survey

Trustworthy Artificial Intelligence: A Review

Authors:

Kaley J. Rittichier,

Arjan DurresiAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 55, Issue 2

Article No.: 39, Pages 1 - 38

https://doi.org/10.1145/3491209

Published: 18 January 2022 Publication History

Abstract

Artificial intelligence (AI) and algorithmic decision making are having a profound impact on our daily lives. These systems are vastly used in different high-stakes applications like healthcare, business, government, education, and justice, moving us toward a more algorithmic society. However, despite so many advantages of these systems, they sometimes directly or indirectly cause harm to the users and society. Therefore, it has become essential to make these systems safe, reliable, and trustworthy. Several requirements, such as fairness, explainability, accountability, reliability, and acceptance, have been proposed in this direction to make these systems trustworthy. This survey analyzes all of these different requirements through the lens of the literature. It provides an overview of different approaches that can help mitigate AI risks and increase trust and acceptance of the systems by utilizing the users and society. It also discusses existing strategies for validating and verifying these systems and the current standardization efforts for trustworthy AI. Finally, we present a holistic view of the recent advancements in trustworthy AI to help the interested researchers grasp the crucial facets of the topic efficiently and offer possible future research directions.

References

[1]

Peter Achinstein. 1983. The Nature of Explanation. Oxford University Press on Demand.

[2]

Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI). IEEE Access 6 (2018), 52138–52160.

[3]

Aniya Agarwal, Pranay Lohia, Seema Nagar, Kuntal Dey, and Diptikalyan Saha. 2018. Automated test generation to detect individual discrimination in AI models. arXiv preprint arXiv:1809.03260 (2018). https://arxiv.org/abs/1809.03260.

[4]

Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica, May 23, 2016.

[5]

Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, et al. 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion 58 (2020), 82–115.

Digital Library

[6]

Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, et al. 2019. One explanation does not fit all: A toolkit and taxonomy of AI explainability techniques. arXiv e-prints (2019), arXiv–1909. https://arxiv.org/abs/1909.03012.

[7]

Pranjal Awasthi, Matthäus Kleindessner, and Jamie Morgenstern. 2020. Equalized odds postprocessing under imperfect group information. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 1770–1780.

[8]

Sulin Ba. 2001. Establishing online trust through a community responsibility system. Decision Support Systems 31, 3 (2001), 323–336.

Digital Library

[9]

Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS One 10, 7 (2015), e0130140.

[10]

Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, and Tal Wagner. 2019. Scalable fair clustering. In Proceedings of the International Conference on Machine Learning. 405–413.

[11]

Edelman Trust Barometer. 2019. Edelman Trust Barometer Global Report. Retrieved November 2, 2021 from https://www.edelman.com/sites/g/files/aatuss191/files/2019-02/2019_Edelman_Trust_Barometer_Global_Report.pdf.

[12]

Valérie Beaudouin, Isabelle Bloch, David Bounie, Stéphan Clémençon, Florence d’Alché Buc, James Eagan, Winston Maxwell, Pavlo Mozharovskyi, and Jayneel Parekh. 2020. Flexible and context-specific AI explainability: A multidisciplinary approach. Available at SSRN 3559477 (2020).

[13]

Yahav Bechavod and Katrina Ligett. 2017. Penalizing unfairness in binary classification. arXiv preprint arXiv:1707.00044 (2017). https://arxiv.org/pdf/1707.00044.pdf.

[14]

Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, et al. 2019. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias. IBM Journal of Research and Development 63, 4–5 (2019), Article 4, 15 pages.

[15]

Hal Berghel. 2017. Equifax and the latest round of identity theft roulette. Computer 50, 12 (2017), 72–76.

Digital Library

[16]

Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. 2017. A convex framework for fair regression. arXiv preprint arXiv:1706.02409 (2017). https://arxiv.org/abs/1706.02409.

[17]

Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020. Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 648–657.

Digital Library

[18]

Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt. 2018. ‘It’s reducing a human being to a percentage’ perceptions of justice in algorithmic decisions. In Proceedings of the 2018 Chi Conference on Human Factors in Computing Systems. 1–14.

Digital Library

[19]

Emily Black, Samuel Yeom, and Matt Fredrikson. 2020. FlipTest: Fairness testing via optimal transport. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 111–121.

Digital Library

[20]

Miranda Bogen and Aaron Rieke. 2018. Help Wanted: An Examination of Hiring Algorithms, Equity. Technical Report. Upturn.

[21]

Tolga Bolukbasi, Kai-Wei Chang, James Y. Zou, Venkatesh Saligrama, and Adam T. Kalai. 2016. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In Advances in Neural Information Processing Systems. 4349–4357.

Digital Library

[22]

Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021. Machine unlearning. In Proceedings of the 2021 IEEE Symposium on Security and Privacy (SP’21). IEEE, Los Alamitos, CA, 141–159.

[23]

Jon Boyens, Celia Paulsen, Rama Moorthy, Nadya Bartol, and Stephanie A. Shankles. 2015. Supply chain risk management practices for federal information systems and organizations. NIST Special Publication 800, 161 (2015), 32.

[24]

Valerie Braithwaite. 2020. Beyond the bubble that is Robodebt: How governments that lose integrity threaten democracy. Australian Journal of Social Issues 55, 3 (2020), 242–259.

[25]

Kiel Brennan-Marquez. 2017. Plausible cause: Explanatory standards in the age of powerful machines. Vanderbilt Law Review 70 (2017), 1249.

[26]

Dennis Broeders, Erik Schrijvers, Bart van der Sloot, Rosamunde van Brakel, Josta de Hoog, and Ernst Hirsch Ballin. 2017. Big data and security policies: Towards a framework for regulating the phases of analytics and use of big data. Computer Law & Security Review 33, 3 (2017), 309–323.

[27]

Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, and Richard Zemel. 2019. Understanding the origins of bias in word embeddings. In Proceedings of the International Conference on Machine Learning. 803–811.

