Show source-provided references
1. Binu, D., and Rajakumar, B.R. (2021). Artificial Intelligence in Data Mining: Theories and Applications, Academic Press.
2. Ahmadi, A., Meybodi, M.R., and Saghiri, A.M. (2016, January 9). Adaptive search in unstructured peer-to-peer networks based on ant colony and Learning Automata. Proceedings of the 2016 Artificial Intelligence and Robotics, Qazvin, Iran.
10.1109/RIOS.2016.7529503 ↗3. Cheng, X., Lin, X., Shen, X.-L., Zarifis, A., and Mou, J. (2022). The dark sides of AI. Electron. Mark., 1–5.
10.1007/s12525-022-00531-5 ↗4. Jabbarpour, M.R., Saghiri, A.M., and Sookhak, M. (2021). A framework for component selection considering dark sides of artificial intelligence: A case study on autonomous vehicle. Electronics, 10.
10.3390/electronics10040384 ↗5. Scary dark side of artificial intelligence: A perilous contrivance to mankind
6. Mahmoud, A.B., Tehseen, S., and Fuxman, L. (2020). The dark side of artificial intelligence in retail innovation. Retail Futures, Emerald Publishing Limited.
10.1108/978-1-83867-663-620201019 ↗8. Hanif, M.A., Khalid, F., Putra, R.V.W., Rehman, S., and Shafique, M. (2018, January 2–4). Robust machine learning systems: Reliability and security for deep neural networks. Proceedings of the 2018 IEEE 24th International Symposium on On-Line Testing and Robust System Design (IOLTS), Platja d’Aro, Spain.
10.1109/IOLTS.2018.8474192 ↗9. Varshney, K.R. (February, January 31). Engineering safety in machine learning. Proceedings of the 2016 Information Theory and Applications Workshop (ITA), La Jolla, CA, USA.
10.1109/ITA.2016.7888195 ↗11. Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. arXiv.
10.18653/v1/P19-1355 ↗13. A collection of definitions of intelligence
14. Legg, S. (2008). Machine Super Intelligence. [Ph.D. Thesis, University of Lugano].
15. Saghiri, A.M. (2020, January 22–23). A Survey on Challenges in Designing Cognitive Engines. Proceedings of the 2020 6th International Conference on Web Research (ICWR), Tehran, Iran.
10.1109/ICWR49608.2020.9122273 ↗16. Boström, N. (2014). Superintelligence: Paths, Dangers, Strategies, Oxford University Press.
17. Chollet, F. (2019). On the measure of intelligence. arXiv.
18. Yampolskiy, R.V. (2020). Human is not equal to AGI. arXiv.
20. Russell, S.J., and Norvig, P. (1994). Artificial Intelligence: A Modern Approach, Prentice Hall. [3rd ed.].
21. Linz, P. (2006). An Introduction to Formal Languages and Automata, Jones & Bartlett Learning.
23. Sutton, R.S., and Barto, A.G. (1998). Reinforcement Learning: An Introduction, Cambridge University Press.
10.1109/TNN.1998.712192 ↗28. Big Data Tools: Advantages and Disadvantages
32. Bhagoji, A.N., Cullina, D., Sitawarin, C., and Mittal, P. (2018, January 21–23). Enhancing robustness of machine learning systems via data transformations. Proceedings of the 2018 52nd Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA.
10.1109/CISS.2018.8362326 ↗33. Rozsa, A., Günther, M., and Boult, T.E. (2016, January 18–20). Are accuracy and robustness correlated. Proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA.
10.1109/ICMLA.2016.0045 ↗34. Pérez-Rosas, V., Abouelenien, M., Mihalcea, R., and Burzo, M. (2015, January 9–13). Deception detection using real-life trial data. Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, Seattle, WA, USA.
10.1145/2818346.2820758 ↗35. Krishnamurthy, G., Majumder, N., Poria, S., and Cambria, E. (2018). A deep learning approach for multimodal deception detection. arXiv.
36. Randhavane, T., Bhattacharya, U., Kapsaskis, K., Gray, K., Bera, A., and Manocha, D. (2019). The Liar’s Walk: Detecting Deception with Gait and Gesture. arXiv.
37. Zhao, S., Jiang, G., Huang, T., and Yang, X. (2005, January 14–16). The deception detection and restraint in multi-agent system. Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05), Hong Kong, China.
38. Zlotkin, G., and Rosenschein, J.S. (1991, January 24–30). Incomplete Information and Deception in Multi-Agent Negotiation. Proceedings of the IJCAI, Sydney, Australia.
39. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8–13). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.
40. Lies, Line Drawing, and Deep Fake News
43. Kinsner, W. (2012, January 22–24). Towards cognitive security systems. Proceedings of the 11th International Conference on Cognitive Informatics and Cognitive Computing, Kyoto, Japan.
10.1109/ICCI-CC.2012.6311207 ↗45. Barreno, M., Nelson, B., Sears, R., Joseph, A.D., and Tygar, J.D. (2006, January 21–24). Can machine learning be secure?. Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security, Taipei, Taiwan.
10.1145/1128817.1128824 ↗47. Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., and Tygar, J. (2011, January 21). Adversarial machine learning. Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, Chicago, IL, USA.
10.1145/2046684.2046692 ↗48. Ateniese, G., Felici, G., Mancini, L.V., Spognardi, A., Villani, A., and Vitali, D. (2013). Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers. arXiv.
49. Tucker, C., Agrawal, A., Gans, J., and Goldfarb, A. (2018). Privacy, algorithms, and artificial intelligence. The Economics of Artificial Intelligence: An Agenda, Oxford University Press.
