Relevant bibliographies by topics / Algorithmic Auditing

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

Academic literature on the topic 'Algorithmic Auditing'

Author: Grafiati
Published: 8 February 2025

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Journal articles on the topic "Algorithmic Auditing"

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Dash, Abhisek, Stefan Bechtold, Jens Frankenreiter, et al. "Antitrust, Amazon, and Algorithmic Auditing." Journal of Institutional and Theoretical Economics 180, no. 2 (2024): 319. http://dx.doi.org/10.1628/jite-2024-0014.

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Shen, Hong, Alicia DeVos, Motahhare Eslami, and Kenneth Holstein. "Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021): 1–29. http://dx.doi.org/10.1145/3479577.

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A growing body of literature has proposed formal approaches to audit algorithmic systems for biased and harmful behaviors. While formal auditing approaches have been greatly impactful, they often suffer major blindspots, with critical issues surfacing only in the context of everyday use once systems are deployed. Recent years have seen many cases in which everyday users of algorithmic systems detect and raise awareness about harmful behaviors that they encounter in the course of their everyday interactions with these systems. However, to date little academic attention has been granted to these
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Raji, Inioluwa Deborah, and Joy Buolamwini. "Actionable Auditing Revisited." Communications of the ACM 66, no. 1 (2022): 101–8. http://dx.doi.org/10.1145/3571151.

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Although algorithmic auditing has emerged as a key strategy to expose systematic biases embedded in software platforms, we struggle to understand the real-world impact of these audits and continue to find it difficult to translate such independent assessments into meaningful corporate accountability. To analyze the impact of publicly naming and disclosing performance results of biased AI systems, we investigate the commercial impact of Gender Shades, the first algorithmic audit of gender- and skin-type performance disparities in commercial facial analysis models. This paper (1) outlines the au
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Broussard, Meredith. "How to Investigate an Algorithm." Issues in Science and Technology 39, no. 4 (2023): 85–89. http://dx.doi.org/10.58875/oake4546.

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Metaxa, Danaë, Joon Sung Park, Ronald E. Robertson, et al. "Auditing Algorithms: Understanding Algorithmic Systems from the Outside In." Foundations and Trends® in Human–Computer Interaction 14, no. 4 (2021): 272–344. http://dx.doi.org/10.1561/1100000083.

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Conitzer, Vincent, Gillian K. Hadfield, and Shannon Vallor. "Technical Perspective: The Impact of Auditing for Algorithmic Bias." Communications of the ACM 66, no. 1 (2022): 100. http://dx.doi.org/10.1145/3571152.

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Seidelin, Cathrine, Therese Moreau, Irina Shklovski, and Naja Holten Møller. "Auditing Risk Prediction of Long-Term Unemployment." Proceedings of the ACM on Human-Computer Interaction 6, GROUP (2022): 1–12. http://dx.doi.org/10.1145/3492827.

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As more and more governments adopt algorithms to support bureaucratic decision-making processes, it becomes urgent to address issues of responsible use and accountability. We examine a contested public service algorithm used in Danish job placement for assessing an individual's risk of long-term unemployment. The study takes inspiration from cooperative audits and was carried out in dialogue with the Danish unemployment services agency. Our audit investigated the practical implementation of algorithms. We find (1) a divergence between the formal documentation and the model tuning code, (2) tha
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Jin, Xing, Mingchu Li, Xiaomei Sun, Cheng Guo, and Jia Liu. "Reputation-based multi-auditing algorithmic mechanism for reliable mobile crowdsensing." Pervasive and Mobile Computing 51 (December 2018): 73–87. http://dx.doi.org/10.1016/j.pmcj.2018.10.001.

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Nguyen, Lan N., J. David Smith, Jinsung Bae, Jungmin Kang, Jungtaek Seo, and My T. Thai. "Auditing on Smart-Grid With Dynamic Traffic Flows: An Algorithmic Approach." IEEE Transactions on Smart Grid 11, no. 3 (2020): 2293–302. http://dx.doi.org/10.1109/tsg.2019.2951505.

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Beatrice Oyinkansola Adelakun. "THE IMPACT OF AI ON INTERNAL AUDITING: TRANSFORMING PRACTICES AND ENSURING COMPLIANCE." Finance & Accounting Research Journal 4, no. 6 (2022): 350–70. http://dx.doi.org/10.51594/farj.v4i6.1316.

