Relevant bibliographies by topics / AI Bias

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI Bias'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "AI Bias"

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Park, Kee-Burm. "AI Bias and Citizenship." Social Studies Education 61, no. 2 (2022): 95–106. http://dx.doi.org/10.37561/sse.2022.06.61.2.95.

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Michael, Katina, Roba Abbas, Payyazhi Jayashree, Ruwan J. Bandara, and Anas Aloudat. "Biometrics and AI Bias." IEEE Transactions on Technology and Society 3, no. 1 (2022): 2–8. http://dx.doi.org/10.1109/tts.2022.3156405.

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Weber, Cynthia. "Engineering Bias in AI." IEEE Pulse 10, no. 1 (2019): 15–17. http://dx.doi.org/10.1109/mpuls.2018.2885857.

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Richards, Justin, and Krishna Gummadi. "Combating Bias in AI." ITNOW 60, no. 4 (2018): 62–63. http://dx.doi.org/10.1093/itnow/bwy111.

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TYLER, NEIL. "AI BIAS NEEDS TACKLING." New Electronics 55, no. 3 (2022): 26–27. http://dx.doi.org/10.12968/s0047-9624(22)60110-x.

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El-Samad, Hana. "AI, Bias, and Discrimination." GEN Biotechnology 2, no. 6 (2023): 445. http://dx.doi.org/10.1089/genbio.2023.29126.editorial.

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Shim, So Hee. "Research on Instructional Design to Minimize AI Bias and Bias." Korean Society for Artificial Intelligence Ethics 4, no. 1 (2025): 6–25. https://doi.org/10.59728/jaie.2025.4.1.6.

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This study aims to develop students’ critical thinking skills by recognizing and minimizing AI bias. While AI enhances information efficiency, it can also generate distorted outputs due to algorithmic biases. This research analyzes the concept and causes of AI bias and proposes an educational approach to mitigate its effects. Specifically, the study introduces the concept of “flexible bias,” which enables students to acknowledge AI’s potential errors and adopt diverse perspectives. The proposed lesson plan includes analyzing AI bias cases and establishing AI ethics guidelines, fostering critic
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Gawali, Apurva. "Bias Checker AI Web Application: A Framework for Identifying Bias in AI Models." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48266.

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Abstract— Artificial Intelligence (AI) models are widely deployed in decision-making systems, but they often exhibit bias due to skewed training data or inherent algorithmic issues. This paper presents a Bias Checker AI Web Application designed to analyze and detect biases in AI-generated outputs. The system uses natural language processing (NLP) and statistical analysis techniques to assess potential biases in text-based predictions. The web-based interface enables [1] real-time bias evaluation, ensuring transparency and fairness in AI systems. The proposed system provides a user-friendly pla
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JUNG, WONSUP. "“AI for Humanity”: Fairness and the Optimization of Bias in AI." Journal of Human Studies 64 (June 30, 2024): 5–15. http://dx.doi.org/10.33638/jhs.64.1.

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CHISTRUGA, Alexandru. "AI Ethics: The Bias Puzzle." ANALELE ȘTIINŢIFICE ALE UNIVERSITĂŢII „ALEXANDRU IOAN CUZA” DIN IAȘI (SERIE NOUĂ). ȘTIINŢE JURIDICE 70, no. 2 (2024): 37–51. http://dx.doi.org/10.47743/jss-2024-70-2-3.

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The advantages of artificial intelligence are extensively discussed in specialized literature, which claim that technology has the power to fundamentally change society. However, rapid development of artificial intelligence does carry some serious risks, the most important of which is the spread of false and discriminatory information. Since artificial intelligence is "fed" with data from many sources, there is an increased risk that some of the data contains extremist or xenophobic literature. In such circumstances, artificial intelligence could spread extremely dangerous theories and ideas.
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Dissertations / Theses on the topic "AI Bias"

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Lima, Anderson Silva, and Andreas Blixt. "Investigating the possibility of bias against AI-computercomposed music." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19963.

