Big Data's Disparate Impact

Cited by (1,102)

Items 301–400 of 1,102

1. Criminal Futures
   Simon Egbert, Matthias Leese · 2020 · Routledge eBooks
2. Untitled work
   2017 · arXiv (Cornell University)
3. Why Machine Learning May Lead to Unfairness: Evidence from Risk Assessment for Juvenile Justice in Catalonia
   Tolan, Songül, Miron, Marius, Gómez, Emilia, Castillo, Carlos · 2019 · Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law
4. Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment
   Barabas, Chelsea, Dinakar, Karthik, Ito, Joichi, Virza, Madars, Zittrain, Jonathan · 2017 · arXiv
5. Beyond Porn and Discreditation: Epistemic Promises and Perils of Deepfake Technology in Digital Lifeworlds
   Kerner, Catherine, Risse, Mathias · 2021 · Moral Philosophy and Politics
6. Toward a Sociology of Privacy
   Denise L. Anthony, Celeste Campos‐Castillo, Christine Horne · 2017 · Annual Review of Sociology
7. Uncertainty, risk and the use of algorithms in policy decisions: a case study on criminal justice in the USA
   Kathrin Hartmann, Georg Wenzelburger · 2021 · Policy Sciences
8. Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application
   Florian Jaton · 2021 · Big Data & Society
9. Fair Task Allocation in Crowdsourced Delivery
   Fuat Basık, Buğra Gedik, Hakan Ferhatosmanoğlu · 2018 · IEEE Transactions on Services Computing
10. "Ignorance and Prejudice" in Software Fairness
    Jie M. Zhang, Mark Harman · 2021 · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)
11. The explanation game: a formal framework for interpretable machine learning
    David Watson, Luciano Floridi · 2020 · Synthese
12. The “black box” at work
    Ajunwa, Ifeoma · 2020 · Big Data & Society
13. Algorithms as fetish: Faith and possibility in algorithmic work
    Thomas, Suzanne L, Nafus, Dawn, Sherman, Jamie · 2018 · Big Data & Society
14. A flexible framework for evaluating user and item fairness in recommender systems
    Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogín, Tommaso Di Noia · 2021 · User Modeling and User-Adapted Interaction
15. Value Cards
    Hong Shen, Wesley Hanwen Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, Haiyi Zhu · 2021 · ACM Conference on Fairness, Accountability, and Transparency (FAccT)
16. Agency Laundering and Information Technologies
    Rubel, Alan, Castro, Clinton, Pham, Adam · 2019 · Ethical Theory and Moral Practice
17. Auditing Search Engines for Differential Satisfaction Across Demographics
    Rishabh Mehrotra, Ashton Anderson, Fernando Díaz, Amit Kumar Sharma, Hanna Wallach, Emine Yılmaz · 2017 · International World Wide Web Conference (WWW)
18. Toward involving end-users in interactive human-in-the-loop AI fairness
    Nakao, Yuri, Stumpf, Simone, Ahmed, Subeida, Naseer, Aisha, Strappelli, Lorenzo · 2022 · ACM Transactions on Interactive Intelligent Systems
19. AI and recruiting software: Ethical and legal implications
    Carmen Fernández-Martínez, A. Fernández · 2020 · Paladyn J. Behav. Robotics
20. Protected grounds and the system of non-discrimination law in the context of algorithmic decision-making and artificial intelligence
    Gerards, Janneke, Zuiderveen Borgesius, Frederik · 2021
21. Policing in the Era of Big Data
    G. Ridgeway · 2018 · Annual Review of Criminology
22. Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?
