Relevant bibliographies by topics / Algorithmic Auditing

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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, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, and Krishna P. Gummadi. "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 (October 13, 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 bottom-up, user-driven auditing processes. In this paper, we propose and explore the concept of everyday algorithm auditing, a process in which users detect, understand, and interrogate problematic machine behaviors via their day-to-day interactions with algorithmic systems. We argue that everyday users are powerful in surfacing problematic machine behaviors that may elude detection via more centrally-organized forms of auditing, regardless of users' knowledge about the underlying algorithms. We analyze several real-world cases of everyday algorithm auditing, drawing lessons from these cases for the design of future platforms and tools that facilitate such auditing behaviors. Finally, we discuss work that lies ahead, toward bridging the gaps between formal auditing approaches and the organic auditing behaviors that emerge in everyday use of algorithmic systems.
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Raji, Inioluwa Deborah, and Joy Buolamwini. "Actionable Auditing Revisited." Communications of the ACM 66, no. 1 (December 20, 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 audit design and structured disclosure procedure used in the Gender Shades study, (2) presents new performance metrics from targeted companies such as IBM, Microsoft, and Megvii (Face++) on the Pilot Parliaments Benchmark (PPB) as of August 2018, (3) provides performance results on PPB by non-target companies such as Amazon and Kairos, and (4) explores differences in company responses as shared through corporate communications that contextualize differences in performance on PPB. Within 7 months of the original audit, we find that all three targets released new application program interface (API) versions. All targets reduced accuracy disparities between males and females and darker- and lighter-skinned subgroups, with the most significant update occurring for the darker-skinned female subgroup that underwent a 17.7--30.4% reduction in error between audit periods. Minimizing these disparities led to a 5.72--8.3% reduction in overall error on the Pilot Parliaments Benchmark (PPB) for target corporation APIs. The overall performance of non-targets Amazon and Kairos lags significantly behind that of the targets, with error rates of 8.66% and 6.60% overall, and error rates of 31.37% and 22.50% for the darker female subgroup, respectively. This is an expanded version of an earlier publication of these results, revised for a more general audience, and updated to include commentary on further developments.
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Broussard, Meredith. "How to Investigate an Algorithm." Issues in Science and Technology 39, no. 4 (July 3, 2023): 85–89. http://dx.doi.org/10.58875/oake4546.

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Metaxa, Danaë, Joon Sung Park, Ronald E. Robertson, Karrie Karahalios, Christo Wilson, Jeff Hancock, and Christian Sandvig. "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 (December 20, 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 (January 14, 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) that the algorithmic model relies on subjectivity, namely the variable which focus on the individual's self-assessment of how long it will take before they get a job, (3) that the algorithm uses the variable "origin" to determine its predictions, and (4) that the documentation neglects to consider the implications of using variables indicating personal characteristics when predicting employment outcomes. We discuss the benefits and limitations of cooperative audits in a public sector context. We specifically focus on the importance of collaboration across different public actors when investigating the use of algorithms in the algorithmic society.
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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 (May 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 (December 30, 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 techniques, are being supplanted by AI-driven approaches that offer comprehensive analysis and real-time insights. This shift enables auditors to identify anomalies, fraud, and operational inefficiencies more effectively, thereby improving the overall accuracy and reliability of audit outcomes. One of the significant benefits of AI in internal auditing is its ability to automate routine and repetitive tasks. By leveraging AI, auditors can focus on higher-value activities, such as strategic risk assessment and decision-making, thus enhancing the overall productivity of the audit function. Furthermore, AI-driven tools can continuously monitor financial transactions and operational processes, providing real-time alerts and insights that help in early detection of potential issues and proactive risk management. AI also plays a crucial role in ensuring compliance with regulatory standards. By integrating AI systems with compliance frameworks, organizations can automate the tracking and reporting of compliance-related activities. This not only reduces the risk of human error but also ensures that organizations stay updated with evolving regulatory requirements. AI’s ability to process and analyze regulatory texts enables organizations to swiftly adapt to new compliance mandates, thereby mitigating the risk of non-compliance penalties. However, the integration of AI into internal auditing is not without challenges. Ensuring data quality and integrity is paramount, as AI systems rely on accurate data inputs to function effectively. Additionally, the "black box" nature of some AI algorithms can pose transparency issues, making it difficult for auditors to explain how specific conclusions were reached. Addressing algorithmic biases and maintaining auditor expertise in AI technologies are also critical considerations. In conclusion, AI is significantly transforming internal auditing practices by enhancing efficiency, accuracy, and compliance. While the benefits are substantial, careful management of data quality, transparency, and algorithmic biases is essential to fully realize the potential of AI in internal auditing. Keywords: Impact, Ai, Internal Auditing, Transforming Practices, Ensuring Compliance.