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This study explores how respondents perceive human-composed music and AI-computer-composed music. The aim was to find out if there is a negative bias against AI-computer-composed music. The research questions are 1. How is AI-computer-composed music perceived compared to human-composed music? 2. Are there prejudices towards AI-computer-composed music? If yes, what are the prejudices? Four participants took part in a qualitative experiment and a semi-structured interview. Two music pieces were used as artifacts, one was human-composed, and the AI-computer AIVA composed the other. The results sh
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Fridensköld, Jonatan. "Biased AI : The hidden problem that needs an answer." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17943.

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Kessing, Maria. "Fairness in AI : Discussion of a Unified Approach to Ensure Responsible AI Development." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299936.

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Besides entailing various benefits, AI technologies have also led to increased ethical concerns. Due to the growing attention, a large number of frameworks discussing responsible AI development have been released since 2016. This work aims at analyzing some of these proposals to answer the question (1) “Which approaches can be found to ensure responsible AI development?” For this, the theory section of this paper is looking at various approaches, including (inter-)governmental regulations, research organizations and private companies.  Further, expert interviews have been conducted to answer t
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Joneken, Isabelle. "Empathy and Ethnicity : The Ethnic Empathy Bias." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-10139.

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The aim of this thesis is to overview studies examining the effect ethnicity has on the neural and physiological responses associated with empathy and the underlying mechanisms behind this effect.  It has been revealed that ethnicity can modulate the empathic responses in that faster physiological arousal and greater sensorimotor resonance occurs during the perception of own ethnic members in suffering. A reduction and even total absence of activity in empathy-associated brain regions such as anterior cingulate cortex, anterior insula, temporo partial junction and medial prefrontal cortex has
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Nordström, Rebecca A., and Hannah Björnlinger. "Artificiell intelligens- mer än bara en stödfunktion? : En kvalitativ undersökning hur artificiell intelligens kan medvetandegöra bias i en rekryteringsprocess." Thesis, Högskolan Dalarna, Institutionen för kultur och samhälle, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37486.

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Syftet med denna studie är att bidra med en djupare förståelse för hur rekryterare använder Artificiell Intelligens (AI) i en rekryteringsprocess för att medvetandegöra bias. Tidigare forskning visar att arbetssökandens chanser till arbete påverkas av rekryterarens bias, detta gör att arbetssökanden inte bedöms utefter kompetens. Tidigare studier visar att arbetssökanden missgynnas baserat på olika egenskaper, kopplat till etnicitet, ålder och kön. Rekryteringsprocessen är i ett behov av verktyg som minskar denna bias, där forskning visar att AI-system kan vara ett sådant verktyg. I denna stud
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Fyrvald, Johanna. "Mitigating algorithmic bias in Artificial Intelligence systems." Thesis, Uppsala universitet, Matematiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-388627.

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Artificial Intelligence (AI) systems are increasingly used in society to make decisions that can have direct implications on human lives; credit risk assessments, employment decisions and criminal suspects predictions. As public attention has been drawn towards examples of discriminating and biased AI systems, concerns have been raised about the fairness of these systems. Face recognition systems, in particular, are often trained on non-diverse data sets where certain groups often are underrepresented in the data. The focus of this thesis is to provide insights regarding different aspects that
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Lycken, Hanna. "Artificiell intelligens och gender bias : En studie av samband mellan artificiell intelligens, gender bias och könsdiskriminering." Thesis, Uppsala universitet, Avdelningen för visuell information och interaktion, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-398282.

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AI spås få lika stor påverkan på samhället som elektricitet haft och avancemangen inom till exempel maskininlärning och neurala nätverk har tagit AI in i sektorer som rättsväsende, rekrytering och hälso- och sjukvård. Men AI-system är, precis som människor, känsliga för olika typer av snedvridningar, vilket kan leda till orättvisa beslut. En alarmerande mängd studier och rapporter visar att AI i flera fall speglar, sprider och förstärker befintliga snedvridningar i samhället i form av fördomar och värderingar vad gäller könsstereotyper och könsdiskriminering. Algoritmer som används i bildigenk
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Schildt, Alexandra, and Jenny Luo. "Tools and Methods for Companies to Build Transparent and Fair Machine Learning Systems." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279659.

