CoChat
Sign Up
Cited by 261Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Artificial Intelligence Applications

Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

Open access
Cheng, Lu, Varshney, Kush R., Liu, Huan·2021·Journal of Artificial Intelligence Research
DOI 10.1613/jair.1.12814
Open this paper in CoChat — free accountOpen PDF

Abstract

In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI’s indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation. This article appears in the special track on AI & Society.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1613/jair.1.12814

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 135

  1. Angwin, J., & Larson, J. (2015). The tiger mom tax: Asians are nearly twice as likely to get a higher price from princeton review. Retrieved September, 1, 2015.
  2. Ashktorab, Z. (2017). Designing Cyberbullying Prevention and Mitigation Tools. Ph.D. thesis.
  3. Ashktorab, Z., & Vitak, J. (2016). Designing cyberbullying mitigation and prevention solutions through participatory design with teenagers. In CHI, pp. 3895-3905.
  4. Ateniese, G., Mancini, L. V., Spognardi, A., Villani, A., Vitali, D., & Felici, G. (2015). Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers. International Journal of Security and Networks, 10 (3), 137-150.
  5. Baeza-Yates, R. (2018). Bias on the web. Communications of the ACM, 61 (6), 54-61.
  6. Barreno, M., Nelson, B., Sears, R., Joseph, A. D., & Tygar, J. D. (2006). Can machine learning be secure?. In ASIACCS, pp. 16-25.
  7. Becker, G. S. (1993). Nobel lecture: The economic way of looking at behavior. Journal of Political Economy, 101 (3), 385-409.
  8. Beigi, G., & Liu, H. (2020). A survey on privacy in social media: Identification, mitigation, and applications. ACM Transactions on Data Science, 1 (1), 1-38.
  9. Bellamy, R. K., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilović, A., et al. (2019). AI fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM Journal of Research and Development, 63 (4/5), 4-1.
  10. Bickel, P. J., Hammel, E. A., & O'Connell, J. W. (1975). Sex bias in graduate admissions: Data from Berkeley. Science, 187 (4175), 398-404.
  11. Biel, J.-I., Aran, O., & Gatica-Perez, D. (2011). You are known by how you vlog: Personality impressions and nonverbal behavior in youtube.. In ICWSM, pp. 446-449.
  12. Blyth, C. R. (1972). On Simpson's paradox and the sure-thing principle. Journal of the American Statistical Association, 67 (338), 364-366.
  13. Budak, C., Agrawal, D., & El Abbadi, A. (2011). Limiting the spread of misinformation in social networks. In WWW, pp. 665-674.
  14. Cabañas, J. G., Cuevas, Á., Arrate, A., & Cuevas, R. (2020). Does facebook use sensitive data for advertising purposes?. Communications of the ACM, 64 (1), 62-69.
  15. Calders, T., Karim, A., Kamiran, F., Ali, W., & Zhang, X. (2013). Controlling attribute effect in linear regression. In ICDM, pp. 71-80. IEEE.
  16. Calders, T., & Verwer, S. (2010). Three naive Bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery, 21 (2), 277-292.
  17. Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K. N., & Varshney, K. R. (2017). Opti- mized pre-processing for discrimination prevention. In NeurIPS, pp. 3992-4001.
  18. Canca, C. (2020). Operationalizing AI ethics principles. Communications of the ACM, 63 (12), 18-21.
  19. Carroll, A. B., et al. (1991). The pyramid of corporate social responsibility: Toward the moral management of organizational stakeholders. Business Horizons, 34 (4), 39-48.
  20. Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8 (8), 832.
  21. Chen, Z., & Liu, B. (2018). Lifelong machine learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 12 (3), 1-207.
  22. Cheng, L., Guo, R., Shu, K., & Liu, H. (2021). Causal understanding of fake news dissem- ination on social media. In KDD.