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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 données de terrain massives. Les contributions notables de cette thèse comprennent une étude approfondie de la bibliothèque publicitaire de Meta, une analyse des systèmes de recommandation d'Amazon et de Twitter, et la création d'une initiative de don de données pour recueillir des informations sur les expériences réelles des utilisateurs sur des plateformes comme Facebook, Google Search, YouTube et Twitter. La thèse examine également les méthodologies utilisées dans l'audit algorithmique, soulignant la nécessité de prendre en compte la personnalisation et les caractéristiques individuelles des utilisateurs lors de l'évaluation de ces systèmes. Une simulation d'une plateforme similaire à Twitter a été développée, combinant des modèles prédictifs d'engagement des utilisateurs avec une collecte de données à grande échelle. Cette approche a été utilisée pour évaluer comment les stratégies de classement de contenu axées sur la maximisation de l'engagement affectent les informations que les utilisateurs voient, montrant une réduction de la variété du contenu et une représentation politique altérée. Cette thèse conclut en examinant des approches alternatives de curation de contenu au-delà de l'engagement immédiat des utilisateurs, par example via un système de classement basé sur l'approbation diverse des utilisateurs, tout en reconnaissant les difficultés d'évaluer la "valeur démocratique" du contenu civique pour créer des alternatives viables aux systèmes actuels basés sur l'engagement
This thesis examines the effects of algorithmic systems used by major online platforms on public discourse and society. Through experimental audits and social simulations, the research aims to decipher how these systems, which serve billions of users, operate. The thesis addresses three main objectives: conducting audits of online platform algorithmic systems, investigating mitigation measures for misalignments between platform operations and public good, and enhancing social media simulations with massive field data. Notable contributions include a comprehensive study of Meta's Ad Library, an analysis of Amazon's and Twitter's recommendation systems, and the creation of a data donation tool to gather information on actual user experiences across platforms like Facebook, Google Search, YouTube, and Twitter.The thesis also considers the methods used in algorithmic auditing, emphasizing the need to account for personalization and individual user traits when evaluating these systems. A simulation of a Twitter-like platform was developed, combining predictive models of user engagement with large-scale data collection. This approach was used to assess how content ranking strategies focused on maximizing engagement affect the information users see, showing reduced content variety and altered political representation. The research concludes by investigating alternative content curation approaches beyond immediate user engagement, including a ranking system based on diverse user approval, while recognizing the difficulties in assessing the "democratic value" of civic content to create viable alternatives to current engagement-based systems
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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'oreille. Dans le troisième chapitre, le principe de fonctionnement de la prothèse auditive sera étudié. On y décrira ses principales caractéristiques électroacoustiques, ainsi que les systèmes de contrôle et d'adaptation les plus utilisés. On finira ce chapitre par la description de l'influence qu'a eu le développement rapide de la technologie sur la prothèse auditive. Le quatrième chapitre portera sur l'anatomie de l'appareil vocal. Il sera démontré comment un signal vocal peut acquérir, lors de son émission, certaines caractéristiques propres qui lui confèrent des sonorités différentes. Et les caractéristiques distinctives de chacune de ces sonorités seront précisées. Enfin le cinquième et dernier chapitre exposera les étapes suivies pour développer un algorithme de discrimination, présentera les résultats obtenus à partir des essais expérimentaux effectués sur le signal de parole, et discutera ces résultats.
