Artificial Intelligence
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Resources
Ahlberg, S. (2018). N. Katherine Hayles, Unthought: The power of the cognitive nonconscious. Chicago and London: University of Chicago Press, 2017. 250 pages. ISBN-13: 978-0-226-44774-2 (cloth); 978-0-226-44788-9 (paper); 978-0-226-44791-9 (e-book). Studia Neophilologica, 90(2), 273-274. https://doi-org.ezp-prod1.hul.harvard.edu/10.1080/00393274.2018.1460222
Ahmadi, M., Borcea, C., & Jones, Q. (2019, March). Collaborative lifelogging through the integration of machine and human computation. In Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion (pp. 23-24).
Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & SOCIETY, 1-13.
Arntz, M., Gregory, T., & Zierahn, U. (2017). Revisiting the risk of automation. Economics Letters, 159, 157-160.
Baker, M. J. (2000). The roles of models in Artificial Intelligence and Education research: A prospective view. Journal of Artificial Intelligence and Education, 11, 122-143.
Bakkar, N., Kovalik, T., Lorenzini, I., Spangler, S., Lacoste, A., Sponaugle, K., ... & Bowser, R. (2018). Artificial intelligence in neurodegenerative disease research: Use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathologica, 135(2), 227-247.
Bala, B. M. (2019). Artificial intelligence and its implications for future. Artificial Intelligence.
Bellamy, R. K., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., ... & Ramamurthy, K. N. (2019). Think your artificial intelligence software is fair? Think again. IEEE Software, 36(4), 76-80.
Bodily, R., Kay, J., Aleven, V., Jivet, I., Davis, D., Xhakaj, F., & Verbert, K. (2018, March). Open learner models and learning analytics dashboards: A systematic review. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 41-50). ACM.
Bundy, A. (2017). Preparing for the future of Artificial Intelligence. Edinburgh Research Explorer.The University of Edinburgh.
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019). Artificial intelligence, bias and clinical safety . BMJ Qual Saf, 28(3), 231-237.
Chui, M. (2017). Artificial intelligence the next digital frontier?. McKinsey and Company Global Institute, 47, 3-6.
Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (No. w24449). National bureau of economic research.
Conitzer, V. (2019). Designing preferences, beliefs, and identities for artificial intelligence. Durham, NC: Duke University. Association For the Advancement of Artificial Intelligence.
Conitzer, V., Sinnott-Armstrong, W., Borg, J. S., Deng, Y., & Kramer, M. (2017, February). Moral decision making frameworks for artificial intelligence. In Thirty-first aaai conference on artificial intelligence.
Crawford, K. (2016). Artificial intelligence’s white guy problem. The New York Times, 25.
D'Alfonso, S., Santesteban-Echarri, O., Rice, S., Wadley, G., Lederman, R., Miles, C., ... & Alvarez-Jimenez, M. (2017). Artificial intelligence-assisted online social therapy for youth mental health. Frontiers in Psychology, 8, 796.
Das, A. K., Ashrafi, A., & Ahmmad, M. (2019, February). Joint cognition of both human and machine for predicting criminal punishment in judicial system. In 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 36-40). IEEE.
Dean, J. (2017). How will artificial intelligence affect your life. [Video]. (0:15:55). TedTalks. How Will Artificial Intelligence Affect Your Life | Jeff Dean | TEDxLA
Deng, L. (2018). Artificial intelligence in the rising wave of deep learning: The historical path and future outlook [perspectives ]. IEEE Signal Processing Magazine, 35(1), 180-177.
Deweerdt, S. (2019). Deep connections. Nature, 571(7766), S6-S8.
Dewey, D. (2013). The long-term future of AI (and what we can do about it). Ted Talk. (15:05 minutes)- [video]. Available on:The long-term future of AI(and what we can do about it): Daniel Dewey at TEDxVienna
Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338-349.
Ding, R. X., Palomares, I., Wang, X., Yang, G. R., Liu, B., Dong, Y., ... & Herrera, F. (2020). Large-scale decision-making: characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion.
Eaton, E., Koenig, S., Schulz, C., Maurelli, F., Lee, J., Eckroth, J., ... & Williams, T. (2018). Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program. AI Matters, 3(4), 23-31.
Fazal, M. I., Patel, M. E., Tye, J., & Gupta, Y. (2018). The past, present and future role of artificial intelligence in imaging. European Journal of Radiology, 105, 246-250.