[28]

Fursind Bundesamt. 2004. Study: “An Investigation into the Performance of Facial Recognition Systems Relative toTheir Planned Use in Photo Identification Documents–BioP I. ” Bundesamt fur Sicherheit in der Informationstechnik.

[29]

Andrea Bunt, Matthew Lount, and Catherine Lauzon. 2012. Are explanations always important? A study of deployed, low-cost intelligent interactive systems. In Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces. 169–178.

Digital Library

[30]

Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency. 77–91.

[31]

B. Burke, D. Cearley, N. Jones, D. Smith, A. Chandrasekaran, C. K. Lu, and K. Panetta. 2019. Gartner Top 10 Strategic Technology Trends for 2020-Smarter with Gartner. Retrieved November 2, 2021 from https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/.

[32]

Ewen Callaway. 2021. DeepMind’s AI predicts structures for a vast trove of proteins. Nature 595, 7869 (2021), 635–635.

[33]

Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. 2017. Optimized pre-processing for discrimination prevention. In Advances in Neural Information Processing Systems. 3992–4001.

Digital Library

[34]

Chiara Campione. 2020. The Dark Nudge Era: Cambridge Analytica, Digital Manipulation in Politics, and the Fragmentation of Society. Bachelor’s Thesis. Luiss Guido Carli.

[35]

Yinzhi Cao and Junfeng Yang. 2015. Towards making systems forget with machine unlearning. In Proceedings of the 2015 IEEE Symposium on Security and Privacy. IEEE, Los Alamitos, CA, 463–480.

Digital Library

[36]

Giuseppe Casalicchio, Christoph Molnar, and Bernd Bischl. 2018. Visualizing the feature importance for black box models. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 655–670.

[37]

Davide Castelvecchi. 2016. Can we open the black box of AI? Nature News 538, 7623 (2016), 20.

[38]

L. Elisa Celis, Amit Deshpande, Tarun Kathuria, and Nisheeth K. Vishnoi. 2016. How to be fair and diverse? arXiv preprint arXiv:1610.07183 (2016).

[39]

Huili Chen, Siam Umar Hussain, Fabian Boemer, Emmanuel Stapf, Ahmad Reza Sadeghi, Farinaz Koushanfar, and Rosario Cammarota. 2020. Developing privacy-preserving AI systems: The lessons learned. In Proceedings of the 2020 57th ACM/IEEE Design Automation Conference (DAC’20). IEEE, Los Alamitos, CA, 1–4.

Digital Library

[40]

Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 785–794.

Digital Library

[41]

Tsong Yueh Chen, Fei-Ching Kuo, Huai Liu, Pak-Lok Poon, Dave Towey, T. H. Tse, and Zhi Quan Zhou. 2018. Metamorphic testing: A review of challenges and opportunities. ACM Computing Surveys 51, 1 (2018), 1–27.

Digital Library

[42]

Alexandra Chouldechova, Diana Benavides-Prado, Oleksandr Fialko, and Rhema Vaithianathan. 2018. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Proceedings of the Conference on Fairness, Accountability, and Transparency. 134–148.

[43]

European Commission. 2020. White Paper on Artificial Intelligence—A European Approach to Excellence and Trust. European Commission.

[44]

Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017. Algorithmic decision making and the cost of fairness. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 797–806.

Digital Library

[45]

Kate Crawford. 2016. Can an algorithm be agonistic? Ten scenes from life in calculated publics. Science, Technology, & Human Values 41, 1 (2016), 77–92.

[46]

Kate Crawford. 2021. The Atlas of AI. Yale University Press.

[47]

Bruno Silveira Cruz and Murillo de Oliveira Dias. 2020. Crashed Boeing 737-MAX: Fatalities or malpractice? GSJ 8, 1 (2020), 2615–2624.

[48]

Angela Daly, S. Kate Devitt, and Monique Mann. 2021. AI ethics needs good data. arXiv preprint arXiv:2102.07333 (2021). https://arxiv.org/ftp/arxiv/papers/2102/2102.07333.pdf.

[49]

M. D. Danny Tobey. 2019. Explainability: Where AI and Liability Meet: Actualités: DLA Piper Global Law Firm. Retrieved November 2, 2021 from https://www.dlapiper.com/fr/france/insights/publications/2019/02/explainability-where-ai-and-liability-meet/.

[50]

Jeffrey Dastin. 2018. Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. Available at https://www.reuters.com.

[51]

Paul R. Daugherty and H. James Wilson. 2018. Human+ Machine: Reimagining Work in the Age of AI. Harvard Business Press.

Digital Library

[52]

Paul B. De Laat. 2018. Algorithmic decision-making based on machine learning from big data: Can transparency restore accountability? Philosophy & Technology 31, 4 (2018), 525–541.

[53]

S. Kate Devitt. 2018. Trustworthiness of autonomous systems. In Foundations of Trusted Autonomy. Springer, Cham, Switzerland, 161–184.

Digital Library

[54]

Virginia Dignum. 2017. Responsible artificial intelligence: Designing AI for human values. ICT Discoveries 1 (2017), 1–8.

[55]

James E. Dobson. 2015. Can an algorithm be disturbed? Machine learning, intrinsic criticism, and the digital humanities. College Literature 42, 4 (2015), 543–564.

[56]

Graham Dove, Kim Halskov, Jodi Forlizzi, and John Zimmerman. 2017. UX design innovation: Challenges for working with machine learning as a design material. In Proceedings of the 2017 Chi Conference on Human Factors in Computing Systems. 278–288.

Digital Library

[57]

Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. 214–226.

Digital Library

[58]

Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson. 2018. Decoupled classifiers for group-fair and efficient machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency. 119–133.

[59]

Morris Dworkin. 2016. Recommendation for block cipher modes of operation: Methods for format-preserving encryption. NIST Special Publication 800 (2016), 38G.

[60]

European Commission. 2018. Ethics Guidelines for Trustworthy AI. Retrieved November 2, 2021 from https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai.

[61]

Anthony Elliott. 2019. The Culture of AI: Everyday Life and the Digital Revolution. Routledge.