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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 an analytical model. A case study is also included to illustrate the application of the algorithm in the warranty domain. The second paper relies on experimental data to build a personalized medical recommender system geared towards re-enforcing price-sensitive prescription behavior. The study analyzes the impact of time pressure, and procedure cost and prescription prevalence/popularity on the physicians' use of the system's recommendations. The third paper investigates the relationship between patients' compliance and healthcare costs. The study includes a survey of the literature along with a longitudinal analysis of patients' data to determine factors leading to patients' non-compliance, and ways to alleviate it.
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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 difficultés constituent un enjeu important dans le domaine de l'audition artificielle. Cette thèse considère une approche radicalement différente de l'approche passive, inspirée de la psychologie de la perception et de la théorie des contingences sensorimotrices. Cette approche place l'action au coeur du processus de perception, qui est alors vu comme une interaction qu'un agent biologique ou robotique entretient avec son environnement. Alors que l'approche passive nécessite des connaissances sur l'environnement, implicement intégrées dans les traitements par le roboticien, l'approche sensorimotrice suggère au contraire que ces connaissances sont acquises par l'agent de manière autonome, à travers son expérience sensorimotrice. Ainsi cette thèse applique la théorie des contingences sensorimotrices à la localisation de sources sonores pour la robotique autonome. Sur la base d'un modèle bioinspiré du système auditif adapté au contexte robotique, cette thèse propose une redéfinition du problème de la localisation en termes sensorimoteurs. Un modèle de localisation sensorimotrice est alors proposé. Celui-ci se base sur des capacités de perception active bas-niveau pour construire une représentation de l'espace auditif qui est ensuite utilisée pour une localisation passive. En exploitant les capacités d'action du robot, ce modèle permet de s'affranchir des dépendances à l'environnement qui mettent en difficulté l'approche passive, en proposant ainsi un degré d'autonomie supérieur à celui des modèles actuels
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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 de la tête et des pavillons, la spatialisation n'est correctement assurée que si ces filtres sont bien adaptés à l'auditeur. Cependant, la mesure exhaustive des HRTF est coûteuse et inconfortable, et il s'agit donc de développer des moyens alternatifs pour les obtenir : c'est le problème de l'individualisation. On se focalise sur les indices spectraux de la localisation auditive, c'est-à-dire les colorations du spectre à dépendance directionnelle, qui constituent la part des HRTF la plus complexe et la plus variable d'un individu à l'autre. Le constat fondateur de nos investigations est le suivant: bien que les HRTF présentent des caractéristiques intrinsèquement individuelles, on peut dégager des évolutions spatiofréquentielles de leur spectre d'amplitude, communes d'un individu à l'autre, mais susceptibles d'être masquées par deux sources importantes de variabilité, que sont la taille et l'orientation des pavillons. Nous proposons des outils permettant de dépasser ces différences apparentes, afin de se focaliser sur ce qui est vraiment spécifique à chaque individu. Deux solutions techniques d'individualisation des HRTF sont développées en utilisant avantageusement la diversité des comportements offerte par les HRTF d'une base de données. La première solution proposée permet d'adapter, pour un nouvel auditeur, les HRTF d'un autre individu issues d'une base de données, en leur appliquant des transformations guidées par une comparaison morphologique entre les pavillons des deux sujets. Les hypothèses de travail et les outils proposés pour mettre en oeuvre la technique sont validés objectivement grâce aux données recueillies sur 6 sujets, et on montre que la méthode d'adaptation proposée dépasse les performances de l'état de l'art. La seconde solution permet de reconstruire les HRTF d'un nouvel auditeur pour une direction quelconque de l'espace à partir d'un nombre réduit de HRTF individuelles mesurées. La technique proposée est basée sur une base de données constituée des HRTF mesurées finement sur une centaine de sujets, à partir desquelles on génère des prototypes. La reconstruction des HRTF repose sur un processus de reconnaissance de formes entre les HRTF individuelles mesurées et ces prototypes. Une validation objective montre que, selon différents critères, les performances de reconstruction de la technique proposée dépassent celles de l'état de l'art. Ces résultats sont confirmés par une évaluation subjective, menée selon un protocole novateur en synthèse binaurale dynamique
