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Computer Supported Cooperative Work (CSCW)

02-02-2024 | Editorial

Machine Learning and the Work of the User

Authors: Richard Harper, Dave Randall

Published in: Computer Supported Cooperative Work (CSCW)

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Abstract

This paper introduces the collection of the Journal on Machine Learning (ML) and the user. It provides a brief history of ML from the 1950’s through to the current time, sketching the nature of the kinds of precursor AI techniques used in such things as expert systems right the way through to the emergence of ML and its tool sets, including deep learning. It concludes with the ‘generative AI’ used in such ML technologies as PaLM and GPT-3. The history highlights key changes and developments in ML, the especial importance and limitations of deep learning, and the changing attitudes and expectations of users in an environment when ML can and often is oversold. The paper then explores the ways CSCW research has addressed the social context of organisational systems and how the same can apply for ML tools and techniques. It urges research that focuses on the particular ways that ML comes to fit into ‘real world’ collaborative work sites and hence speaks to the CSCW cannon.

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Literature
Adomavicius, G., J. Bockstedt, P. Shawn, and J. Zhang. 2013. Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research 24 (4): 956–975.CrossRef
Afoudi, Y., M. Lazaar, and M. Al Achhab. 2021. Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network. Simulation Modelling Practice and Theory 113: 102375.CrossRef
Asaro, P. 2019. AI ethics in predictive policing: From models of threat to an ethics of care. IEEE Technology and Society Magazine 38 (2): 40–53.ADSMathSciNetCrossRef
Bassett, C. 2019. The computational therapeutic: Exploring Weizenbaum’s ELIZA as a history of the present. AI and SOCIETY 34 (4): 803–812.CrossRef
Bassett, C. 2021. Anti-computing: Dissent and the machine. Manchester University Press.
Bender, E.M., and A. Koller. 2020. Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics.
Berk, R., H. Heidari, S. Jabbari, M. Kearns, and A. Roth. 2021. Fairness in criminal justice risk assessments: The state of the art. Sociological Methods and Research 50 (1): 3–44.MathSciNetCrossRef
Bickhard, M., and L. Terveen. 1996. Foundational issues in artificial intelligence and cognitive science: Impasse and solution. Amsterdam: North Holland, Elsevier.
Bittner, E. 1965. The concept of organization. Social Research 32 (3): 239–325.
Brodeala, C. 2020. Online recommender system for accessible tourism destinations. In Fourteenth ACM Conference on Recommender Systems, 787–791.
Bruns, A. 2019. Are filter bubbles real? London: Wiley.
Button, G., J. Coulter, J. Lee, and W. Sharrock. 1995. Computers, minds and conduct. Cambridge: Polity Press.
Chitra, U., and C. Musco. 2020. Analyzing the impact of filter bubbles on social network polarization. In Proceedings of the 13th International Conference on Web Search and Data Mining, 115–123. New York, NY: Association for Computing Machinery.
Collins, H. 2018. Artifictional Intelligence: Against Humanities Surrender to Computers. New York: Wiley.
Cosley, D., S.L. Lam, L. Albert, J. Konstan and J. Riedl. 2003. Is seeing believing? How recommender system interfaces affect users' opinions. In Proceedings of the SIGCHI conference on Human factors in computing systems, 585–592. New York, NY: Association for Computing Machinery.
Coulter, J. 1987. The social construction of mind: Studies in ethnomethodology and linguistic philosophy. Godalming: Springer.
Dahlgren, G. 2021. A critical review of filter bubbles and a comparison with selective exposure. Nordicom Review 42 (1): 15–33.CrossRef
Domingos, P. 2017. The Master Algorithm, How the Quest for the Ultimate Learning Machine will Remake our World. London: Penguin Books.
Dubois, E., and G. Blank. 2018. The echo chamber is overstated: The moderating effect of political interest and diverse media. Information, Communication and Society 21 (5): 729–745.CrossRef
Duboue, D. 2020. The Art of Feature Engineering. Cambridge: Cambridge University Press.CrossRef
Flaxman, S., G. Sharad, and M. Justin. 2016. Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly 80 (S1): 298–320.CrossRef
Fortuna, B., C. Fortuna, and D. Mladenić. 2010. Real-time news recommender system. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 583–586. Berlin, Heidelberg: Springer.CrossRef
Franco, R. Z. 2017. Online recommender system for personalized nutrition advice. In Proceedings of the Eleventh ACM Conference on Recommender Systems, 411–415. New York, NY: Association for Computing Machinery.
Garfinkel, H. 1967. Studies in Ethnomethodology. New York: Prentice Hall.
Ge, M., F. Ricci, and D. Massimo. 2015. Health-aware food recommender system. In Proceedings of the 9th ACM Conference on Recommender Systems, 333–334. New York, NY: Association for Computing Machinery.