10.7208/chicago/9780226613475.001.0001 ↗51. Zhang, W., and Ntoutsi, E. (2019). Faht: An adaptive fairness-aware decision tree classifier. arXiv.
10.24963/ijcai.2019/205 ↗52. Kamani, M.M., Haddadpour, F., Forsati, R., and Mahdavi, M. (2022). Efficient fair principal component analysis. Machine Learning, Springer.
10.1007/s10994-021-06100-9 ↗53. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012, January 8–10). Fairness through awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, Cambridge, MA, USA.
10.1145/2090236.2090255 ↗54. Kamiran, F., and Calders, T. (2009, January 17–18). Classifying without discriminating. Proceedings of the 2009 2nd International Conference on Computer, Control and Communication, Karachi, Pakistan.
10.1109/IC4.2009.4909197 ↗55. Calders, T., Kamiran, F., and Pechenizkiy, M. (2009, January 6). Building classifiers with independency constraints. Proceedings of the 2009 IEEE International Conference on Data Mining Workshops, Miami, FL, USA.
10.1109/ICDMW.2009.83 ↗56. Quy, T.L., Roy, A., Iosifidis, V., and Ntoutsi, E. (2021). A survey on datasets for fairness-aware machine learning. arXiv.
57. Equality of opportunity in supervised learning
58. Kamishima, T., Akaho, S., and Sakuma, J. (2011, January 11). Fairness-aware learning through regularization approach. Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, Vancouver, BC, Canada.
10.1109/ICDMW.2011.83 ↗59. Satisfying real-world goals with dataset constraints
61. Empirical risk minimization under fairness constraints
62. Morgenstern, J., Samadi, S., Singh, M., Tantipongpipat, U., and Vempala, S. (2019). Fair dimensionality reduction and iterative rounding for sdps. arXiv.
63. The price of fair pca: One extra dimension
64. On fairness and calibration
65. Adadi, A., and Berrada, M. (2020). Explainable AI for healthcare: From black box to interpretable models. Embedded Systems and Artificial Intelligence, Springer.
10.1007/978-981-15-0947-6_31 ↗66. Gade, K., Geyik, S.C., Kenthapadi, K., Mithal, V., and Taly, A. (2019, January 4–8). Explainable AI in industry. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.
10.1145/3292500.3332281 ↗67. Došilović, F.K., Brčić, M., and Hlupić, N. (2018, January 21–25). Explainable artificial intelligence: A survey. Proceedings of the 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.
10.23919/MIPRO.2018.8400040 ↗68. Samek, W., and Müller, K.-R. (2019). Towards explainable artificial intelligence. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer.
10.1007/978-3-030-28954-6 ↗69. Sharma, S., Nag, A., Cordeiro, L., Ayoub, O., Tornatore, M., and Nekovee, M. (2020, January 1–4). Towards explainable artificial intelligence for network function virtualization. Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies, Barcelona, Spain.
10.1145/3386367.3431673 ↗71. Neri, E., Coppola, F., Miele, V., Bibbolino, C., and Grassi, R. (2020). Artificial Intelligence: Who Is Responsible for the Diagnosis?, Springer.
10.1007/s11547-020-01135-9 ↗74. Yampolskiy, R.V. (2020). On Controllability of AI. arXiv.
75. Russell, S. (2019). Human Compatible: Artificial Intelligence and the Problem of Control, Penguin.
76. Yampolskiy, R. (2020). On Controllability of Artificial Intelligence, University of Louisville. Technical Report.
78. Yampolskiy, R.V. (2019). Unpredictability of AI. arXiv.
79. Hofstadter, D.R. (2007). I Am a Strange Loop, Basic Books.
80. Musiolik, G. (2021). Predictability of AI Decisions. Analyzing Future Applications of AI, Sensors, and Robotics in Society, IGI Global.
10.4018/978-1-7998-3499-1 ↗81. Delange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T. (2021). A continual learning survey: Defying forgetting in classification tasks. IEEE Trans. Pattern Anal. Mach. Intell.
10.1109/TPAMI.2021.3057446 ↗82. Shin, H., Lee, J.K., Kim, J., and Kim, J. (2017, January 4–9). Continual learning with deep generative replay. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA.
83. Artificial intelligence (AI) or intelligence augmentation (IA): What is the future?
10.3390/ai1020008 ↗85. Kumar, A., Boehm, M., and Yang, J. (2017, January 14–19). Data management in machine learning: Challenges, techniques, and systems. Proceedings of the 2017 ACM International Conference on Management of Data, Chicago, IL, USA.
10.1145/3035918.3054775 ↗87. The semantic web
92. Teslya, N., and Smirnov, A. (2018, January 4–5). Blockchain-based framework for ontology-oriented robots’ coalition formation in cyberphysical systems. Proceedings of the MATEC Web of Conferences, Anyer, Indonesia.
10.1051/matecconf/201816103018 ↗94. Abdel-Fattah, A.M., Besold, T.R., Gust, H., Krumnack, U., Schmidt, M., Kuhnberger, K.-U., and Wang, P. (2012, January 1–4). Rationality-guided AGI as cognitive systems. Proceedings of the Annual Meeting of the Cognitive Science Society, Sapporo, Japan.
99. Rabinowitz, N., Perbet, F., Song, F., Zhang, C., Eslami, S.A., and Botvinick, M. (2018, January 10–15). Machine theory of mind. Proceedings of the International Conference on Machine Learning, Vienna, Austria.