  23. Cheng, L., Guo, R., Silva, Y., Hall, D., & Liu, H. (2019). Hierarchical attention networks for cyberbullying detection on the instagram social network. In SDM, pp. 235-243.
  24. Cheng, L., Guo, R., Silva, Y. N., Hall, D., & Liu, H. (2021). Modeling temporal patterns of cyberbullying detection with hierarchical attention networks. ACM/IMS Transactions on Data Science, 2 (2), 1-23.
  25. Cheng, L., Li, J., Silva, Y. N., Hall, D. L., & Liu, H. (2019a). PI-bully: Personalized cyberbullying detection with peer influence.. In IJCAI, pp. 5829-5835.
  26. Cheng, L., Li, J., Silva, Y. N., Hall, D. L., & Liu, H. (2019b). Xbully: Cyberbullying detection within a multi-modal context. In WSDM, pp. 339-347.
  27. Cheng, L., Mosallanezhad, A., Silva, Y. N., Hall, D. L., & Liu, H. (2021). Mitigating bias in session-based cyberbullying detection: A non-compromising approach. In Proceedings of ACL.
  28. Cheng, L., Shu, K., Wu, S., Silva, Y. N., Hall, D. L., & Liu, H. (2020a). Unsupervised cyberbullying detection via time-informed gaussian mixture model. In CIKM.
  29. Ciampaglia, G. L., Shiralkar, P., Rocha, L. M., Bollen, J., Menczer, F., & Flammini, A. (2015). Computational fact checking from knowledge networks. PloS One, 10 (6), e0128193.
  30. Craven, M., & Shavlik, J. W. (1996). Extracting tree-structured representations of trained networks. In NeurIPS, pp. 24-30.
  31. Dadvar, M., Trieschnigg, D., Ordelman, R., & de Jong, F. (2013). Improving cyberbullying detection with user context. In ECIR, pp. 693-696. Springer.
  32. Dalvi, N., Domingos, P., Sanghai, S., & Verma, D. (2004). Adversarial classification. In KDD, pp. 99-108.
  33. Dani, H., Li, J., & Liu, H. (2017). Sentiment informed cyberbullying detection in social media. In ECML PKDD, pp. 52-67. Springer.
  34. Datta, A., Tschantz, M. C., & Datta, A. (2015). Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination. PoPETs, 2015 (1), 92-112.
  35. Della Vedova, M. L., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M., & de Alfaro, L. (2018). Automatic online fake news detection combining content and social signals. In 2018 FRUCT, pp. 272-279. IEEE.
  36. Diaz, M., Wang, H., Calmon, F. P., & Sankar, L. (2019). On the robustness of information- theoretic privacy measures and mechanisms. IEEE Transactions on Information The- ory, 66 (4), 1949-1978.
  37. DiFranzo, D., Taylor, S. H., Kazerooni, F., Wherry, O. D., & Bazarova, N. N. (2018). Upstanding by design: Bystander intervention in cyberbullying. In CHI, pp. 1-12.
  38. Dinakar, K., Jones, B., Havasi, C., Lieberman, H., & Picard, R. (2012). Common sense reasoning for detection, prevention, and mitigation of cyberbullying. ACM TiiS, 2 (3), 1-30.
  39. Dwork, C. (2008). Differential privacy: A survey of results. In TAMC, pp. 1-19. Springer.
  40. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In ITCS, pp. 214-226.
  41. Dwork, C., Immorlica, N., Kalai, A. T., & Leiserson, M. (2018). Decoupled classifiers for group-fair and efficient machine learning. In FAT*, pp. 119-133.
  42. Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography Conference, pp. 265-284. Springer.
  43. Dwork, C., Roth, A., et al. (2014). The algorithmic foundations of differential privacy.. FnT-TCS, 9 (3-4), 211-407.
  44. Ekstrand, M. D., Tian, M., Kazi, M. R. I., Mehrpouyan, H., & Kluver, D. (2018). Exploring author gender in book rating and recommendation. In RecSys, pp. 242-250.