Geetha, G., M. Safa, C. Fancy, and D. Saranya. 2018. A hybrid approach using collaborative filtering and content-based filtering for recommender system. Journal of Physics: Conference Series 1000 (1): 012101.
Ghosh, S., M. Mundhe, K. Hernandez, and S. Sen. 1999. Voting for movies: The anatomy of a recommender system. In Proceedings of the third annual conference on Autonomous Agents, 434–435. New York, NY: Association for Computing Machinery.
Gunawan, A., and D. Suhartono. 2019. Music recommender system based on genre using convolutional recurrent neural networks. Procedia Computer Science 157: 99–109.CrossRef
Harper, R., D. Randall, and W. Sharrock. 2016. Choice: The sciences of reason in the 21st Century. Cambridge: Polity Press.
Harper, R., D. Watson, and C. Licoppe (eds.). 2019. Skyping the family: Interpersonal video and domestic life. Amsterdam, Netherlands: John Benjamins.
Hilderbrant, M. 2006. Profiling: From data to knowledge. Datenschutz und Datensicherhiet 30, 9.
Jackson, K. 2018. Predictive analytics in child welfare–benefits and challenges. Social Work Today 18 (2): 10.
Kosseff, J. 2019. The twenty-six words that created the Internet. New York: Cornell University Press.CrossRef
Kurtzweil, R. 2013. How to Create a Mind: The Secret of Human Thought Revealed. New York: Viking.
Li, Shan, and D. Weihong. 2020. Deep facial expression recognition: A survey. IEEE Transactions on Affective Computing (2020).
Mahata, A., N. Saini, S. Saharawat, and R. Tiwari. 2016. Intelligent movie recommender system using machine learning. In International Conference on Intelligent Human Computer Interaction, 94–110. Cham: Springer.
Marcus, G., and E. Davis. 2019. Rebooting AI: Building artificial intelligence we can trust. New York: Vintage Books.
Moscato, V., A. Picariello, and G. Sperli. 2020. An emotional recommender system for music. IEEE Intelligent Systems 36 (5): 57–68.CrossRef
Natale, S. 2019. If software is narrative: Joseph Weizenbaum, artificial intelligence and the biographies of ELIZA. New Media and Society 21 (3): 712–728.CrossRef
Nilashi, M., K. Bagherifard, M. Rahmani, and V. Rafe. 2017. A recommender system for tourism industry using cluster ensemble and prediction machine learning techniques. Computers and Industrial Engineering 109: 357–368.CrossRef
Ontika, N.N., Syed, H.A., Saßmannshausen, S.S., Harper, R. Chen, Y. Park, S.Y., and M. Grisot. 2022. Exploring human-centred AI in healthcare: Diagnosis, explainability, and trust. In Proceedings of 20th European Conference on Computer-Supported Cooperative Work. Coimbra, Portugal: European Society for Socially Embedded Technologies (EUSSET).
Pariser, Eli. 2011. The filter bubble: What the Internet is hiding from you. New York: The Penguin Press.
Park, S.Y., P. Kuo, A. Barbarin, E. Kaziunas, A. Chow, K. Singh, L. Wilcox, and W.S Lasecki. 2019. Identifying challenges and opportunities in human-AI collaboration in healthcare. In Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing, 506–510. New York, NY: Association for Computing Machinery.
Ploug, T., and S. Holm. 2020. The four dimensions of contestable AI diagnostics-A patient-centric approach to explainable AI. Artificial Intelligence in Medicine 107: 101901.CrossRefPubMed
Resnick, R., and H. Varian. 1997. Recommender systems. Communications of the ACM 40 (3): 56.CrossRef
Ruchir, P. Kung, D.S., Janssen, G. Zhang, W. Domeniconi, G. Zolotov, V., Dolby, J. et al. 2021. CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks. arXiv preprint arXiv:2105.12655.
Russell, S. 2019. Human Compatible: AI and the problem of control. London: Penguin.
Russell, S., and P. Norvig. 2017. Artificial intelligence: A modern approach. Boston, USA: Pearson.
Spano, L., and L. Boratto. 2019. Advances in computer-human interaction for recommender systems (AdCHIReS). International Journal of Human-Computer Studies 121: 1–3.CrossRef
Sunstein, C. 2004. Democracy and filtering. Communications of the ACM 47 (12): 57–59.CrossRef
Valdez, A., and M. Ziefle. 2019. The users’ perspective on the privacy-utility trade-offs in health recommender systems. International Journal of Human-Computer Studies 121: 108–121.CrossRef
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, 6000–6010. Red Hook, NY: Curran Associates Inc.
Walek, B., and V. Fojtik. 2020. A hybrid recommender system for recommending relevant movies using an expert system. Expert Systems with Applications 158: 113452.CrossRef
Zhang, J. 2011. Anchoring effects of recommender systems. In Proceedings of the fifth ACM conference on Recommender systems, 375–378. New York, NY: Association for Computing Machinery.
Metadata
Title
Machine Learning and the Work of the User
Authors
Richard Harper
Dave Randall
Publication date
02-02-2024
Publisher
Springer Netherlands
Published in
Computer Supported Cooperative Work (CSCW)
Print ISSN: 0925-9724
Electronic ISSN: 1573-7551
DOI
https://doi.org/10.1007/s10606-023-09483-6

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