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The widespread adoption of algorithmic decision-making systems has brought about the necessity to interpret the reasoning behind these decisions.
B. Kim, C. Rudin, and J. A. Shah, “The bayesian case model: A generative approach for case-based reasoning and prototype classification,” in Proc NIPS , 2014, pp. 1952–1960
1960
Earlier work this paper cites.
D. Harrison Jr and D. L. Rubinfeld, “Hedonic housing prices and the demand for clean air,” 1978
1978
Earlier work this paper cites.
P. M. Bentler and D. G. Weeks, “Linear structural equations with latent variables,” Psychometrika , vol. 45, no. 3, pp. 289–308, 1980
1980
Earlier work this paper cites.
J. W. Smith, J. Everhart, W. Dickson, W. Knowler, and R. Johannes, “Using the adap learning algorithm to forecast the onset of diabetes mellitus,” in Proc. of the Annual Symposium on Computer Application in Medical Care , 1988, p. 261
1988
Earlier work this paper cites.
T. Hastie and R. Tibshirani, Generalized Additive Models . CRC Press, 1990, vol. 43
1990
Earlier work this paper cites.
G. A. Miller, “The magical number seven, plus or minus two: Some limits on our capacity for processing information.” Psychological review , vol. 101, no. 2, p. 343, 1994
1994
Earlier work this paper cites.
R. H. Hoyle, Structural equation modeling: Concepts, issues, and applications . Sage, 1995
1995
Earlier work this paper cites.
J. Ellson, E. Gansner, L. Koutsofios, S. C. North, and G. Woodhull, “Graphviz—open source graph drawing tools,” in International Symposium on Graph Drawing . Springer, 2001, pp. 483–484
2001
Earlier work this paper cites.
R. Ryan and E. Deci, “Self-determination theory and the role of basic psychological needs in personality and the organization of behavior.” in Handbook of Personality: R&T , 2008, pp. 654–678
2008
Earlier work this paper cites.
J. Schell, The Art of Game Design: Book of Lenses . CRC Press, 2008
2008
Earlier work this paper cites.
T. Brennan, W. Dieterich, and B. Ehret, “Evaluating the predictive validity of the compas risk and needs assessment system,” Criminal Justice and Behavior , vol. 36, no. 1, pp. 21–40, 2009
2009
Earlier work this paper cites.
M. Bostock, V. Ogievetsky, and J. Heer, “D 3 data-driven documents,” IEEE Trans on Visualization and Computer Graphics , vol. 17, no. 12, pp. 2301–2309, 2011
2011
Earlier work this paper cites.
Z. Zhang, K. T. McDonnell, and K. Mueller, “A network-based interface for the exploration of high-dimensional data spaces,” in 2012 IEEE Pacific Visualization Symposium , 2012, pp. 17–24
2012
Earlier work this paper cites.
J. Pearl, Causality: Models, Reasoning & Inference, 2nd Ed. Cambridge University Press, 2013
2013
Earlier work this paper cites.
S. Amershi, M. Chickering, S. Drucker, B. Lee, P. Simard, and J. Suh, “Modeltracker: Redesigning performance analysis tools for machine learning,” in ACM CHI , 2015, pp. 337–346
2015
Earlier work this paper cites.
J. Wang and K. Mueller, “The visual causality analyst: An interactive interface for causal reasoning,” IEEE Trans on Visualization and Computer graphics , vol. 22, no. 1, pp. 230–239, 2015
2015
Earlier work this paper cites.
J. Angwin, J. Larson, S. Mattu, and L. Kirchner, “Machine bias,” ProPublica, May , vol. 23, p. 2016, 2016
2016
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should i trust you? explaining the predictions of any classifier,” in Proc. ACM Knowledge Discovery and Data Mining , 2016, pp. 1135–1144
2016
Earlier work this paper cites.
J. Krause, A. Perer, and K. Ng, “Interacting with predictions: Visual inspection of black-box machine learning models,” in Proc. ACM CHI , 2016, pp. 5686–5697
2016
Earlier work this paper cites.
S. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc NIPS , 2017, pp. 4765–4774
2017
Earlier work this paper cites.
M. Kahng, P. Y. Andrews, A. Kalro, and D. Chau, “Activis: Visual exploration of industry-scale deep neural network models,” IEEE Trans on Vis. and Computer Graphics , vol. 24, no. 1, pp. 88–97, 2017
2017
Cited alongside, same era.
P. Voigt and A. Von dem Bussche, “The eu general data protection regulation (gdpr),” A Practical Guide 1st ed., Springer Int’l. , 2017
2017
Cited alongside, same era.
——, “Visual causality analysis made practical,” in 2017 IEEE Conf. on Visual Analytics Science and Technology (VAST) , 2017, pp. 151–161
2017
Cited alongside, same era.
A. Chouldechova, D. Benavides-Prado, O. Fialko, and R. Vaithianathan, “A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions,” in Conference on Fairness, Accountability and Transparency , 2018, pp. 134–148
2018
Cited alongside, same era.
T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” Artificial Intelligence , vol. 267, pp. 1–38, 2019
2019
Later among the works it cites.
C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature Machine Intelligence , vol. 1, no. 5, pp. 206–215, 2019
2019
Later among the works it cites.
B. Glymour and J. Herington, “Measuring the biases that matter: The ethical and casual foundations for measures of fairness in algorithms,” in Proc Conf. on Fairness, Accountability, and Transparency , 2019, pp. 269–278
2019
Later among the works it cites.