This Ph. D. Thesis deals with the problem of Head-Related Transfer Functions (HRTFs) individualization, in the context of binaural synthesis. HRTFs embed ail the acoustical phenomena occurring on the path between a source at a given position in space and the listener's eardrums. As these linear filters convey all free field localization cues needed by the auditory system to perceive a 3D sound scene, HRTF can be used to sculpt the signals to be reproduced over headphones in order to create convincing spatialized auditory displays : this is the aim of binaural synthesis. HRTFs strongly depend on idiosyncratic morphological features (overall shape of the head, fine structure of the pinnae), and as a result, the use of non-individual HRTFs often leads to perceptual artifacts. Unfortunately, exhaustive acoustic measurements of individual HRTFs are long and uncomfortable for subjects, and it is therefore expected to develop alternative techniques to obtain customized HRTFs : this is the problem of individualization. As they represent the most complex and the most individual part of HRTFs, our study focusses on the colorations induced by pinna filtering, known as spectral cues. The founding assumption of our work is the following : although HRTFs contain intrinsically individual features, common spatio-frequential behaviours can be found from subject to subject. Such similarities may be hidden by the existence of two morphological sources of variability, being the size and orientation of ear pinnae. We develop tools whose aim is to go beyond apparent differences, and to focus on what is really specific of each individual. We propose two technical solutions for HRTF individualization, based on the use of a HRTF database. The first solution uses a 3D model-based morphological matching of pinnae shapes, to properly adapt existing non-individual HRTFs from a database, so that they fit to a new listener. To transform HRTF data, we propose a combination of frequency scaling and rotation shift, whose parameters are predicted by the result of the morphological comparison. The method is designed on the basis of data acquired from six subjects, and it is shown objectively that a better customization is achieved compared to the state-of-the-art technique. The second solut ion aims at reconstructing HRTF for any direction, from only sparse individual HRTF measurements. In order t o overcome the performance of classical blind interpolation techniques, additional knowledge is injected in the reconstruction process :HRTF prototypes are first extracted from the analysis of a large HRTF database, and serve as a well-informed background in a pattern recognition process. An objective assessment shows that , compared to previously developped techniques, HRTF reconstruction achieves a better spatial fidelity with the proposed method. FinaIly, this result is confirmed by a subjective evaluation based on a new protocol
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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
大同大學
資訊經營學系(所)
101
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 which combines over ten columns. Such as large flow of Network, it will create many Giga Byte of log. Therefore, the purpose of my research focuses on how to use dictionary-based compress algorism on detecting abnormal data. With the compress procedure, it will generate a lot of dictionary. Simultaneously, the manager can do real-time audit and continuous monitoring to analyze large amount of data. In the research, I choose LZ78, LZW and LZAP algorism. Use same auditing rule to compress same log and compare the difference of result. Through the analysis process, I can recognize each characteristic of compress algorism and find out what is the best method to audit data. Furthermore, the auditing job would improve log audit efficiency, greatly reduce the time of analytical audit procedures, and also saving the manual checking time.
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Books on the topic "Algorithmic Auditing"

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Wang, Wenwu. Machine audition: Principles, algorithms, and systems. Hershey, PA: 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, 129–40. Cham: 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, 62–79. Boca Raton: 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, 83–91. Cham: 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, 123–36. Cham: 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, 149–83. Cham: 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, 35–44. Singapore: 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, 482–93. Cham: 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, 542–54. Cham: 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, 292–310. Singapore: 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, 1–8. Cham: 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, 79. 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. New York, NY, USA: 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, Casey Jisoo Hong, Bjarke Felbo, Abhimanyu Dubey, Matthew Groh, Nick Obradovich, Manuel Cebrian, and Iyad Rahwan. "TuringBox: An Experimental Platform for the Evaluation of AI Systems." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. Cambridge, MA: National Bureau of Economic Research, August 2024. http://dx.doi.org/10.3386/w32889.

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