    Michał Choraś, Marek Pawlicki, Damian Puchalski, Rafał Kozik · 2020 · Lecture notes in computer science
23. Mitigating Demographic Bias in AI-based Resume Filtering
    Ketki V. Deshpande, Shimei Pan, James R. Foulds · 2020 · ACM Conference on User Modeling, Adaptation and Personalization (UMAP)
24. A vulnerability analysis: Theorising the impact of artificial intelligence decision-making processes on individuals, society and human diversity from a social justice perspective
    Tetyana Krupiy · 2020 · Computer law & security review
25. The usefulness of algorithmic models in policy making
    Daan Kolkman · 2020 · Government Information Quarterly
26. Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
    Manish Raghavan · 2023 · ACM eBooks
27. When Users Control the Algorithms
    Jenna Burrell, Zoe Kahn, Anne Jonas, Daniel Griffin · 2019 · Proceedings of the ACM on Human-Computer Interaction
28. Improving fairness of artificial intelligence algorithms in Privileged-Group Selection Bias data settings
    Dana Pessach, Erez Shmueli · 2021 · Expert Systems with Applications
29. Diversity and Inclusion Metrics in Subset Selection
    Mitchell, Margaret, Baker, Dylan, Moorosi, Nyalleng, Denton, Remi, Hutchinson, Ben, Hanna, Alex, Gebru, Timnit, Morgenstern, Jamie · 2020 · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
30. Invisible Influence
    Daniel Susser · 2019 · Proceedings of the AAAI/ACM Conference on AI Ethics and Society
31. Bias in Multimodal AI: Testbed for Fair Automatic Recruitment
    Alejandro Peña, Ignacio Serna, Aythami Morales, Julián Fiérrez · 2020 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
32. Hiring as Exploration
    Li, Danielle, Raymond, Lindsey, Bergman, Peter · 2020
33. Hard choices in artificial intelligence
    Dobbe, Roel, Krendl Gilbert, Thomas, Mintz, Yonatan · 2021 · Artificial Intelligence
34. Missing the missing values: The ugly duckling of fairness in machine learning
    Fernando, Martínez‐Plumed, Cèsar, Ferri, David, Nieves, José, Hernández‐Orallo · 2021 · International Journal of Intelligent Systems
35. Situated algorithms: a sociotechnical systemic approach to bias
    Claude Draude, Goda Klumbytė, Phillip Lücking, Pat Treusch · 2019 · Online Information Review
36. A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models
    Wang, H Echo, Landers, Matthew, Adams, Roy, Subbaswamy, Adarsh, Kharrazi, Hadi, Gaskin, Darrell J, Saria, Suchi · 2022 · Journal of the American Medical Informatics Association
37. Equity Beyond Bias in Language Technologies for Education
    Mayfield, Elijah, Madaio, Michael, Prabhumoye, Shrimai, Gerritsen, David, McLaughlin, Brittany, Dixon-Román, Ezekiel, Black, Alan W · 2019 · Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
38. Quantifying and Reducing Stereotypes in Word Embeddings
    Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Tauman Kalai · 2016 · OpenBU (Boston University)
39. Perfectly parallel fairness certification of neural networks
    Caterina Urban, Maria Christakis, Valentin Wüstholz, Fuyuan Zhang · 2020 · Proceedings of the ACM on Programming Languages
40. Algorithms, Correcting Biases
    Cass Robert Sunstein · 2019 · Social research
41. Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
    Veale, Michael, Binns, Reuben · 2017
42. Chapter 3 Platforms at Work: Automated Hiring Platforms and Other New Intermediaries in the Organization of Work
    Ajunwa, Ifeoma, Greene, Daniel · 2019 · Research in the Sociology of Work
43. Algorithmic Fairness
    Dana Pessach, Erez Shmueli · 2023 · Springer eBooks
44. Monsters, Metaphors, and Machine Learning
    Graham Dove, Anne‐Laure Fayard · 2020 · CHI Conference on Human Factors in Computing Systems
45. Economies of Virtue: The Circulation of ‘Ethics’ in Big Tech
    Thao Phan, Jake Goldenfein, Monique Mann, Declan Kuch · 2021 · Science as Culture
46. Machine learning fairness notions: Bridging the gap with real-world applications
    Karima Makhlouf, Sami Zhioua, Catuscia Palamidessi A · 2021 · Information Processing & Management
47. Fair Top-k Ranking with multiple protected groups
    Meike Zehlike, Tom Sühr, R. Baeza-Yates, F. Bonchi, C. Castillo, S. Hajian · 2022 · Information Processing & Management
48. Introduction: Computing in/from the South
    Sareeta Amrute, Luis Felipe R. Murillo · 2020 · Catalyst Feminism Theory Technoscience
49. Algorithmic bias and the Value Sensitive Design approach