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AI has quickly grown from being a vast concept to an emerging technology that many companies are looking to integrate into their businesses, generally considered an ongoing “revolution” transforming science and society altogether. Researchers and organizations agree that AI and the recent rapid developments in machine learning carry huge potential benefits. At the same time, there is an increasing worry that ethical challenges are not being addressed in the design and implementation of AI systems. As a result, AI has sparked a debate about what principles and values should guide its developmen
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Melsion, Perez Gaspar Isaac. "Leveraging Explainable Machine Learning to Raise Awareness among Preadolescents about Gender Bias in Supervised Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287554.

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Machine learning systems have become ubiquitous into our society. This has raised concerns about the potential discrimination that these systems might exert due to unconscious bias present in the data, for example regarding gender and race. Whilst this issue has been proposed as an essential subject to be included in the new AI curricula for schools, research has shown that it is a difficult topic to grasp by students. This thesis aims to develop an educational platform tailored to raise the awareness of the societal implications of gender bias in supervised learning. It assesses whether using
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Victorin, Karin. "AI as Gatekeepers to the Job Market : A Critical Reading of; Performance, Bias, and Coded Gaze in Recruitment Chatbots." Thesis, Linköpings universitet, Tema Genus, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177257.

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The topic of this thesis is AI recruitment chatbots, digital discrimination, and data feminism (D´Ignazio and F.Klein 2020), where I aim to critically analyze issues of bias in these types of human-machine interaction technologies. Coming from a professional background of theatre, performance art, and drama, I am curious to analyze how using AI and social robots as hiring tools entails a new type of “stage” (actor’s space), with a special emphasis on social acting. Humans are now required to adjust their performance and facial expressions in the search for, and approval of, a new job. I will u
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Books on the topic "AI Bias"

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1968-, Hao Ping, ed. Mu ai tang bian. Shang wu yin shu guan, 2017.

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Teng, Zhen. Ai ren 72 bian. Long Yin, 2003.

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Wan, Wan. Ai ren bao biao. Fang hua, 1997.

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Ziyan, ed. Ai de biao shi. Lin bai, 1994.

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(xin jia po) Huang, mei qin and Huang xiu min, eds. A mo si de bian bian ri ji: Ai nu˜ sheng, ai peng you, ai you yong! Hu nan shao nian er tong chu ban she, 2012.

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Ying, Wei. Ai qing bian zou qu. Geng Lin, 2002.

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Dan, Xia. Ai qing bian zou qu. Bo Yi, 1997.

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Sachiko, Someya, ed. Ke ai bian zhi xiao wu. Feng shu fang wen hua chu ban she, 2006.

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Haibo, Yang, ed. Ai zai sheng si bian yuan. Xian dai chu ban she, 2002.

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Mengru, Zhou, ed. Ai de bian zhi: Popular fancywork. Hua you chu ban you xian gong si, 2003.

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Book chapters on the topic "AI Bias"

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Agarwal, Sray, and Shashin Mishra. "Bias in Data." In Responsible AI. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76860-7_3.

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Górska, Anna M., and Dariusz Jemielniak. "AI Racial Bias." In Algorithms, Artificial Intelligence and Beyond. Routledge, 2024. http://dx.doi.org/10.4324/9781032646930-18.

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Shin, Donghee. "Algorithmic Bias and Trust." In Debiasing AI. Routledge, 2025. https://doi.org/10.1201/9781003530244-7.

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Agarwal, Sray, and Shashin Mishra. "Remove Bias from ML Output." In Responsible AI. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76860-7_6.

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Agarwal, Sray, and Shashin Mishra. "Remove Bias from ML Model." In Responsible AI. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76860-7_5.

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Manure, Avinash, Shaleen Bengani, and Saravanan S. "Bias and Fairness." In Introduction to Responsible AI. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9982-1_2.

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Balan, Anil. "Bias Mitigation and Fairness." In AI and Legal Education. Routledge, 2025. https://doi.org/10.4324/9781003607397-3.

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Kundi, Bushra, Christo El Morr, Rachel Gorman, and Ena Dua. "Artificial Intelligence and Bias: A Scoping Review." In AI and Society. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003261247-15.