  45. Erhan, D., Bengio, Y., Courville, A., & Vincent, P. (2009). Visualizing higher-layer features of a deep network. University of Montreal, 1341 (3), 1.
  46. Errouissi, R., Cardenas-Barrera, J., Meng, J., Castillo-Guerra, E., Gong, X., & Chang, L. (2015). Bootstrap prediction interval estimation for wind speed forecasting. In 2015 IEEE ECCE, pp. 1919-1924. IEEE.
  47. Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015). Certifying and removing disparate impact. In KDD, pp. 259-268.
  48. Feng, S., Banerjee, R., & Choi, Y. (2012). Syntactic stylometry for deception detection. In ACL, pp. 171-175.
  49. Fredrikson, M., Jha, S., & Ristenpart, T. (2015). Model inversion attacks that exploit confidence information and basic countermeasures. In CCS, pp. 1322-1333.
  50. Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., & Ristenpart, T. (2014). Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing. In USENIX Security, pp. 17-32.
  51. Ge, S., Cheng, L., & Liu, H. (2021). Improving cyberbullying detection with user interaction. In Proceedings of the Web Conference 2021, pp. 496-506.
  52. Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. In STOC, pp. 169- 178.
  53. Getoor, L. (2019). Responsible data science. In Big Data, pp. 1-1. IEEE.
  54. Geyik, S. C., Ambler, S., & Kenthapadi, K. (2019). Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In KDD, pp. 2221-2231.
  55. Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., & Wernsing, J. (2016). Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In ICML, pp. 201-210.
  56. Goldwasser, S., & Micali, S. (1984). Probabilistic encryption. JCSS, 28 (2), 270-299.
  57. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In NeurIPS, pp. 2672-2680.
  58. Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In NeurIPS, pp. 3315-3323.
  59. Hartigan, J. A., & Wong, M. A. (1979). Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (applied statistics), 28 (1), 100-108.
  60. Hind, M., Houde, S., Martino, J., Mojsilovic, A., Piorkowski, D., Richards, J., & Varshney, K. R. (2020). Experiences with improving the transparency of AI models and services. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-8.
  61. Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71 (4), 1161-1189.
  62. Ho, S., Xie, M., Tang, L., Xu, K., & Goh, T. (2001). Neural network modeling with confi- dence bounds: a case study on the solder paste deposition process. IEEE Transactions on Electronics Packaging Manufacturing, 24 (4), 323-332.
  63. Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019). Causability and explainability of artificial intelligence in medicine. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9 (4), e1312.
  64. Hosen, M. A., Khosravi, A., Nahavandi, S., & Creighton, D. (2014). Improving the quality of prediction intervals through optimal aggregation. IEEE Transactions on Industrial Electronics, 62 (7), 4420-4429.
  65. Hu, L., & Chen, Y. (2018). A short-term intervention for long-term fairness in the labor market. In The Web Conference, pp. 1389-1398.
  66. Huang, Q., Singh, V. K., & Atrey, P. K. (2014). Cyberbullying detection using social and textual analysis. In SAM, pp. 3-6.
  67. Jagadish, V. H. (2019). Responsible data science. In 2019 WSDM, pp. 1-1. ACM.
  68. Jiang, H., & Nachum, O. (2020). Identifying and correcting label bias in machine learning. In AISTATS, pp. 702-712.
  69. Joachims, T., Swaminathan, A., & Schnabel, T. (2017). Unbiased learning-to-rank with biased feedback. In WSDM, pp. 781-789.
  70. Kabir, H. D., Khosravi, A., Hosen, M. A., & Nahavandi, S. (2018). Neural network-based uncertainty quantification: A survey of methodologies and applications. IEEE Access, 6, 36218-36234.
  71. Kairouz, P., Oh, S., & Viswanath, P. (2014). Extremal mechanisms for local differential privacy. In NeurIPS, pp. 2879-2887.