[62]

Wenjuan Fan, Jingnan Liu, Shuwan Zhu, and Panos M. Pardalos. 2020. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Annals of Operations Research 294, 1 (2020), 567–592.

[63]

Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015. Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 259–268.

Digital Library

[64]

Stefan Feuerriegel, Mateusz Dolata, and Gerhard Schwabe. 2020. Fair AI: Challenges and opportunities. Business & Information Systems Engineering 62, 1 (2020), 1–7.

[65]

Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter. 2015. Efficient and robust automated machine learning. In Advances in Neural Information Processing Systems. 2962–2970.

Digital Library

[66]

Aaron Fisher, Cynthia Rudin, and Francesca Dominici. 2018. All models are wrong but many are useful: Variable importance for black-box, proprietary, or misspecified prediction models, using model class reliance. arXiv preprint arXiv:1801.01489 (2018), 237–246.

[67]

Anthony W. Flores, Kristin Bechtel, and Christopher T. Lowenkamp. 2016. False positives, false negatives, and false analyses: A rejoinder to machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks. Federal Probation 80 (2016), 38.

[68]

Luciano Floridi and Josh Cowls. 2019. A unified framework of five principles for AI in society. HDSR 1.1 (2019).

[69]

Luciano Floridi, Josh Cowls, Thomas C. King, and Mariarosaria Taddeo. 2020. How to design AI for social good: Seven essential factors. Science and Engineering Ethics 26, 3 (2020), 1771–1796.

[70]

Department for Transport (UK). 2015. The Pathway to Driverless Cars: A Code of Practice for Testing. Department for Transport (UK).

[71]

Maria Jose Gacto, Rafael Alcalá, and Francisco Herrera. 2011. Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures. Information Sciences 181, 20 (2011), 4340–4360.

Digital Library

[72]

Pratik Gajane and Mykola Pechenizkiy. 2017. On formalizing fairness in prediction with machine learning. arXiv preprint arXiv:1710.03184 (2017).

[73]

Gemma Galdon Clavell, Mariano Martín Zamorano, Carlos Castillo, Oliver Smith, and Aleksandar Matic. 2020. Auditing algorithms: On lessons learned and the risks of data minimization. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 265–271.

Digital Library

[74]

Simson L. Garfinkel. 2015. De-Identification of Personal Information. National Institute of Standards and Technology.

[75]

Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. 2018. Datasheets for datasets. arXiv preprint arXiv:1803.09010 (2018). https://arxiv.org/abs/1803.09010.

Digital Library

[76]

Robin C. Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017).

[77]

Amirata Ghorbani, James Wexler, James Y. Zou, and Been Kim. 2019. Towards automatic concept-based explanations. In Advances in Neural Information Processing Systems. 9277–9286.

Digital Library

[78]

Bryce Goodman and Seth Flaxman. 2017. European Union regulations on algorithmic decision-making and a “right to explanation.” AI Magazine 38, 3 (2017), 50–57.

[79]

Mark Granovetter. 2018. Economic action and social structure: The problem of embeddedness. In The Sociology of Economic Life. Routledge, 22–45.

[80]

Claire Greene and Joanna Stavins. 2017. Did the target data breach change consumer assessments of payment card security? Journal of Payments Strategy & Systems 11, 2 (2017), 121–133.

[81]

David Gunning. 2017. Explainable Artificial Intelligence (XAI). Defense Advanced Research Projects Agency.

[82]

Dogan Gursoy, Oscar Hengxuan Chi, Lu Lu, and Robin Nunkoo. 2019. Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management 49 (2019), 157–169.

Digital Library

[83]

Thilo Hagendorff. 2020. The ethics of AI ethics: An evaluation of guidelines. Minds and Machines 30, 1 (2020), 99–120.

Digital Library

[84]

Tameru Hailesilassie. 2016. Rule extraction algorithm for deep neural networks: A review. arXiv preprint arXiv:1610.05267 (2016).

[85]

Meng Hao, Hongwei Li, Xizhao Luo, Guowen Xu, Haomiao Yang, and Sen Liu. 2019. Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Transactions on Industrial Informatics 16, 10 (2019), 6532–6542.

[86]

Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems. 3315–3323.

Digital Library

[87]

Stefan Haufe, Frank Meinecke, Kai Görgen, Sven Dähne, John-Dylan Haynes, Benjamin Blankertz, and Felix Bießmann. 2014. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87 (2014), 96–110.

[88]

Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015). https://arxiv.org/abs/1503.02531.

[89]

Fred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, and Kayur Patel. 2020. Understanding and visualizing data iteration in machine learning. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13.

Digital Library

[90]

Sarah Holland, Ahmed Hosny, and Sarah Newman. 2020. The dataset nutrition label. arXiv preprint arXiv:1805.03677[cs.DB] (2020).

[91]

Lingxiao Huang and Nisheeth Vishnoi. 2019. Stable and fair classification. In Proceedings of the International Conference on Machine Learning. 2879–2890.

[92]

ISO 24028:2020. 2020. Information Technology–Artificial Intelligence–Overview of Trustworthiness in Artificial Intelligence.Standard. International Organization for Standardization.

[93]

Alon Jacovi, Oren Sar Shalom, and Yoav Goldberg. 2018. Understanding convolutional neural networks for text classification. arXiv preprint arXiv:1809.08037 (2018). https://arxiv.org/abs/1809.08037.

[94]

Zihan Jiang, Wanling Gao, Lei Wang, Xingwang Xiong, Yuchen Zhang, Xu Wen, Chunjie Luo, et al. 2018. HPC AI500: A benchmark suite for HPC AI systems. In Proceedings of the International Symposium on Benchmarking, Measuring, and Optimization. 10–22.

[95]

Anna Jobin, Marcello Ienca, and Effy Vayena. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence 1, 9 (2019), 389–399.

[96]

Margot E. Kaminski and Gianclaudio Malgieri. 2020. Multi-layered explanations from algorithmic impact assessments in the GDPR. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 68–79.

Digital Library

[97]

Faisal Kamiran and Toon Calders. 2012. Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems 33, 1 (2012), 1–33.