Fiske, A., Henningsen, P., & Buyx, A. (2019). Your robot therapist will see you now: Ethical implications of embodied artificial intelligence in psychiatry, psychology, and psychotherapy. Journal of Medical Internet Research, 21(5), e13216.
Frey, L. (2019). Artificial intelligence and integrated genotype–Phenotype identification. Genes, 10(1), 18.
Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452.
Gil, Y., Honaker, J., Gupta, S., Ma, Y., D'Orazio, V., Garijo, D., ... & Jahanshad, N. (2019, March). Towards human-guided machine learning . In Proceedings of the 24th International Conference on Intelligent User Interfaces (pp. 614-624).
Greene, D., Hoffmann, A. L., & Stark, L. (2019, January). Better, nicer, clearer, fairer: A critical Assessment of the movement for ethical artificial intelligence and machine learning. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23(1), 40-55. https://doi.org/10.1038/s41580-021-00407-0
Guestrin, C. (2019, October). 4 Systems perspectives into human-centered machine learning. In The 25th Annual International Conference on Mobile Computing and Networking (pp. 1-2).
Hager, G. D., Drobnis, A., Fang, F., Ghani, R., Greenwald, A., Lyons, T., ... & Tambe, M. (2019). Artificial intelligence for social good. arXiv preprint arXiv:1901.05406.
Haryanto, E., & Ali, R. M. (2019, January). Students’ attitudes towards the use of artificial intelligence SIRI in EFL learning at one public university. In International Seminar and Annual Meeting BKS-PTN Wilayah Barat (Vol. 1, No. 1).
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.
Hazard, C. J., Fusting, C., Resnick, M., Auerbach, M., Meehan, M., & Korobov, V. (2019). Natively interpretable machine learning and artificial intelligence: Preliminary results and future directions. arXiv preprint arXiv:1901.00246.
Helbing, D. (2019). Societal, economic, ethical and legal challenges of the digital revolution: From big data to deep learning, artificial intelligence, and manipulative technologies. In Towards Digital Enlightenment (pp. 47-72). Springer, Cham.
Hvizdalova, D. (2018). General artificial intelligence: Making sci-fi a reality. [video] (17:32 minutes). TEDTalk. Available on:General Artificial Intelligence: Making sci-fi a reality | Darya Hvizdalova | TEDxTrencin
Jaeger, H. (2016). Artificial intelligence: Deep neural reasoning. Nature, 538(7626), 467.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586.
Jiang, H., & Nachum, O. (2019). Identifying and correcting label bias in machine learning. arXiv preprint arXiv:1901.04966.
Jo, Y., Cho, H., Lee, S. Y., Choi, G., Kim, G., Min, H. S., & Park, Y. (2019). Quantitative phase imaging and artificial intelligence: A review. IEEE Journal of Selected Topics in Quantum Electronics, 25(1), 1-14.
Johns Hopkins University. (2011, May 13). Artificial grammar reveals inborn language sense, study shows. ScienceDaily. www.sciencedaily.com/releases/2011/05/110513112256.htm
Karsenti, T. (2019). Artificial intelligence in education: The urgent need to prepare teachers for tomorrow’s schools. Formation et Profession, 27(1), 112-116.
Katyal, S. K. (2019). Private accountability in the age of artificial intelligence. UCLA Law Review, 66, 54.
Knight, S., Shibani, A., & Shum, S. B. (2018). Augmenting formative writing assessment with learning analytics: A design abstraction approach. In 13th International Conference of the Learning Sciences, London, United Kingdom.
Korteling, J. E., Brouwer, A. M., & Toet, A. (2018). A neural network framework for cognitive bias. Frontiers in psychology, 9, 1561.
Kühl, N., Goutier, M., Hirt, R., & Satzger, G. (2019, January). Machine learning in artificial intelligence: Towards a common understanding . In Proceedings of the 52nd Hawaii International Conference on System Sciences.
Langley, P. (2019, July). An integrative framework for artificial intelligence education . In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 9670-9677).
Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K. R. (2019). Unmasking Clever Hans predictors and assessing what machines really learn . Nature communications, 10(1), 1096.
Lee, Y. J., & Park, J. Y. (2018). Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence . Quality & Quantity, 52(2), 653-667.
Liu, M. (2018). The application and development research of artificial intelligence education in wisdom education era. In proceedings of the 2nd International Conference on Social Sciences, Arts and Humanities.