  72. Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012). Fairness-aware classifier with prejudice remover regularizer. In ECML PKDD, pp. 35-50. Springer.
  73. Karimi, F., Génois, M., Wagner, C., Singer, P., & Strohmaier, M. (2018). Homophily influences ranking of minorities in social networks. Scientific Reports, 8 (1), 1-12.
  74. Kasiviswanathan, K., & Sudheer, K. (2016). Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models. Modeling Earth Systems and Environment, 2 (1), 22.
  75. Khosravi, A., Nahavandi, S., Srinivasan, D., & Khosravi, R. (2014). Constructing optimal prediction intervals by using neural networks and bootstrap method. IEEE Transac- tions on Neural Networks and Learning Systems, 26 (8), 1810-1815.
  76. Kim, B., Khanna, R., & Koyejo, O. O. (2016). Examples are not enough, learn to criticize! criticism for interpretability. In NeurIPS, pp. 2280-2288.
  77. Kontostathis, A., Reynolds, K., Garron, A., & Edwards, L. (2013). Detecting cyberbullying: query terms and techniques. In Web Science, pp. 195-204.
  78. Kraft, E. M., & Wang, J. (2009). Effectiveness of cyber bullying prevention strategies: A study on students' perspectives.. International Journal of Cyber Criminology, 3 (2).
  79. Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. In NeurIPS, pp. 4066-4076.
  80. Li, J., Cheng, K., Wang, S., Morstatter, F., Trevino, R. P., Tang, J., & Liu, H. (2017). Feature selection: A data perspective. CSUR, 50 (6), 1-45.
  81. Liu, J., Juuti, M., Lu, Y., & Asokan, N. (2017). Oblivious neural network predictions via minionn transformations. In CCS, pp. 619-631.
  82. Lu, T., & Viljanen, M. (2009). Prediction of indoor temperature and relative humidity using neural network models: model comparison. Neural Computing and Applications, 18 (4), 345.
  83. Maaten, L. v. d., & Hinton, G. (2008). Visualizing data using t-sne. JMLR, 9 (Nov), 2579-2605.
  84. Makri, E., Rotaru, D., Smart, N. P., & Vercauteren, F. (2019). EPIC: efficient private image classification (or: Learning from the masters). In CT-RSA, pp. 473-492. Springer.
  85. Marín, L. G., Valencia, F., & Sáez, D. (2016). Prediction interval based on type-2 fuzzy systems for wind power generation and loads in microgrid control design. In 2016 FUZZ-IEEE, pp. 328-335. IEEE.
  86. Menon, A. K., & Williamson, R. C. (2018). The cost of fairness in binary classification. In FAT*, pp. 107-118.
  87. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In FAT*, pp. 220-229.
  88. Mokhberian, N., Abeliuk, A., Cummings, P., & Lerman, K. (2020). Moral framing and ideological bias of news. In SocInfo, pp. 206-219. Springer.
  89. Moraffah, R., Karami, M., Guo, R., Raglin, A., & Liu, H. (2020). Causal interpretability for machine learning-problems, methods and evaluation. SIGKDD Explorations, 22 (1), 18-33.
  90. Nabi, R., & Shpitser, I. (2018). Fair inference on outcomes. In AAAI, Vol. 2018, p. 1931. NIH Public Access.
  91. Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. In 2008 IEEE sp, pp. 111-125. IEEE.
  92. Nguyen, N. P., Yan, G., Thai, M. T., & Eidenbenz, S. (2012). Containment of misinformation spread in online social networks. In Web Science, pp. 213-222.
  93. Papernot, N., McDaniel, P., Wu, X., Jha, S., & Swami, A. (2016b). Distillation as a defense to adversarial perturbations against deep neural networks. In IEEE Symposium on SP, pp. 582-597. IEEE.