    Judith Simon, Pak-Hang Wong, Gernot Rieder · 2020 · Internet Policy Review
50. Seeing like an infrastructure: avidity and difference in algorithmic recommendation
    Nick Seaver · 2021 · Cultural Studies
51. Fair Kernel Learning
    A. Pérez-Suay, V. Laparra, Gonzalo Mateo-García, Jordi Mũnoz-Marí, L. Gómez-Chova, Gustau Camps-Valls · 2017 · ECML/PKDD
52. Misinformation and Its Correction
    Chloe Wittenberg, Adam J. Berinsky · 2020 · Cambridge University Press eBooks
53. Probabilistic verification of fairness properties via concentration
    Bastani, Osbert, Zhang, Xin, Solar-Lezama, Armando · 2019 · Proceedings of the ACM on Programming Languages
54. Transport-based Counterfactual Models
    de Lara, Lucas, González-Sanz, Alberto, Nicholas M. Asher, Laurent Risser, Jean–Michel Loubes · 2021 · arXiv (Cornell University)
55. Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems
    Yash Raj Shrestha, Yongjie Yang · 2019 · Algorithms
56. Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making
    Veale, Michael, Van Kleek, Max, Binns, Reuben · 2018
57. Selecting Directors Using Machine Learning
    Isil Erel, Lea Henny Stern, Chenhao Tan, Michael S. Weisbach · 2018 · National Bureau of Economic Research
58. The Discriminating (Pricing) Actuary
    Frees, Edward W. (Jed), Huang, Fei · 2023 · North American Actuarial Journal
59. Making Sense of Big Data for Security
    Janet B. L. Chan, Lyria Bennett Moses · 2016 · The British Journal of Criminology
60. AI Documentation: A path to accountability
    Königstorfer, Florian, Thalmann, Stefan · 2022 · Journal of Responsible Technology
61. On Measuring Bias in Online Information
    Evaggelia Pitoura, Panayiotis Tsaparas, Giorgos Flouris, Irini Fundulaki, Panagiotis Papadakos, Serge Abiteboul, Gerhard Weikum · 2018 · ACM SIGMOD Record
62. Data deprivations, data gaps and digital divides: Lessons from the COVID-19 pandemic
    Wim A. Naudé, Ricardo Vinuesa · 2021 · Big Data & Society
63. Establishing and evaluating trustworthy AI: overview and research challenges
    Kowald, Dominik, Scher, Sebastian, Pammer-Schindler, Viktoria, Müllner, Peter, Waxnegger, Kerstin, Demelius, Lea, Fessl, Angela, Toller, Maximilian, Mendoza Estrada, Inti Gabriel, Šimić, Ilija et al. · 2024 · Frontiers in Big Data
64. Use of large language models as artificial intelligence tools in academic research and publishing among global clinical researchers
    Mishra, Tanisha, Sutanto, Edward, Rossanti, Rini, Pant, Nayana, Ashraf, Anum, Raut, Akshay, Uwabareze, Germaine, Oluwatomiwa, Ajayi, Zeeshan, Bushra · 2024 · Scientific Reports
65. Silva: Interactively Assessing Machine Learning Fairness Using Causality
    Yan, Jing Nathan, Gu, Ziwei, Lin, Hubert, Rzeszotarski, Jeffrey M. · 2020 · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
66. Computational social science ≠ computer science + social data
    Hanna Wallach · 2018 · Communications of the ACM
67. Algorithms and values in justice and security
    Paul Hayes, Ibo van de Poel, Marc Steen · 2020 · AI & Society
68. Municipal surveillance regulation and algorithmic accountability
    Young, Meg, Katell, Michael, Krafft, P. M. · 2019 · Big Data & Society
69. Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples
    Müller, Sandrine R., Chen, Xi, Peters, Heinrich, Chaintreau, Augustin, Matz, Sandra C. · 2021 · Scientific Reports
70. Data Is the New What? Popular Metaphors & Professional Ethics in Emerging Data Culture
    Stark, Luke, Hoffmann, Anna Lauren · 2019 · Journal of Cultural Analytics
71. Designing Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of Algorithm Performance
    Hong Shen, Haojian Jin, Ángel Alexander Cabrera, Adam Perer, Haiyi Zhu, Jason Hong · 2020 · Proceedings of the ACM on Human-Computer Interaction
72. Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality
    Mark Ryan, Josephina Antoniou, Laurence Brooks, Tilimbe Jiya, Kevin Macnish, Bernd Carsten Stahl · 2021 · Science and Engineering Ethics
73. Managing Bias in Machine Learning Projects
    Tobias Fahse, Viktoria Huber, Benjamin van Giffen · 2021 · Lecture notes in information systems and organisation
74. Recycling Privileged Learning and Distribution Matching for Fairness
    Novi Quadrianto, Viktoriia Sharmanska · 2017 · Spiral (Imperial College London)
75. Machine learning models for credit analysis improvements: Predicting low-income families’ default