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Bouckaert, Remco R. "Practical Bias Variance Decomposition." In AI 2008: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3_24.

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Latorre Ruiz, Enrique, and Eulalia Pérez Sedeño. "Gender Bias in Artificial Intelligence." In Gender in AI and Robotics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21606-0_4.

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Conference papers on the topic "AI Bias"

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Akhonda, Mohammad Abu Baker S., Alexis Burgon, Kenny Cha, Nicholas Petrick, and Ravi K. Samala. "Label-free AI bias mitigation." In Computer-Aided Diagnosis, edited by Susan M. Astley and Axel Wismüller. SPIE, 2025. https://doi.org/10.1117/12.3047437.

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Senthil, G. A., S. Geerthik, J. Jerlin Ida, and S. Ashika Jubi. "Ethical AI Auditor for Bias Detecting in AI Models Using Adversarial Debiasing." In 2025 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE). IEEE, 2025. https://doi.org/10.1109/amathe65477.2025.11081330.

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Vajpayee, Ashutosh S., and Deepak Khobragade. "The Problem Of Data Bias In Healthcare AI." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842779.

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Moldovan, Andrada-Mihaela-Nicoleta, Andreea Vescan, and Crina Grosan. "Healthcare Bias in AI: A Systematic Literature Review." In 20th International Conference on Evaluation of Novel Approaches to Software Engineering. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013480300003928.

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Mienye, Ibomoiye Domor, George Obaido, Ikiomoye Douglas Emmanuel, and Ayodeji Akeem Ajani. "A Survey of Bias and Fairness in Healthcare AI." In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI). IEEE, 2024. http://dx.doi.org/10.1109/ichi61247.2024.00103.

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Gye, Seoyeon, Junwon Ko, Hyounguk Shon, Minchan Kwon, and Junmo Kim. "Reducing the Content Bias for AI-generated Image Detection." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00049.

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Mustafaev, Tamerlan, Md Belayat Hossain, Robert M. Nishikawa, and Juhun Lee. "AI-driven race prediction from mammographic images: anatomical insights for AI model's bias mitigation." In Computer-Aided Diagnosis, edited by Susan M. Astley and Axel Wismüller. SPIE, 2025. https://doi.org/10.1117/12.3047312.

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Prakhar, Jyoti, and Md Tanwir Uddin Haider. "Detection of Bias Origins in Training Dataset of Cardiovascular Disease using Machine Learning Classifier." In 2025 AI-Driven Smart Healthcare for Society 5.0. IEEE, 2025. https://doi.org/10.1109/ieeeconf64992.2025.10962852.

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Yuan, Chih-Cheng Rex, and Bow-Yaw Wang. "Ensuring Fairness with Transparent Auditing of Quantitative Bias in AI Systems." In 2024 Pacific Neighborhood Consortium Annual Conference and Joint Meetings (PNC). IEEE, 2024. http://dx.doi.org/10.23919/pnc63053.2024.10697374.

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Chansiri, Karikarn, Xinyu Wei, and Ka Ho Brian Chor. "Addressing Gender Bias: A Fundamental Approach to AI in Mental Health." In 2024 5th International Conference on Big Data Analytics and Practices (IBDAP). IEEE, 2024. http://dx.doi.org/10.1109/ibdap62940.2024.10689686.

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Reports on the topic "AI Bias"

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Zinn, Zach. Interrogating AI Bias through Digital Art. Just Tech, Social Science Research Council, 2022. http://dx.doi.org/10.35650/jt.3039.d.2022.

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Blanchard, Alexander, and Laura Bruun. Bias in Military Artificial Intelligence. Stockholm International Peace Research Institute, 2024. https://doi.org/10.55163/cjft9557.

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To support states involved in the policy debate on military artificial intelligence (AI), this background paper provides a deeper examination of the issue of bias in military AI. Three insights arise. First, policymakers could usefully develop an account of bias in military AI that captures shared concern around unfairness. If so, ‘bias in military AI’ might be taken to refer to the systemically skewed performance of a military AI system that leads to unjustifiably different behaviours—which may perpetuate or exacerbate harmful or discriminatory outcomes—depending on such social characteristic
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Haupt, Sue, Bronwyn Graham, and Jane Hirst. Can AI fight sex and gender bias in healthcare? Edited by Grace Jennings-Edquist. Monash University, 2024. http://dx.doi.org/10.54377/6079-ad33.

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Chandler, Katherine. Does Military AI Have Gender? Understanding Bias And Promoting Ethical Approaches In Military Applications of AI. United Nations Institute for Disarmament Research, 2021. http://dx.doi.org/10.37559/gen/2021/04.

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Strauss, Ilan, Isobel Moure, Tim O’Reilly, and Sruly Rosenblat. The State of AI Governance Research: AI Safety and Reliability in Real World Commercial Deployment. AI Disclosures Project, Social Science Research Council, 2025. https://doi.org/10.35650/aidp.4112.d.2025.

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Drawing on 1,178 safety and reliability papers from 9,439 generative AI papers (Jan- uary 2020 - March 2025), we compare research outputs of leading AI companies (An- thropic, Google DeepMind, Meta, Microsoft, and OpenAI) and AI universities (CMU, MIT, NYU, Stanford, UC Berkeley, and University of Washington). We find that cor- porate AI research increasingly concentrates on pre-deployment areas — model align- ment and testing & evaluation — while attention to deployment-stage issues, such as model bias, has waned, as commercial imperatives and existential risks have come into focus. We fi
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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it add
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Ferryman, Kadija. Framing Inequity in Health Technology: The Digital Divide, Data Bias, and Racialization. Just Tech, Social Science Research Council, 2022. http://dx.doi.org/10.35650/jt.3018.d.2022.

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" Since 2010, there has been an exponential growth in health data and health information technologies, such as electronic health records (EHRs), and AI-enabled medical tools. Despite the growth and investment in these technologies, they have had few positive effects on health outcomes, especially for marginalized populations. This review begins by addressing common rhetorical and ethical responses to inequities in health technologies, such as the digital divide and data bias frames. It then problematizes both approaches before proposing that examining racialization, or the creation and circula
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Chernavskikh, Vladislav, and Jules Palayer. Impact of Military Artificial Intelligence on Nuclear Escalation Risk. Stockholm International Peace Research Institute, 2025. https://doi.org/10.55163/fziw8544.

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Increasing integration of artificial intelligence (AI) into military systems has the potential to influence nuclear escalation even when that integration occurs outside nuclear weapon systems. Non-nuclear applications of military AI may compress decision-making timelines, potentially increasing miscalculation risks during a crisis. Opaque recommendations from an AI-powered decision-support system can bias a decision-maker towards acting, while autonomy in a system with counterforce potential may undermine strategic stability by threatening the integrity of second-strike capabilities. Such uses
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Benites, Miguel, and Eric Parrado. Mirror, Mirror on the Wall: Which Jobs Will AI Replace After All?: A New Index of Occupational Exposure. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013125.

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This paper introduces the AI Generated Index of Occupational Exposure (GENOE), a novel measure quantifying the potential impact of artificial intelligence on occupations and their associated tasks. Our methodology employs synthetic AI surveys, leveraging large language models to conduct expert-like assessments. This approach allows for a more comprehensive evaluation of job replacement likelihood, minimizing human bias and reducing assumptions about the mechanisms through which AI innovations could replace job tasks and skills. The index not only considers task automation, but also contextual
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Hunt, Will, and Jacqueline O'Reilly. Rapid Recruitment in Retail: Leveraging AI in the hiring of hourly paid frontline associates during the Covid-19 Pandemic. Digital Futures at Work Research Centre, 2022. http://dx.doi.org/10.20919/alnb9606.

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Increased demand due to the Coronavirus pandemic created the need for Walmart to onboard tens of thousands of workers in a short period. This acted as a catalyst for Walmart to bring forward existing plans to update the hiring system for store-level hourly paid associates in its US stores. The Rapid Recruitment project sought to make hiring safer, faster, fairer and more effective by removing in-person interviews and leveraging machine learning and predictive analytics. This working paper reports on a case study of the Rapid Recruitment project involving semi-structured qualitative interviews
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