understanding the role of generative AI big models in economic behavior, but also provides a scientific decision-making basis for policy formulation and enterprise practice.

关键词： generative AI economic behavior social impact data privacy algorithm bias

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