Longo, L. (2018). How to empower education with artificial intelligence (11:31). [Video]. How to Empower Education with Artificial Intelligence | Luca Longo | TEDxDublinInstituteofTechnology
Longo, L. (2019, October). Empowering qualitative research methods in education with artificial intelligence. In World Conference on Qualitative Research (pp. 1-21). Springer, Cham.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education . UK: The Open University.
Lynch, M. (2018). The effects of artificial intelligence on education. [video] (7:22 minutes). TEDxTalks. Available on:Effect of Artificial Intelligence on Education | Adrien Dubois | TEDxCanadianIntlSchool
Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., ... & Ogu, I. O. (2018). Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare . Oncotarget, 9(5), 5665.
Marblestone AH, Wayne G and Kording KP (2016). Toward an Integration of Deep Learning and Neuroscience. Front. Comput. Neurosci. 10:94. doi: 10.3389/fncom. 2016.00094
Maseleno, A., Sabani, N., Huda, M., Ahmad, R., Jasmi, K. A., & Basiron, B. (2018). Demystifying learning analytics in personalised learning . International Journal of Engineering & Technology, 7(3), 1124-1129.
Meloney, D. (2015). Artificial intelligence and education. [video] (4:09 minutes). Available on:Artificial intelligence and education
Michie, S., Thomas, J., Johnston, M., Mac Aonghusa, P., Shawe-Taylor, J., Kelly, M. P., ... & O’Mara-Eves, A. (2017). The human behaviour-change project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation . Implementation Science, 12(1), 121.
Mitchell, M. (2021). Why AI is harder than we think. arXiv preprint arXiv:2104.12871.
Mitchell, T, (2018) Conversational Machine Learning. Rice Ken Kennedy Institute for Information and Technology. Retrieved: 11/11/2019 https://www.youtube.com/watch? v=NXD0aE8w27g
Moldoveanu, M. C. (2019). Intelligent artificiality: Algorithmic microfoundations for strategic problem solving. Harvard Business School.
Moravčík, M., Schmid, M., Burch, N., Lisý, V., Morrill, D., Bard, N., ... & Bowling, M. (2017). Deepstack: Expert-level artificial intelligence in heads-up no-limit poker . Science, 356(6337), 508-513.
Motta, A., Berning, M., Boergens, K. M., Staffler, B., Beining, M., Loomba, S., ... & Helmstaedter, M. (2019). Dense connectomic reconstruction in layer 4 of the somatosensory cortex . Science, 366(6469).
Müller, V. C. (Ed.). (2016). Risks of artificial intelligence (p. 291). Boca Raton, FL: CRC Press.
Müller, V. C., & Bostrom, N. (2016). Future progress in artificial intelligence: A survey of expert opinion . In Fundamental issues of artificial intelligence (pp. 555-572). Cham, Switzerland, Springer.
Nalmpantis, C., & Vrakas, D. (2019). Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparasion . Artificial Intelligence Review, 52(1), 217-243.
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Oita, M. (2019). Reverse engineering creativity into interpretable neural networks . In Proceedings of the Future of Information and Communication Conference. FICC.
Osoba, O. A., & Welser IV, W. (2017). An intelligence in our image: The risks of bias and errors in artificial intelligence . Rand Corporation.
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Pasquale, F. (2019). Professional judgment in an era of artificial intelligence and machine learning . boundary 2: an international journal of literature and culture, 46(1), 73-101.
Pearl, J., & Mackenzie, D. (2018). The book of why: the new science of cause and effect . Basic Books.
Perrotta, C., & Williamson, B. (2018). The social life of Learning Analytics: cluster analysis and the ‘performance’ of algorithmic education. Learning, Media and Technology, 43(1), 3-16.
Porayska-Pomsta, K., & Rajendran, G. (2019). Accountability in human and artificial intelligence decision-making as the basis for diversity and educational inclusion. In Artificial Intelligence and Inclusive Education (pp. 39-59). Springer, Singapore.
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Schindlholzer, B. (2016). Artificial intelligence & the future of education systems. Ted Talks. (14: 51 minutes) [video]. Available on:Artificial intelligence & the future of education systems | Bernhard Schindlholzer | TEDxFHKufstein
Shneiderman, B. (2016). Opinion: The dangers of faulty, biased, or malicious algorithms requires independent oversight. Proceedings of the National Academy of Sciences, 113(48), 13538-13540.
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Date of last update: 5-Feb-2021
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