  94. Pearl, J. (2009). Causality. Cambridge university press.
  95. Peters, J., Janzing, D., & Schölkopf, B. (2017). Elements of causal inference. The MIT Press.
  96. Pinceti, A., Kosut, O., & Sankar, L. (2019). Data-driven generation of synthetic load datasets preserving spatio-temporal features. In 2019 IEEE PESGM, pp. 1-5. IEEE.
  97. Qian, F., Gong, C., Sharma, K., & Liu, Y. (2018). Neural user response generator: Fake news detection with collective user intelligence.. In IJCAI, Vol. 18, pp. 3834-3840.
  98. Rajabi, Z., Shehu, A., & Purohit, H. (2019). User behavior modelling for fake information mitigation on social web. In SBP-BRiMS, pp. 234-244. Springer.
  99. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" explaining the predictions of any classifier. In KDD, pp. 1135-1144.
  100. Robnik-Šikonja, M., & Bohanec, M. (2018). Perturbation-based explanations of prediction models. In Human and Machine Learning, pp. 159-175. Springer.
  101. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70 (1), 41-55.
  102. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1 (5), 206-215.
  103. Schaefer, R. F., Boche, H., Khisti, A., & Poor, H. V. (2017). Information Theoretic Security and Privacy of Information Systems. Cambridge University Press.
  104. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In FAT*, pp. 59-68.
  105. Shu, K., Cui, L., Wang, S., Lee, D., & Liu, H. (2019). dEFEND: Explainable fake news detection. In KDD, pp. 395-405.
  106. Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2020). Fakenewsnet: A data repos- itory with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data, 8 (3), 171-188.
  107. Shu, K., Wang, S., & Liu, H. (2018). Understanding user profiles on social media for fake news detection. In 2018 IEEE MIPR, pp. 430-435. IEEE.
  108. Shu, K., Wang, S., & Liu, H. (2019). Beyond news contents: The role of social context for fake news detection. In WSDM, pp. 312-320.
  109. Silva, Y. N., Rich, C., & Hall, D. (2016). Bullyblocker: Towards the identification of cyber- bullying in social networking sites. In ASONAM, pp. 1377-1379. IEEE.
  110. Singh, R., Vatsa, M., & Ratha, N. (2021). Trustworthy AI. In 8th ACM IKDD CODS and 26th COMAD, pp. 449-453.
  111. Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling lime and shap: Adversarial attacks on post hoc explanation methods. In AIES, pp. 180-186.
  112. Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008). Cyberbullying: Its nature and impact in secondary school pupils. Journal of Child Psychology and Psychiatry, 49 (4), 376-385.
  113. Ståhl, N., Falkman, G., Karlsson, A., & Mathiason, G. (2020). Evaluation of uncertainty quantification in deep learning. In IPMU, pp. 556-568. Springer.
  114. Stoica, A.-A., Riederer, C., & Chaintreau, A. (2018). Algorithmic glass ceiling in social networks: The effects of social recommendations on network diversity. In The Web Conference, pp. 923-932.
  115. Suter, R., Miladinovic, D., Schölkopf, B., & Bauer, S. (2019). Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness. In ICML, pp. 6056-6065. PMLR.
  116. Sweeney, L. (2002). k-anonymity: A model for protecting privacy. IJUFKS, 10 (05), 557-570.
  117. Tong, A., Du, D.-Z., & Wu, W. (2018). On misinformation containment in online social networks. In NeurIPS, pp. 341-351.
  118. Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., & Ristenpart, T. (2016). Stealing machine learning models via prediction apis. In USENIX Security, pp. 601-618.
  119. Ungar, L. H., De Veaux, R. D., & Rosengarten, E. (1996). Estimating prediction intervals for artificial neural networks. In Proc. of the 9th Yale WALS.
  120. Varodayan, D., & Khisti, A. (2011). Smart meter privacy using a rechargeable battery: Minimizing the rate of information leakage. In ICASSP, pp. 1932-1935. IEEE.