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Artificial Intelligence (AI) is revolutionizing internal auditing by transforming traditional practices and enhancing compliance mechanisms. This abstract explores the multifaceted impact of AI on internal auditing, highlighting key advancements in efficiency, accuracy, risk management, and regulatory adherence. AI technologies, particularly machine learning and advanced data analytics, are enhancing the capabilities of internal auditors to analyze large volumes of data swiftly and with greater precision. Traditional internal auditing methods, often constrained by manual processes and sampling
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Dissertations / Theses on the topic "Algorithmic Auditing"

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Bouchaud, Paul. "Beyond the Black Box. Social Structures and Dynamics in the Digital Age : Reconstructing, Modelling and Assessing the Impact of Major Digital Infrastructures." Electronic Thesis or Diss., Paris, EHESS, 2024. http://www.theses.fr/2024EHES0162.

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Cette thèse examine les effets des systèmes algorithmiques utilisés par les grandes plateformes en ligne sur le discours public et la société. À travers des audits expérimentaux et des simulations sociales, cette thèse vise à déchiffrer le fonctionnement de ces systèmes qui servent des milliards d'utilisateurs. La thèse aborde trois objectifs principaux : mener des audits des systèmes algorithmiques des plateformes en ligne, étudier les mesures adoptées par les plateformes pour atténuer les effects néfastes de leurs opérations sur la société, et améliorer les simulations sociales avec des donn
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Bendjebla, Souad. "Un algorithme de discrimination de la parole dans le bruit appliqué à une prothèse auditive externe." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq21714.pdf.

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Bendjebla, Souad. "Un algorithme de discrimination de la parole dans le bruit appliqué à une prothèse auditive externe." Mémoire, Université de Sherbrooke, 1995. http://savoirs.usherbrooke.ca/handle/11143/970.

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La présente étude décrit, le développement, la conception et la réalisation d'un algorithme de discrimination de la parole dans le bruit. Cet algorithme est destiné à être utilisé par une prothèse auditive. Le premier chapitre revoit en détail des travaux de recherches qui traitent de la détection de la parole dans le bruit appliquées dans différents domaines. Deux résultats de ces recherches utilisés par la prothèse auditive seront étudiées plus en détail. Le deuxième chapitre traitera de l'anatomie de l'oreille et du mécanisme de l'audition. On y verra en détail l'effet de masque de l'oreill
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Bouayad, Lina. "Analytics and Healthcare Costs (A Three Essay Dissertation)." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5876.

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Both literature and practice have looked at different strategies to diminish healthcare associated costs. As an extension to this stream of research, the present three paper dissertation addresses the issue of reducing elevated healthcare costs using analytics. The first paper looks at extending the benefits of auditing algorithms from mere detection of fraudulent providers to maximizing the deterrence from inappropriate behavior. Using the structure of the physicians' network, a new auditing algorithm is developed. Evaluation of the algorithm is performed using an agent-based simulation and a
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Bernard, Mathieu. "Audition active et intégration sensorimotrice pour un robot autonome bioinspiré." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2014. http://tel.archives-ouvertes.fr/tel-01023986.

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La grande majorité des systèmes perceptifs proposés en robotique héritent d'une conception passive de la perception dans laquelle la génération d'une commande motrice est l'étape ultime d'une succession de traitements purement passifs. Dans le cadre de la localisation de sources sonores, qui est une tâche fondamentale du système auditif, cette approche passive offre de bons résultats lorsque les conditions environnementales sont bien connues et facilement modélisables. Cependant des difficultés apparaissent lorsque l'environnement se complexifie, a fortiori s'il est inconnu ou changeant. Ces d
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Guillon, Pierre. "Individualisation des indices spectraux pour la synthèse binaurale : recherche et exploitation des similarités inter-individuelles pour l’adaptation ou la reconstruction de HRTF." Le Mans, 2009. http://cyberdoc.univ-lemans.fr/theses/2009/2009LEMA1027.pdf.

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Le travail de thèse qui est rapporté dans le présent document a porté sur le problème de l'individualisation des HRTF pour la synthèse binaurale. Les HRTF sont les filtres linéaires, chacun associé à une direction de l'espace, qui portent en eux l'expression de tous les indices physiques de localisation nécessaires pour une perception de l'espace par le système auditif. La synthèse binaurale utilise avantageusement ces filtres pour sculpter les signaux à présenter aux tympans de l'auditeur, afin de lui procurer l'illusion d'une scène sonore réaliste. Les HRTF étant très liées à la morphologie
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Weng, Yi-ting, and 翁翊庭. "Performance Analysis of Dictionary based Data Compression Algorithms for Real-time Auditing." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/68847185722425371366.