    José Rômulo de Castro Vieira, Flávio Barboza, Vinícius Amorim Sobreiro, Herbert Kimura · 2019 · Applied Soft Computing
76. Evaluating causes of algorithmic bias in juvenile criminal recidivism
    Marius Miron, Songül Tolan, Emília Gómez, Carlos Castillo · 2020 · Artificial Intelligence and Law
77. Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning
    Cabrera, Ángel Alexander, Fu, Erica, Bertucci, Donald, Holstein, Kenneth, Talwalkar, Ameet, Hong, Jason I., Perer, Adam · 2023 · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
78. Reassessing values for emerging big data technologies: integrating design-based and application-based approaches
    La Fors, Karolina, Custers, Bart, Keymolen, Esther · 2019 · Ethics and Information Technology
79. Knowledge Management for Sustainable Development in the Era of Continuously Accelerating Technological Revolutions: A Framework and Models
    Meir Russ · 2021 · Sustainability
80. Legal and Ethical Challenges for HR in Machine Learning
    R. Hamilton, H. K. Davison · 2021 · Employee Responsibilities and Rights Journal
81. Measuring the Biases that Matter: The Ethical and Casual Foundations for Measures of Fairness in Algorithms
    Glymour, Bruce, Herington, Jonathan · 2019 · Proceedings of the Conference on Fairness, Accountability, and Transparency
82. FARE: Diagnostics for Fair Ranking using Pairwise Error Metrics
    C. Kuhlman, M. VanValkenburg, Elke A. Rundensteiner · 2019 · The Web Conference
83. Vulnerability in a tracked society: Combining tracking and survey data to understand who gets targeted with what content
    Nadine Bol, Joanna Strycharz, Natali Helberger, Bob van de Velde, Claes H. de Vreese · 2020 · New Media & Society
84. AI and Algorithmic Bias: Source, Detection, Mitigation and Implications
    Runshan Fu, Yan Hua Huang, Param Vir Singh · 2020
85. Improving fairness in machine learning systems: What do industry practitioners need?
    Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé, Miro Dudík, Hanna Wallach · 2018 · arXiv (Cornell University)
86. Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices
    Raghavan, Manish, Barocas, Solon, Kleinberg, Jon, Levy, Karen · 2019 · SSRN Electronic Journal
87. ‘For good measure’: data gaps in a big data world
    Sarah N. Giest, Annemarie Samuels · 2020 · Policy Sciences
88. The Doctor Just Won't Accept That!
    Zachary C. Lipton · 2017 · arXiv (Cornell University)
89. Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability
    Jon Michael Kleinberg, Sendhil Mullainathan · 2019 · National Bureau of Economic Research
90. Bias in Machine Learning -- What is it Good for?
    Thomas Hellström, Virginia Dignum, Suna Bensch · 2020 · arXiv (Cornell University)
91. Accuracy and Fairness for Juvenile Justice Risk Assessments
    Berk, Richard · 2019 · Journal of Empirical Legal Studies
92. Predicting the Future: Art and Algorithms
    Ákos Róna‐Tas · 2020 · Socio-Economic Review
93. A Survey of Bias in Machine Learning Through the Prism of Statistical Parity
    Philippe C. Besse, Eustasio del Barrio, Paula Gordaliza, Jean–Michel Loubes, Laurent Risser · 2021 · The American Statistician
94. Big Data Dreams and Reality in Shenzhen: An Investigation of Smart City Implementation in China
    Jelena Große-Bley, Genia Kostka · 2021 · Big Data & Society
95. Algorithmic paranoia: the temporal governmentality of predictive policing
    Bonnie Sheehey · 2018 · Ethics and Information Technology
96. The disparate equilibria of algorithmic decision making when individuals invest rationally
    Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer Chayes · 2020 · ACM Conference on Fairness, Accountability, and Transparency (FAccT)
97. Rank aggregation algorithms for fair consensus
    Caitlin Kuhlman, Elke Angelika Rundensteiner · 2020 · Proceedings of the VLDB Endowment
98. Rawlsian Fairness for Machine Learning.
    Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth · 2016 · arXiv (Cornell University)
99. A Framework for Assurance Audits of Algorithmic Systems
    Lam, Khoa, Lange, Benjamin, Blili-Hamelin, Borhane, Davidovic, Jovana, Brown, Shea, Hasan, Ali · 2024 · The 2024 ACM Conference on Fairness, Accountability, and Transparency
100. Anti-discrimination learning: a causal modeling-based framework
     Zhang, Lu, Wu, Xintao · 2017 · International Journal of Data Science and Analytics