Digital Library

[98]

Faisal Kamiran, Toon Calders, and Mykola Pechenizkiy. 2010. Discrimination aware decision tree learning. In Proceedings of the 2010 IEEE International Conference on Data Mining. IEEE, Los Alamitos, CA, 869–874.

Digital Library

[99]

Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2012. Fairness-aware classifier with prejudice remover regularizer. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 35–50.

[100]

Andreas Kaplan and Michael Haenlein. 2019. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons 62, 1 (2019), 15–25.

[101]

Davinder Kaur, Suleyman Uslu, and Arjan Durresi. 2019. Trust-based security mechanism for detecting clusters of fake users in social networks. In Proceedings of the Workshops of the International Conference on Advanced Information Networking and Applications. 641–650.

[102]

Davinder Kaur, Suleyman Uslu, and Arjan Durresi. 2020. Requirements for trustworthy artificial intelligence—A review. In Proceedings of the International Conference on Network-Based Information Systems. 105–115.

[103]

Davinder Kaur, Suleyman Uslu, Arjan Durresi, Sunil Badve, and Murat Dundar. 2021. Trustworthy explainability acceptance: A new metric to measure the trustworthiness of interpretable AI medical diagnostic systems. In Proceedings of the International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS’21).

[104]

Davinder Kaur, Suleyman Uslu, Arjan Durresi, George Mohler, and Jeremy G. Carter. 2020. Trust-based human-machine collaboration mechanism for predicting crimes. In Proceedings of the International Conference on Advanced Information Networking and Applications. 603–616.

[105]

Deanna Kemp and Frank Vanclay. 2013. Human rights and impact assessment: Clarifying the connections in practice. Impact Assessment and Project Appraisal 31, 2 (2013), 86–96.

[106]

Florian Kerschbaum. 2015. Frequency-hiding order-preserving encryption. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security. 656–667.

Digital Library

[107]

Mohammad Khalil and Martin Ebner. 2016. De-identification in learning analytics. Journal of Learning Analytics 3, 1 (2016), 129–138.

[108]

Been Kim, Rajiv Khanna, and Oluwasanmi O. Koyejo. 2016. Examples are not enough, learn to criticize! Criticism for interpretability. In Advances in Neural Information Processing Systems. 2280–2288.

Digital Library

[109]

Been Kim, Cynthia Rudin, and Julie A. Shah. 2014. The Bayesian case model: A generative approach for case-based reasoning and prototype classification. In Advances in Neural Information Processing Systems. 1952–1960.

Digital Library

[110]

Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2018. Interpretability beyond feature attribution: Quantitative Testing with Concept Activation Vectors (TCAV). In Proceedings of the International Conference on Machine Learning. 2668–2677.

[111]

Pauline T. Kim. 2017. Auditing algorithms for discrimination. University of Pennsylvania Law Review Online 166 (2017), 189.

[112]

Rainer Knauf, Avelino J. Gonzalez, and Thomas Abel. 2002. A framework for validation of rule-based systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 32, 3 (2002), 281–295.

Digital Library

[113]

Rafal Kocielnik, Saleema Amershi, and Paul N. Bennett. 2019. Will you accept an imperfect AI? Exploring designs for adjusting end-user expectations of ai systems. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–14.

Digital Library

[114]

Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. In Proceedings of the International Conference on Machine Learning. 1885–1894.

Digital Library

[115]

Puneet Kohli and Anjali Chadha. 2019. Enabling pedestrian safety using computer vision techniques: A case study of the 2018 Uber Inc. self-driving car crash. In Proceedings of the Future of Information and Communication Conference. 261–279.

[116]

Joshua A. Kroll, Solon Barocas, Edward W. Felten, Joel R. Reidenberg, David G. Robinson, and Harlan Yu. 2016. Accountable algorithms. University of Pennsylvania Law Review 165 (2016), 633.

[117]

Abhishek Kumar, Tristan Braud, Sasu Tarkoma, and Pan Hui. 2020. Trustworthy AI in the age of pervasive computing and big data. In Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops’20). IEEE, Los Alamitos, CA, 1–6.

[118]

Matt J. Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017. Counterfactual fairness. In Advances in Neural Information Processing Systems. 4066–4076.

Digital Library

[119]

Ryan C. LaBrie and Gerhard Steinke. 2019. Towards a framework for ethical audits of AI algorithms. In Proceedings of the Conference on Data Science and Analytics for Decision Support.

[120]

Himabindu Lakkaraju, Stephen H. Bach, and Jure Leskovec. 2016. Interpretable decision sets: A joint framework for description and prediction. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1675–1684.

Digital Library

[121]

Taesung Lee, Ian M. Molloy, and Dong Su. 2019. Protecting cognitive systems from model stealing attacks. US Patent App. 15/714,514.

[122]

Shane Legg and Marcus Hutter. 2007. A collection of definitions of intelligence. Frontiers in Artificial Intelligence and Applications 157 (2007), 17.

[123]

Bruno Lepri, Nuria Oliver, Emmanuel Letouzé, Alex Pentland, and Patrick Vinck. 2018. Fair, transparent, and accountable algorithmic decision-making processes. Philosophy & Technology 31, 4 (2018), 611–627.

[124]

Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian. 2007. t-Closeness: Privacy beyond k-anonymity and L-diversity. In Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering. IEEE, Los Alamitos, CA, 106–115.

[125]

Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 3 (2020), 50–60.

[126]

Mikael Lindvall, Dharmalingam Ganesan, Ragnar Árdal, and Robert E. Wiegand. 2015. Metamorphic model-based testing applied on NASA DAT—An experience report. In Proceedings of the 2015 IEEE/ACM 37th International Conference on Software Engineering, Vol. 2. IEEE, Los Alamitos, CA, 129–138.

Digital Library

[127]

Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems. 4765–4774.

Digital Library

[128]

Binh Thanh Luong, Salvatore Ruggieri, and Franco Turini. 2011. k-NN as an implementation of situation testing for discrimination discovery and prevention. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 502–510.