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碩士<br>大同大學<br>資訊經營學系(所)<br>101<br>In the information environment, today, most of companies highly rely on information deices. Those which offer kinds of services produce lots of audit trail and log. Also, the new law of Personal Information Protection legislate on October 2012. The content is about how to keep the process of information system completely and to make rule using on process record. Because of that, how to manage log become more important. The general auditing columns are subject action, subject object, action description, time and so on but a completely action may produce log whi
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Books on the topic "Algorithmic Auditing"

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Wang, Wenwu. Machine audition: Principles, algorithms, and systems. Information Science Reference, 2010.

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Metaxa, Danaë, Joon Sung Park, Ronald E. Robertson, Karrie Karahalios, and Christo Wilson. Auditing Algorithms: Understanding Algorithmic Systems from the Outside In. Now Publishers, 2021.

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Book chapters on the topic "Algorithmic Auditing"

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Aragona, Biagio, and Francesco Amato. "Retracing Algorithms: How Digital Social Research Methods Can Track Algorithmic Functioning." In Frontiers in Sociology and Social Research. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11756-5_8.

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AbstractThe expanding use of algorithms in society has called for the emergence of “critical algorithm studies” across several fields, ranging from media studies to geography and from sociology to the humanities. In the past 5 years, a consistent literature on the subject has developed. Inspired by these studies, we explored the ways digital traces may be employed for auditing algorithms and find evidence about algorithmic functioning. We focus on the analysis of digital traces through search engines and Application Programming Interfaces (APIs). We present four cases of how digital traces may be used for auditing algorithms and testing their quality in terms of data, model, and outcomes. The first example is taken from Noble’s (2018) book Algorithms of Oppression. The other three examples are very recent, two of them related to COVID-19 pandemic and about the most controversial type of algorithms: image recognition. Search as research and the analysis of digital traces and footprints within quasi-experimental research designs are useful methods for testing the quality of data, the codes, and the outcomes of algorithms.
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Morgan, Ilse. "The emergence of algorithmic auditing in the public sector." In Continuous Auditing with AI in the Public Sector. CRC Press, 2024. http://dx.doi.org/10.1201/9781003382706-5.

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Hirsch, Dennis, Timothy Bartley, Aravind Chandrasekaran, Davon Norris, Srinivasan Parthasarathy, and Piers Norris Turner. "Technical Solutions." In SpringerBriefs in Law. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-21491-2_9.

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AbstractThis chapter reviews the technological solutions that organizations leverage to ensure the ethical management and downstream use of collected data for building analytic and AI models. Survey respondents discussed solutions that ranged from privacy preserving data management strategies such as differential privacy, to the use of virtualization and data lake control systems for secure access. Survey respondents also keyed in on the clear and pressing need for data and algorithmic auditing technology and systems to support ethical data governance. With respect to how such data is used ethically, respondents identified the importance of algorithmic fairness as well as model transparency as essential to help identify and also mitigate risks associated with real world modeling failures.
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Ovalle, Anaelia, Sunipa Dev, Jieyu Zhao, Majid Sarrafzadeh, and Kai-Wei Chang. "Auditing Algorithmic Fairness in Machine Learning for Health with Severity-Based LOGAN." In Studies in Computational Intelligence. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36938-4_10.

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Boer, Alexander, Léon de Beer, and Frank van Praat. "Algorithm Assurance: Auditing Applications of Artificial Intelligence." In Progress in IS. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11089-4_7.

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AbstractAlgorithm assurance is a specific form of IT assurance that supports risk management and control on applications of risky algorithms in products and in organizations. These algorithms will often be characterized in organizations as applications of Artificial Intelligence (AI), as advanced analytics, or—simply—as predictive models. The aim of this chapter is to introduce the concept of algorithm assurance, to give some background on the relevance and importance of algorithm assurance, and to prepare the auditor for the basic skills needed to organize and execute an algorithm audit. In this chapter we will introduce the algorithm assurance engagement as a specific type of IT audit. After a general discussion of the background of algorithm assurance and the type of IT applications we are concerned with in this type of engagement, we will extensively discuss the scope of an algorithm assurance engagement, how to approach the risk assessment that should take place initially, how to set up and audit plan, and the audit techniques and tools that play a role in an audit plan.
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Khaja Shareef, Sk, Shruti Patil, I. V. Sai Lakshmi Haritha, and Allam Balaram. "Efficient Identity-Based Integrity Auditing for Cloud Storage and Data Sharing." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1669-4_4.