Y. Wu, L. Zhang, X. Wu, and H. Tong, “Pc-fairness: A unified framework for measuring causality-based fairness,” in Proc NIPS , 2019, pp. 3399–3409
2019
Later among the works it cites.
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J. Buolamwini and T. Gebru, “Gender shades: Intersectional accuracy disparities in commercial gender classification,” in Conference on fairness, accountability and transparency . PMLR, 2018, pp. 77–91
2018
Cited alongside, same era.
O. Keyes, “The misgendering machines: Trans/hci implications of automatic gender recognition,” ACM CSCW , vol. 2, pp. 1–22, 2018
2018
Cited alongside, same era.
F. Hohman, M. Kahng, R. Pienta, and D. H. Chau, “Visual analytics in deep learning: An interrogative survey for the next frontiers,” IEEE Trans on Visualization and Computer Graphics , vol. 25, no. 8, pp. 2674–2693, 2018
2018
Cited alongside, same era.
A. Abdul, J. Vermeulen, D. Wang, B. Lim, and M. Kankanhalli, “Trends and trajectories for explainable, accountable and intelligible systems: A hci research agenda,” in ACM CHI , 2018, pp. 1–18
2018
Cited alongside, same era.
Y. Ming, H. Qu, and E. Bertini, “Rulematrix: Visualizing and understanding classifiers with rules,” IEEE Trans on Visualization and Computer Graphics , vol. 25, no. 1, pp. 342–352, 2018
2018
Cited alongside, same era.
J. Pearl and D. Mackenzie, The Book of Why: the New Science of Cause and Effect . Basic Books, 2018
2018
Cited alongside, same era.
J. Zhang and E. Bareinboim, “Fairness in decision-making—the causal explanation formula,” in AAAI Artificial Intelligence , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. Madras, E. Creager, T. Pitassi, and R. Zemel, “Fairness through causal awareness: Learning causal latent-variable models for biased data,” in Proceedings of the Conference on Fairness, Accountability, and Transparency . ACM, 2019, pp. 349–358
2019
Later among the works it cites.
A. Khademi, S. Lee, D. Foley, and V. Honavar, “Fairness in algorithmic decision making: An excursion through the lens of causality,” in ACM World Wide Web , 2019, pp. 2907–2914
2019
Later among the works it cites.
J. Wexler, M. Pushkarna, T. Bolukbasi, M. Wattenberg, F. Viégas, and J. Wilson, “The what-if tool: Interactive probing of machine learning models,” IEEE Trans on Visualization and Computer Graphics , vol. 26, no. 1, pp. 56–65, 2019
2019
Later among the works it cites.
C. Glymour, K. Zhang, and P. Spirtes, “Review of causal discovery methods based on graphical models,” Frontiers in Genetics , vol. 10, p. 524, 2019
2019
Later among the works it cites.
C. Rudin and J. Radin, “Why are we using black box models in ai when we don’t need to? a lesson from an explainable ai competition,” Harvard Data Science Review , vol. 1, no. 2, 2019
2019
Later among the works it cites.
H. Shen, H. Jin, Á. A. Cabrera, A. Perer, H. Zhu, and J. I. Hong, “Designing alternative representations of confusion matrices to support non-expert public understanding of algorithm performance,” Proc. ACM on HCI , vol. 4, pp. 1–22, 2020
2020
Later among the works it cites.
R. Moraffah, M. Karami, R. Guo, A. Raglin, and H. Liu, “Causal interpretability for machine learning-problems, methods and evaluation,” ACM KDD Explorations , vol. 22, no. 1, pp. 18–33, 2020
2020
Later among the works it cites.
I. E. Kumar, S. Venkatasubramanian, C. Scheidegger, and S. Friedler, “Problems with shapley-value-based explanations as feature importance measures,” Proc. ICML , 2020
2020
Later among the works it cites.
O. Gomez, S. Holter, J. Yuan, and E. Bertini, “Vice: visual counterfactual explanations for machine learning models,” in Proc. Intern. Conference on Intelligent User Interfaces , 2020, pp. 531–535
2020
Later among the works it cites.
J. Yan, Z. Gu, H. Lin, and J. Rzeszotarski, “Silva: Interactively assessing machine learning fairness using causality,” in Proc. ACM CHI , 2020, pp. 1–13
2020
Later among the works it cites.
X. Xie, F. Du, and Y. Wu, “A visual analytics approach for exploratory causal analysis: Exploration, validation, and applications,” IEEE Trans on Visualization and Computer Graphics , 2020
2020
Later among the works it cites.
H. Natsukawa, E. R. Deyle, G. M. Pao, K. Koyamada, and G. Sugihara, “A visual analytics approach for ecosystem dynamics based on empirical dynamic modeling,” IEEE Transactions on Visualization and Computer Graphics , 2020
2020
Later among the works it cites.
X. Shen, S. Ma, P. Vemuri, and G. Simon, “Challenges and opportunities with causal discovery algorithms: Application to alzheimer’s pathophysiology,” Scientific Reports , vol. 10, no. 1, pp. 1–12, 2020
2020
Later among the works it cites.
S. Tople, A. Sharma, and A. Nori, “Alleviating privacy attacks via causal learning,” in International Conference on Machine Learning . PMLR, 2020, pp. 9537–9547
2020
Later among the works it cites.
Z. Buçinca, P. Lin, K. Z. Gajos, and E. L. Glassman, “Proxy tasks and subjective measures can be misleading in evaluating explainable ai systems,” in Proc. ACM IUI , 2020, pp. 454–464
2020
Later among the works it cites.