Digital Library

[129]

Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam. 2007. l-Diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data 1, 1 (2007), 3–es.

Digital Library

[130]

Gary Marcus and Ernest Davis. 2019. Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage.

Digital Library

[131]

Bernard Marr. 2018. Is artificial intelligence dangerous? 6 AI risks everyone should know about. Forbes (2018).

[132]

Kirsten Martin. 2019. Ethical implications and accountability of algorithms. Journal of Business Ethics 160, 4 (2019), 835–850.

[133]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics. PMLR, 1273–1282.

[134]

Dan McQuillan. 2018. People’s councils for ethical machine learning. Social Media+ Society 4, 2 (2018), 2056305118768303.

[135]

Ninareh Mehrabi, Fred Morstatter, Nanyun Peng, and Aram Galstyan. 2019. Debiasing community detection: The importance of lowly connected nodes. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’19). IEEE, Los Alamitos, CA, 509–512.

Digital Library

[136]

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021. A survey on bias and fairness in machine learning. ACM Computing Surveys 54, 6 (2021), 1–35.

Digital Library

[137]

Aditya Krishna Menon and Robert C. Williamson. 2018. The cost of fairness in binary classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency. 107–118.

[138]

Jacob Metcalf, Emanuel Moss, Elizabeth Anne Watkins, Ranjit Singh, and Madeleine Clare Elish. 2021. Algorithmic impact assessments and accountability: The co-construction of impacts. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 735–746.

Digital Library

[139]

Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267 (2019), 1–38.

[140]

Ramaravind K. Mothilal, Amit Sharma, and Chenhao Tan. 2020. Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 607–617.

Digital Library

[141]

Jethro Mullen. 2015. Google Rushes to Fix Software That Served Up Racial Slur. Retrieved November 2, 2021 from https://www.cnn.com/2015/07/02/tech/google-image-recognition-gorillas-tag/.

[142]

Patrick M. Murphy and Michael J. Pazzani. 1991. ID2-of-3: Constructive induction of M-of-N concepts for discriminators in decision trees. In Machine Learning Proceedings 1991. Elsevier, 183–187.

Digital Library

[143]

Raghunath Nambiar. 2018. Towards an industry standard for benchmarking artificial intelligence systems. In Proceedings of the 2018 IEEE 34th International Conference on Data Engineering (ICDE’18). IEEE, Los Alamitos, CA, 1679–1680.

[144]

Daniel Neyland. 2016. Bearing account-able witness to the ethical algorithmic system. Science, Technology, & Human Values 41, 1 (2016), 50–76.

[145]

Mei Ngan, Patrick J. Grother, and Mei Ngan. 2015. Face Recognition Vendor Test (FRVT) Performance of Automated Gender Classification Algorithms. U.S. Department of Commerce, National Institute of Standards and Technology.

[146]

Claire Nicodeme. 2020. Build confidence and acceptance of AI-based decision support systems—Explainable and liable AI. In Proceedings of the 2020 13th International Conference on Human System Interaction (HSI’20). IEEE, Los Alamitos, CA, 20–23.

[147]

Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447–453.

[148]

National Institute of Standards and Technology. 2021. NIST Proposes Method for Evaluating User Trust in Artificial Intelligence Systems. Retrieved November 2, 2021 from https://www.nist.gov/news-events/news/2021/05/nist-proposes-method-evaluating-user-trust-artificial-intelligence-systems.

[149]

U.S. Government Accountability Office. 2021. Artificial Intelligence: An Accountability Framework for Federal Agencies and Other Entities. Retrieved November 2, 2021 from https://www.gao.gov/products/gao-21-519sp.

[150]

Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kiciman. 2019. Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data 2 (2019), 13.

[151]

Amy L. Ostrom, Darima Fotheringham, and Mary Jo Bitner. 2019. Customer acceptance of AI in service encounters: Understanding antecedents and consequences. In Handbook of Service Science, Volume II. Springer, 77–103.

[152]

David J. Pauleen, David Rooney, and Ali Intezari. 2017. Big data, little wisdom: Trouble brewing? Ethical implications for the information systems discipline. Social Epistemology 31, 4 (2017), 400–416.

[153]

Petra Perner. 2011. How to interpret decision trees? In Proceedings of the Industrial Conference on Data Mining. 40–55.

Digital Library

[154]

Sundar Pichai. 2018. AI at Google: Our principles. The Keyword, June 7, 2018.

[155]

Stefan Poier. 2020. Clean and green—The volkswagen emissions scandal: Failure of corporate governance? Problemy Ekorozwoju 15, 2 (2020) 33–39.

[156]

Novi Quadrianto and Viktoriia Sharmanska. 2017. Recycling privileged learning and distribution matching for fairness. In Advances in Neural Information Processing Systems. 677–688.

Digital Library

[157]

Iyad Rahwan. 2018. Society-in-the-loop: Programming the algorithmic social contract. Ethics and Information Technology 20, 1 (2018), 5–14.

Digital Library

[158]

Eeva Raita and Antti Oulasvirta. 2011. Too good to be bad: Favorable product expectations boost subjective usability ratings. Interacting with Computers 23, 4 (2011), 363–371.

[159]

Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020. Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 33–44.

Digital Library

[160]

Chris Reed. 2018. How should we regulate artificial intelligence? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, 2128 (2018), 20170360.

[161]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135–1144.

Digital Library

[162]

Mireia Ribera and Agata Lapedriza. 2019. Can we do better explanations? A proposal of user-centered explainable AI. In Proceedings of the IUI Workshops.

[163]

Stuart Ritchie. 2017. Privacy impact assessment System and associated methods. US Patent App. 15/459,909.

[164]

Luc Rocher, Julien M. Hendrickx, and Yves-Alexandre De Montjoye. 2019. Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications 10, 1 (2019), 1–9.

[165]

Drew Roselli, Jeanna Matthews, and Nisha Talagala. 2019. Managing bias in AI. In Companion Proceedings of the 2019 World Wide Web Conference. 539–544.