  121. Varshney, K. R. (2019). Trustworthy machine learning and artificial intelligence. XRDS: Crossroads, The ACM Magazine for Students, 25 (3), 26-29.
  122. Vishwamitra, N., Zhang, X., Tong, J., Hu, H., Luo, F., Kowalski, R., & Mazer, J. (2017). Mcdefender: Toward effective cyberbullying defense in mobile online social networks. In CODASPY, pp. 37-42.
  123. Wan, C., Xu, Z., Pinson, P., Dong, Z. Y., & Wong, K. P. (2013). Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Transactions on Power Systems, 29 (3), 1033-1044.
  124. Wang, J., Zhang, J., Bao, W., Zhu, X., Cao, B., & Yu, P. S. (2018a). Not just privacy: Improving performance of private deep learning in mobile cloud. In KDD, pp. 2407- 2416.
  125. Wang, X., Golbandi, N., Bendersky, M., Metzler, D., & Najork, M. (2018b). Position bias estimation for unbiased learning to rank in personal search. In WSDM, pp. 610-618.
  126. Wei, D., Ramamurthy, K. N., & Calmon, F. d. P. (2020). Optimized score transformation for fair classification. In AISTATS.
  127. Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and intelligent laboratory systems, 2 (1-3), 37-52.
  128. Xu, D., Wu, Y., Yuan, S., Zhang, L., & Wu, X. (2019). Achieving causal fairness through generative adversarial networks.. In IJCAI, pp. 1452-1458.
  129. Xu, J.-M., Jun, K.-S., Zhu, X., & Bellmore, A. (2012). Learning from bullying traces in social media. In NAACL HLT, pp. 656-666. ACL.
  130. Yang, Z., & Feng, J. (2020). A causal inference method for reducing gender bias in word embedding relations.. In AAAI, pp. 9434-9441.
  131. Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). Hierarchical attention networks for document classification. In NAACL HLT, pp. 1480-1489.
  132. Young, M., Magassa, L., & Friedman, B. (2019). Toward inclusive tech policy design: a method for underrepresented voices to strengthen tech policy documents. Ethics and Information Technology, 21 (2), 89-103.
  133. Yuan, X., He, P., Zhu, Q., & Li, X. (2019). Adversarial examples: Attacks and defenses for deep learning. IEEE Transactions on Neural Networks and Learning Systems, 30 (9), 2805-2824.
  134. Zhang, G., Wu, Y., Wong, K. P., Xu, Z., Dong, Z. Y., & Iu, H. H.-C. (2014). An ad- vanced approach for construction of optimal wind power prediction intervals. IEEE Transactions on Power Systems, 30 (5), 2706-2715.
  135. Zio, E. (2006). A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes. IEEE Transactions on Nuclear Science, 53 (3), 1460-1478.

Cited by

Sorted by citations

Authors
Cheng, L., Varshney, K. R., & Liu, H.
Affiliations
  • Arizona State University (US)
  • IBM Research - Thomas J. Watson Research Center (US)
ORCID
Venue
Journal of Artificial Intelligence Research
Publisher
AI Access Foundation
Published
2021-08-28
Volume
71
Pages
1137-1181
ISSN
1076-9757 (ISSN-L)
DOI
10.1613/jair.1.12814
OpenAlex
W3198760712
Cited by
261
References
34
Citations by year
2026: 60 · 2025: 77 · 2024: 58 · 2023: 44 · 2022: 17 · 2021: 5
FWCI
38.76
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
Journal of Artificial Intelligence Research (publishedVersion, cc-by)
Funders
  • National Science Foundation (2036127, 1909555)
  • Office of Naval Research (N00014-21-1-4002, N00014)
  • Army Research Office (W911NF2020124, W911NF2110030)
  • Army Research Laboratory (W911NF2020124, W911NF2110030)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
trustworthy AI, algorithmic fairness, algorithmic bias, trust in AI, societal well-being, AI risk mitigation, AI and society

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review