Abstract
This panel will bring a timely and overdue discussion to SBP-BRiMS on computational social and behavioral modeling for social good. What’s more, we will host a discussion on what it means to critically approach social good in a way that moves beyond discussions of bias and representation. During this panel, panelists will introduce themselves and positions on topics related to social good within computational behavioral and social modeling, and (along with audience members) proceed to discuss sub-topics, queries, and provocations. Chris Dancy will moderate the discussion and ensure an opportunity for interactive experience for all of those in attendance.
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Uyheng, J., Carley, K.M.: Characterizing bot networks on twitter: an empirical analysis of contentious issues in the Asia-Pacific. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds.) SBP-BRiMS 2019. LNCS, vol. 11549, pp. 153–162. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21741-9_16
Clark, M., Frydenlund, E., Padilla, J.J.: Network structures and humanitarian need. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds.) SBP-BRiMS 2021. LNCS, vol. 12720, pp. 214–223. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80387-2_21
Morgan, J.H., Lebiere, C., Moody, J., Orr, M.G.: Trusty ally or faithless snake: modeling the role of human memory and expectations in social exchange. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds.) SBP-BRiMS 2021. LNCS, vol. 12720, pp. 268–278. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80387-2_26
Orr, M.G., Lebiere, C., Stocco, A., Pirolli, P., Pires, B., Kennedy, W.G.: Multi-scale resolution of neural, cognitive and social systems. Comput. Math. Organ. Theory 25(1), 4–23 (2019). https://doi.org/10.1007/s10588-018-09291-0
Atkins, A.A., Brown, M.S., Dancy, C.L.: Examining the effects of race on human-AI cooperation. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds.) SBP-BRiMS 2021. LNCS, vol. 12720, pp. 279–288. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80387-2_27
Shapiro, B., Crooks, A.: Kinetic action and radicalization: a case study of Pakistan. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds.) SBP-BRiMS 2021. LNCS, vol. 12720, pp. 321–330. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80387-2_31
King, C., Bellutta, D., Carley, K.M.: Lying about lying on social media: a case study of the 2019 Canadian elections. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A., Hussain, M. (eds.) SBP-BRiMS 2020. LNCS, vol. 12268, pp. 75–85. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61255-9_8
Memon, S.A., Tyagi, A., Mortensen, D.R., Carley, K.M.: Characterizing sociolinguistic variation in the competing vaccination communities. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A., Hussain, M. (eds.) SBP-BRiMS 2020. LNCS, vol. 12268, pp. 118–129. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61255-9_12
Dineen, J., Haque, A.S.M.AU., Bielskas, M.: Formal methods for an iterated volunteer’s dilemma. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds.) SBP-BRiMS 2021. LNCS, vol. 12720, pp. 81–90. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80387-2_8
Khaouja, I., Makdoun, I., Mezzour, G.: Using social network analysis to analyze development priorities of moroccan institutions. In: Thomson, R., Hussain, M.N., Dancy, C., Pyke, A. (eds.) SBP-BRiMS 2021. LNCS, vol. 12720, pp. 195–203. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80387-2_19
Lee, K., Braithwaite, J., Atchikpa, M.: Understanding colonial legacy and environmental issues in senegal through language use. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A., Hussain, M. (eds.) SBP-BRiMS 2020. LNCS, vol. 12268, pp. 23–34. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61255-9_3
Osterritter, L.J., Carley, K.M.: Modeling interventions for insider threat. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A., Hussain, M. (eds.) SBP-BRiMS 2020. LNCS, vol. 12268, pp. 55–64. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61255-9_6
Ghani, R.: Data science for social good and public policy: examples, opportunities, and challenges. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA, p. 3. ACM (2018)
Kushwaha, S., et al.: Significant applications of machine learning for COVID-19 pandemic. J. Ind. Integr. Manag. 05, 453–479 (2020)
Yang, F., Vereshchaka, A., Lepri, B., Dong, W.: Optimizing city-scale traffic through modeling observations of vehicle movements. IEEE Trans. Intell. Transp. Syst., 1–12 (2021)
Moats, D., Seaver, N.: “You social scientists love mind games”: experimenting in the “divide” between data science and critical algorithm studies. Big Data Soc. 6, 2053951719833404 (2019)
Benjamin, R.: Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press, Medford (2019)
Salganik, M.J., et al.: Measuring the predictability of life outcomes with a scientific mass collaboration. Proc. Natl. Acad. Sci. 117, 8398–8403 (2020)
Ensign, D., Friedler, S.A., Neville, S., Scheidegger, C., Venkatasubramanian, S.: Runaway feedback loops in predictive policing. In: 1st Conference on Fairness, Accountability and Transparency, pp. 160–171. PMLR (2018)
Obermeyer, Z., Powers, B., Vogeli, C., Mullainathan, S.: Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019)
Birhane, A., et al.: The forgotten margins of AI ethics. In: 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 948–958. ACM (2022)
Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-DF: a large-scale challenging dataset for DeepFake forensics. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3204–3213 (2020)
Cardoso Llach, D., Ozkar, M.: Cultivating the critical imagination: post-disciplinary pedagogy in a computational design laboratory. Digit. Creat. 30, 257–276 (2019)
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Dancy, C.L., Joseph, K. (2022). Computational Models for Social Good: Beyond Bias and Representation. In: Thomson, R., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2022. Lecture Notes in Computer Science, vol 13558. Springer, Cham. https://doi.org/10.1007/978-3-031-17114-7_25
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