Digital Library

[166]

Matthew Rosenquist. 2020. There Is No Easy Fix to AI Privacy Problems. Retrieved November 2, 2021 from https://www.helpnetsecurity.com/2020/01/23/ai-privacy-problems/.

[167]

Julian B. Rotter. 1967. A new scale for the measurement of interpersonal trust. Journal of Personality 35, 4 (1967), 651–665.

[168]

Yefeng Ruan, Ping Zhang, Lina Alfantoukh, and Arjan Durresi. 2017. Measurement theory-based trust management framework for online social communities. ACM Transactions on Internet Technology 17, 2 (2017), 1–24.

Digital Library

[169]

Salvatore Ruggieri. 2014. Using t-closeness anonymity to control for non-discrimination. Transactions on Data Privacy 7, 2 (2014), 99–129.

Digital Library

[170]

Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, and Hervé Jégou. 2020. Radioactive data: Tracing through training. In Proceedings of the International Conference on Machine Learning. 8326–8335.

[171]

Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T. Rodolfa, and Rayid Ghani. 2018. Aequitas: A bias and fairness audit toolkit. arXiv preprint arXiv:1811.05577 (2018). https://arxiv.org/abs/1811.05577.

[172]

Samira Samadi, Uthaipon Tantipongpipat, Jamie H. Morgenstern, Mohit Singh, and Santosh Vempala. 2018. The price of fair PCA: One extra dimension. In Advances in Neural Information Processing Systems. 10976–10987.

Digital Library

[173]

Wojciech Samek and Klaus-Robert Müller. 2019. Towards explainable artificial intelligence. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. Springer, 5–22.

Digital Library

[174]

Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. 2014. Auditing algorithms: Research methods for detecting discrimination on Internet platforms. Data and Discrimination: Converting Critical Concerns into Productive Inquiry 22 (2014), 1–23.

[175]

Lindsay Sanneman and Julie A. Shah. 2020. A situation awareness-based framework for design and evaluation of explainable AI. In Proceedings of the International Workshop on Explainable, Transparent, Autonomous Agents and Multi-Agent Systems. 94–110.

Digital Library

[176]

Fernando P. Santos, Francisco C. Santos, Ana Paiva, and Jorge M. Pacheco. 2015. Evolutionary dynamics of group fairness. Journal of Theoretical Biology 378 (2015), 96–102.

[177]

Nripsuta Ani Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, David C. Parkes, and Yang Liu. 2019. How do fairness definitions fare? Examining public attitudes towards algorithmic definitions of fairness. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. 99–106.

Digital Library

[178]

Sergio Segura, Dave Towey, Zhi Quan Zhou, and Tsong Yueh Chen. 2018. Metamorphic testing: Testing the untestable. IEEE Software 37, 3 (2018), 46–53.

[179]

Lei Shi, Furu Wei, Shixia Liu, Li Tan, Xiaoxiao Lian, and Michelle X. Zhou. 2010. Understanding text corpora with multiple facets. In Proceedings of the 2010 IEEE Symposium on Visual Analytics Science and Technology. IEEE, Los Alamitos, CA, 99–106.

[180]

Donghee Shin. 2021. The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies 146 (2021), 102551.

[181]

Donghee Shin and Yong Jin Park. 2019. Role of fairness, accountability, and transparency in algorithmic affordance. Computers in Human Behavior 98 (2019), 277–284.

Digital Library

[182]

Daniel Smilkov, Nikhil Thorat, Charles Nicholson, Emily Reif, Fernanda B. Viégas, and Martin Wattenberg. 2016. Embedding projector: Interactive visualization and interpretation of embeddings. arXiv preprint arXiv:1611.05469 (2016). https://arxiv.org/abs/1611.05469.

[183]

Nathalie A. Smuha. 2019. The eu approach to ethics guidelines for trustworthy artificial intelligence. Computer Law Review International 20, 4 (2019), 97–106.

[184]

Kwonsang Sohn and Ohbyung Kwon. 2020. Technology acceptance theories and factors influencing artificial intelligence-based intelligent products. Telematics and Informatics 47 (2020), 101324.

Digital Library

[185]

Richard S. Sojda. 2007. Empirical evaluation of decision support systems: Needs, definitions, potential methods, and an example pertaining to waterfowl management. Environmental Modelling & Software 22, 2 (2007), 269–277.

[186]

Kacper Sokol and Peter Flach. 2020. Explainability fact sheets: A framework for systematic assessment of explainable approaches. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 56–67.

Digital Library

[187]

Biplav Srivastava and Francesca Rossi. 2018. Towards composable bias rating of AI services. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. 284–289.

Digital Library

[188]

Bernd Carsten Stahl and David Wright. 2018. Ethics and privacy in AI and big data: Implementing responsible research and innovation. IEEE Security & Privacy 16, 3 (2018), 26–33.

[189]

Sophie Stalla-Bourdillon and Alison Knight. 2016. Anonymous data v. personal data-false debate: An EU perspective on anonymization, pseudonymization and personal data. Wisconsin International Law Journal 34 (2016), 284.

[190]

Du Su, Hieu Tri Huynh, Ziao Chen, Yi Lu, and Wenmiao Lu. 2020. Re-identification attack to privacy-preserving data analysis with noisy sample-mean. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1045–1053.

Digital Library

[191]

Latanya Sweeney. 2002. k-Anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 5 (2002), 557–570.

Digital Library

[192]

Andreas Theodorou and Virginia Dignum. 2020. Towards ethical and socio-legal governance in AI. Nature Machine Intelligence 2, 1 (2020), 10–12.

[193]

Mike Thomas. 2019. 6 Dangerous Risks of Artificial Intelligence. Retrieved November 2, 2021 from https://builtin.com/artificial-intelligence/risks-of-artificial-intelligence.

[194]

Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou. 2019. A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security. 1–11.

Digital Library

[195]

Zeynep Tufekci. 2014. Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 8.

[196]

Ryan Turner. 2016. A model explanation system. In Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP’16). IEEE, Los Alamitos, CA, 1–6.