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Wang, Ying, Conghao Ruan, and Chunqiang Hu. "A Blockchain-Based Decentralized Public Auditing Scheme for Cloud Storage." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59016-1_40.

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Xiao, Ke, Ziye Geng, Yunhua He, Gang Xu, Chao Wang, and Wei Cheng. "A Blockchain Based Privacy-Preserving Cloud Service Level Agreement Auditing Scheme." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59016-1_45.

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Xiang, Wenyu, Jie Zhao, Hejiao Huang, Xiaojun Zhang, Zoe Lin Jiang, and Daojing He. "Blockchain-Assisted Privacy-Preserving Public Auditing Scheme for Cloud Storage Systems." In Algorithms and Architectures for Parallel Processing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0801-7_17.

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Chen, Feng, Hong Zhou, Yuchuan Luo, and Yingwen Chen. "Privacy-Preserving Public Auditing Together with Efficient User Revocation in the Mobile Environments." In Wireless Algorithms, Systems, and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21837-3_1.

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Conference papers on the topic "Algorithmic Auditing"

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Ouyang, Xibiao, Baolin Xu, and Jin Jiang. "Analysis on the application of deep neural network model in the improvement of traditional PHP source code auditing tools." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051651.

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Bartley, Nathan, Andres Abeliuk, Emilio Ferrara, and Kristina Lerman. "Auditing Algorithmic Bias on Twitter." In WebSci '21: WebSci '21 13th ACM Web Science Conference 2021. ACM, 2021. http://dx.doi.org/10.1145/3447535.3462491.

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Obi, Ike, and Colin M. Gray. "Auditing Practitioner Judgment for Algorithmic Fairness Implications." In 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS). IEEE, 2023. http://dx.doi.org/10.1109/ethics57328.2023.10154992.

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Epstein, Ziv, Blakeley H. Payne, Judy Hanwen Shen, et al. "TuringBox: An Experimental Platform for the Evaluation of AI Systems." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/851.

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We introduce TuringBox, a platform to democratize the study of AI. On one side of the platform, AI contributors upload existing and novel algorithms to be studied scientifically by others. On the other side, AI examiners develop and post machine intelligence tasks to evaluate and characterize the outputs of algorithms. We outline the architecture of such a platform, and describe two interactive case studies of algorithmic auditing on the platform.
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DeVos, Alicia, Aditi Dhabalia, Hong Shen, Kenneth Holstein, and Motahhare Eslami. "Toward User-Driven Algorithm Auditing: Investigating users’ strategies for uncovering harmful algorithmic behavior." In CHI '22: CHI Conference on Human Factors in Computing Systems. ACM, 2022. http://dx.doi.org/10.1145/3491102.3517441.

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Vecchione, Briana, Karen Levy, and Solon Barocas. "Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies." In EAAMO '21: Equity and Access in Algorithms, Mechanisms, and Optimization. ACM, 2021. http://dx.doi.org/10.1145/3465416.3483294.

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Groves, Lara, Jacob Metcalf, Alayna Kennedy, Briana Vecchione, and Andrew Strait. "Auditing Work: Exploring the New York City algorithmic bias audit regime." In FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency. ACM, 2024. http://dx.doi.org/10.1145/3630106.3658959.

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Costanza-Chock, Sasha, Inioluwa Deborah Raji, and Joy Buolamwini. "Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem." In FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency. ACM, 2022. http://dx.doi.org/10.1145/3531146.3533213.

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Lee, Claire S., Jeremy Du, and Michael Guerzhoy. "Auditing the COMPAS Recidivism Risk Assessment Tool: Predictive Modelling and Algorithmic Fairness in CS1." In ITiCSE '20: Innovation and Technology in Computer Science Education. ACM, 2020. http://dx.doi.org/10.1145/3341525.3393998.

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Perreault, Brooke, Johanna Hoonsun Lee, Ropafadzo Shava, and Eni Mustafaraj. "Algorithmic Misjudgement in Google Search Results: Evidence from Auditing the US Online Electoral Information Environment." In FAccT '24: The 2024 ACM Conference on Fairness, Accountability, and Transparency. ACM, 2024. http://dx.doi.org/10.1145/3630106.3658916.

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Reports on the topic "Algorithmic Auditing"

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Zhang, Shuo, and Peter Kuhn. Measuring Bias in Job Recommender Systems: Auditing the Algorithms. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32889.

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