[197]

Andrew Tutt. 2017. An FDA for algorithms. Administrative Law Review 69 (2017), 83.

[198]

UNI Global Union. 2017. Top 10 Principles for Ethical Artificial Intelligence. UNI Global Union, Nyon, Switzerland.

[199]

Suleyman Uslu, Davinder Kaur, Samuel J. Rivera, Arjan Durresi, and Meghna Babbar-Sebens. 2019. Decision support system using trust planning among food-energy-water actors. In Proceedings of the International Conference on Advanced Information Networking and Applications. 1169–1180.

[200]

Suleyman Uslu, Davinder Kaur, Samuel J. Rivera, Arjan Durresi, and Meghna Babbar-Sebens. 2019. Trust-based game-theoretical decision making for food-energy-water management. In Proceedings of the International Conference on Broadband and Wireless Computing, Communication, and Applications. 125–136.

[201]

Suleyman Uslu, Davinder Kaur, Samuel J. Rivera, Arjan Durresi, and Meghna Babbar-Sebens. 2020. Trust-based decision making for food-energy-water actors. In Proceedings of the International Conference on Advanced Information Networking and Applications. 591–602.

[202]

Suleyman Uslu, Davinder Kaur, Samuel J. Rivera, Arjan Durresi, Meghna Babbar-Sebens, and Jenna H. Tilt. 2020. Control theoretical modeling of trust-based decision making in food-energy-water management. In Proceedings of the Conference on Complex, Intelligent, and Software Intensive Systems. 97–107.

[203]

Suleyman Uslu, Davinder Kaur, Samuel J. Rivera, Arjan Durresi, Mimoza Durresi, and Meghna Babbar-Sebens. 2021. Trustworthy acceptance: A new metric for trustworthy artificial intelligence used in decision making in food-energy-water sectors. In Proceedings of the 35th International Conference on Advanced Information Networking and Applications (AINA’21).208–219.

[204]

Viswanath Venkatesh, James Y. L. Thong, and Xin Xu. 2012. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 36, 1 (2012), 157–178.

Digital Library

[205]

Sahil Verma and Julia Rubin. 2018. Fairness definitions explained. In Proceedings of the 2018 IEEE/ACM International Workshop on Software Fairness (FairWare’18). IEEE, Los Alamitos, CA, 1–7.

Digital Library

[206]

Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2017. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law and Technology 31 (2017), 841.

[207]

Sandra Wachter, Brent Mittelstadt, and Chris Russell. 2020. Why fairness cannot be automated: Bridging the gap between EU non-discrimination law and AI. Available at SSRN (2020).

[208]

Yu-Yin Wang, Yi-Shun Wang, and Tung-Ching Lin. 2018. Developing and validating a technology upgrade model. International Journal of Information Management 38, 1 (2018), 7–26.

Digital Library

[209]

Soeren H. Welling, Hanne H. F. Refsgaard, Per B. Brockhoff, and Line H. Clemmensen. 2016. Forest floor visualizations of random forests. arXiv preprint arXiv:1605.09196 (2016). https://arxiv.org/abs/1605.09196

[210]

Elaine J. Weyuker. 1982. On testing non-testable programs. Computer Journal 25, 4 (1982), 465–470.

[211]

Maranke Wieringa. 2020. What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 1–18.

Digital Library

[212]

Oliver E. Williamson. 1993. Calculativeness, trust, and economic organization. Journal of Law and Economics 36, 1, Part 2 (1993), 453–486.

[213]

H. James Wilson and Paul R. Daugherty. 2018. Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review 96, 4 (2018), 114–123.

[214]

David Wright. 2011. A framework for the ethical impact assessment of information technology. Ethics and Information Technology 13, 3 (2011), 199–226.

Digital Library

[215]

David Wright, Michael Friedewald, and Raphaël Gellert. 2015. Developing and testing a surveillance impact assessment methodology. International Data Privacy Law 5, 1 (2015), 40–53.

[216]

Nicholas D. Writer, Shazeda Ahmed, Natasha E. Bajema, Samuel Bendett, Benjamin A. Chang, Rogier Creemers, Chris C. Demchak, et al. 2019. Artificial Intelligence, China, Russia, and the Global Order Technological, Political, Global, and Creative Perspectives. Technical Report. Air University Press, Maxwell AFB.

[217]

Xiaoyuan Xie, Joshua W. K. Ho, Christian Murphy, Gail Kaiser, Baowen Xu, and Tsong Yueh Chen. 2011. Testing and validating machine learning classifiers by metamorphic testing. Journal of Systems and Software 84, 4 (2011), 544–558.

Digital Library

[218]

Kai Xu, Dae Hoon Park, Chang Yi, and Charles Sutton. 2018. Interpreting deep classifier by visual distillation of dark knowledge. arXiv preprint arXiv:1803.04042 (2018).

[219]

Chengliang Yang, Anand Rangarajan, and Sanjay Ranka. 2018. Global model interpretation via recursive partitioning. In Proceedings of the 2018 IEEE 20th International Conference on High Performance Computing and Communications, the IEEE 16th International Conference on Smart City, and the IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS’18). IEEE, Los Alamitos, CA, 1563–1570.

[220]

Karen Yeung. 2017. ‘Hypernudge’: Big data as a mode of regulation by design. Information, Communication & Society 20, 1 (2017), 118–136.

[221]

Heung Youl Youm. 2020. An overview of de-identification techniques and their standardization directions. IEICE Transactions on Information and Systems 103, 7 (2020), 1448–1461.

[222]

Han Yu, Zhiqi Shen, Chunyan Miao, Cyril Leung, Victor R. Lesser, and Qiang Yang. 2018. Building ethics into artificial intelligence. arXiv preprint arXiv:1812.02953 (2018). https://arxiv.org/abs/1812.02953.

Digital Library

[223]

Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P. Gummadi. 2017. Fairness constraints: Mechanisms for fair classification. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 962–970.

[224]

Yi Zeng, Enmeng Lu, and Cunqing Huangfu. 2018. Linking artificial intelligence principles. arXiv preprint arXiv:1812.04814 (2018). https://arxiv.org/abs/1812.04814.

[225]

Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell. 2018. Mitigating unwanted biases with adversarial learning. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. 335–340.

Digital Library

[226]

Yunfeng Zhang, Rachel Bellamy, and Kush Varshney. 2020. Joint optimization of AI fairness and utility: A human-centered approach. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 400–406.

Digital Library

[227]

Guannan Zhao, Bo Zhou, Kaiwen Wang, Rui Jiang, and Min Xu. 2018. Respond-CAM: Analyzing deep models for 3D imaging data by visualizations. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. 485–492.

Digital Library

[228]

Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba. 2018. Interpretable basis decomposition for visual explanation. In Proceedings of the European Conference on Computer Vision (ECCV’18). 119–134.

Cited By

Giesselbach SWegener DHelmer LMartens CRüping S(2025)Addressing a New Paradigm Shift in Data Science: An Empirical Study on Novel Project Characteristics for Foundation Model ProjectsIEEE Software10.1109/MS.2024.343502442:1(84-92)Online publication date: Jan-2025
https://doi.org/10.1109/MS.2024.3435024
Gursel EMadadi MCoble JAgarwal VYadav VBoring RKhojandi A(2025)The role of AI in detecting and mitigating human errors in safety-critical industries: A reviewReliability Engineering & System Safety10.1016/j.ress.2024.110682256(110682)Online publication date: Apr-2025
https://doi.org/10.1016/j.ress.2024.110682
Zhang GYuan GCheng DLiu LLi JZhang S(2025)Disentangled contrastive learning for fair graph representationsNeural Networks10.1016/j.neunet.2024.106781181(106781)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106781
Show More Cited By

Index Terms

Trustworthy Artificial Intelligence: A Review
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
  2. Machine learning
    1. Cross-validation

Recommendations

Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust
CSCW

Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency, and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI to exaggerate its competence under the ...
Toward Trustworthy Artificial Intelligence (TAI) in the Context of Explainability and Robustness
From the innovation, Artificial Intelligence (AI) materialized as one of the noticeable research areas in various technologies and has almost expanded into every aspect of modern human life. However, nowadays, the development of AI is unpredictable with ...
Trustworthy AI: A Computational Perspective
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone’s daily life and profoundly altering the course of human society. The intention behind developing AI was and is to benefit humans by ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 2

February 2023

803 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3505209

Editor:
Albert Zomaya
University of Sydney, Australia

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 January 2022

Accepted: 01 October 2021

Revised: 01 September 2021

Received: 01 December 2020

Published in CSUR Volume 55, Issue 2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Survey
Refereed

Funding Sources

National Science Foundation (NSF)
U.S. Department of Agriculture (USDA)
National Institute of Food and Agriculture (NIFA)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

243
Total Citations
View Citations
23,841
Total Downloads

Downloads (Last 12 months)9,434
Downloads (Last 6 weeks)1,181

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Giesselbach SWegener DHelmer LMartens CRüping S(2025)Addressing a New Paradigm Shift in Data Science: An Empirical Study on Novel Project Characteristics for Foundation Model ProjectsIEEE Software10.1109/MS.2024.343502442:1(84-92)Online publication date: Jan-2025
https://doi.org/10.1109/MS.2024.3435024
Gursel EMadadi MCoble JAgarwal VYadav VBoring RKhojandi A(2025)The role of AI in detecting and mitigating human errors in safety-critical industries: A reviewReliability Engineering & System Safety10.1016/j.ress.2024.110682256(110682)Online publication date: Apr-2025
https://doi.org/10.1016/j.ress.2024.110682
Zhang GYuan GCheng DLiu LLi JZhang S(2025)Disentangled contrastive learning for fair graph representationsNeural Networks10.1016/j.neunet.2024.106781181(106781)Online publication date: Jan-2025
https://doi.org/10.1016/j.neunet.2024.106781
Affandi SIshaq MRaza ATalpur QAhmad R(2025)AI assistant is my new best friend! Role of emotional disclosure, performance expectations and intention to reuseJournal of Retailing and Consumer Services10.1016/j.jretconser.2024.10408782(104087)Online publication date: Jan-2025
https://doi.org/10.1016/j.jretconser.2024.104087
Kabir MGulandaz MAli MReza MKabir MChung SHan K(2024)Yield monitoring systems for non-grain crops: A reviewKorean Journal of Agricultural Science10.7744/kjoas.51010651:1(63-77)Online publication date: 1-Mar-2024
https://doi.org/10.7744/kjoas.510106
Erdem Aladağ M(2024)KURUMSAL YÖNETİŞİM VE YAPAY ZEKA: POTANSİYEL FIRSATLAR VE ZORLUKLARDenetişim10.58348/denetisim.1541327(18-32)Online publication date: 1-Dec-2024
https://doi.org/10.58348/denetisim.1541327
Dantas USoliani RCarvalho SFerreira Júnior G(2024)Scientific knowledge management in Brazil: an analysis of the Lattes PlatformCuadernos de Educación y Desarrollo10.55905/cuadv16n11-02516:11(e6249)Online publication date: 5-Nov-2024
https://doi.org/10.55905/cuadv16n11-025
Gyevnar BWang CLucas CCohen SAlbrecht SDastani MSichman JAlechina NDignum V(2024)Causal Explanations for Sequential Decision-Making in Multi-Agent SystemsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662930(771-779)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662930
Sultana S(2024)AI in Higher EducationOptimizing Leadership and Governance in Higher Education10.4018/979-8-3693-3534-5.ch006(111-136)Online publication date: 1-Nov-2024
https://doi.org/10.4018/979-8-3693-3534-5.ch006
Venket KAmbika AFreeda A(2024)Integrating ChatGPT and AI Technologies in Engineering FieldsReal-Time Data Decisions With AI and ChatGPT Techniques10.4018/979-8-3693-2284-0.ch002(23-46)Online publication date: 31-May-2024
https://doi.org/10.4018/979-8-3693-2284-0.ch002
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents