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Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals.
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Participatory approaches to machine learning
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Learning interpretable concept-based models with human feedback
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Improving recommender systems beyond the algorithm
T. Schnabel, P. N. Bennett, and T. Joachims · 2018
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Connecting optimization and regularization paths
A. Suggala, A. Prasad, and P. K. Ravikumar · 2018
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A meta-analytic review of two modes of learning and the description-experience gap
D. U. Wulff, M. Mergenthaler-Canseco, and R. Hertwig · 2018
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Fairgan: Fairness-aware generative adversarial networks
D. Xu, S. Yuan, L. Zhang, and X. Wu · 2018
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Toward algorithmic accountability in public services: A qualitative study of affected community perspectives on algorithmic decision-making in child welfare services
A. Brown, A. Chouldechova, E. Putnam-Hornstein, A. Tobin, and R. Vaithianathan · 2019
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Human-centered tools for coping with imperfect algorithms during medical decision-making
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I. Lage and F. Doshi-Velez · 2020
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Explanation-based tuning of opaque machine learners with application to paper recommendation
B. C. G. Lee, K. Lo, D. Downey, and D. S. Weld · 2020
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Mcunet: Tiny deep learning on iot devices
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Predictive multiplicity in classification
C. Marx, F. Calmon, and B. Ustun · 2020
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A large-scale analysis of racial disparities in police stops across the united states
E. Pierson, C. Simoiu, J. Overgoor, S. Corbett-Davies, D. Jenson, A. Shoemaker, V. Ramachandran, P. Barghouty, C. Phillips, R. Shroff, et al · 2020
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Regularizing black-box models for improved interpretability
G. Plumb, M. Al-Shedivat, Á. A. Cabrera, A. Perer, E. Xing, and A. Talwalkar · 2020
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S. Robertson and N. Salehi · 2020
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Feature engineering with clinical expert knowledge: a case study assessment of machine learning model complexity and performance
K. D. Roe, V. Jawa, X. Zhang, C. G. Chute, J. A. Epstein, J. Matelsky, I. Shpitser, and C. O. Taylor · 2020
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Fairness warnings and fair-maml: learning fairly with minimal data
D. Slack, S. A. Friedler, and E. Givental · 2020
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Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics
A. Tseng, A. Shrikumar, and A. Kundaje · 2020
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Human-in-the-loop low-shot learning
S. Wan, Y. Hou, F. Bao, Z. Ren, Y. Dong, Q. Dai, and Y. Deng · 2020
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A mathematical theory of cooperative communication
P. Wang, J. Wang, P. Paranamana, and P. Shafto · 2020
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Deontological ethics by monotonicity shape constraints
S. Wang and M. Gupta · 2020
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Learning deep attribution priors based on prior knowledge
E. Weinberger, J. Janizek, and S.-I. Lee · 2020
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Keeping designers in the loop: Communicating inherent algorithmic trade-offs across multiple objectives
B. Yu, Y. Yuan, L. Terveen, Z. S. Wu, J. Forlizzi, and H. Zhu · 2020
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Beyond reasonable doubt: Improving fairness in budget-constrained decision making using confidence thresholds
M. A. Bakker, D. P. Tu, K. P. Gummadi, A. S. Pentland, K. R. Varshney, and A. Weller · 2021
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Machine unlearning
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot · 2021
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Discovering and validating ai errors with crowdsourced failure reports
Á. A. Cabrera, A. J. Druck, J. I. Hong, and A. Perer · 2021
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Soliciting stakeholders’ fairness notions in child maltreatment predictive systems
H.-F. Cheng, L. Stapleton, R. Wang, P. Bullock, A. Chouldechova, Z. S. S. Wu, and H. Zhu · 2021
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Characterizing fairness over the set of good models under selective labels
A. Coston, A. Rambachan, and A. Chouldechova · 2021
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Understanding the relationship between interactions and outcomes in human-in-the-loop machine learning
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Leveraging expert consistency to improve algorithmic decision support
M. De-Arteaga, A. Dubrawski, and A. Chouldechova · 2021
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Human-in-the-loop for data collection: a multi-target counter narrative dataset to fight online hate speech
M. Fanton, H. Bonaldi, S. S. Tekiroğlu, and M. Guerini · 2021
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Learning representations by humans, for humans
S. Hilgard, N. Rosenfeld, M. R. Banaji, J. Cao, and D. Parkes · 2021
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Optimizing black-box metrics with iterative example weighting
G. Hiranandani, J. Mathur, O. Koyejo, M. M. Fard, and H. Narasimhan · 2021
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Online learning: A comprehensive survey
S. C. Hoi, D. Sahoo, J. Lu, and P. Zhao · 2021
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Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai
A. Jacovi, A. Marasović, T. Miller, and Y. Goldberg · 2021
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Towards observability data management at scale
S. Karumuri, F. Solleza, S. Zdonik, and N. Tatbul · 2021
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Knowledge-adaptation priors
M. E. E. Khan and S. Swaroop · 2021
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Uncertain decisions facilitate better preference learning
C. Laidlaw and S. Russell · 2021
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Intermittent human-in-the-loop model selection using cerebro: a demonstration
L. Li, S. Nakandala, and A. Kumar · 2021
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On incorporating inductive biases into vaes
N. Miao, E. Mathieu, N. Siddharth, Y. W. Teh, and T. Rainforth · 2021
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Finding and fixing spurious patterns with explanations
G. Plumb, M. T. Ribeiro, and A. Talwalkar · 2021
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Deep learning for deep waters: An expert-in-the-loop machine learning framework for marine sciences
I. Ryazanov, A. T. Nylund, D. Basu, I.-M. Hassellöv, and A. Schliep · 2021
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Editing a classifier by rewriting its prediction rules
S. Santurkar, D. Tsipras, M. Elango, D. Bau, A. Torralba, and A. Madry · 2021
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A ranking approach to fair classification
J. Schoeffer, N. Kuehl, and I. Valera · 2021
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Gam changer: Editing generalized additive models with interactive visualization
Z. J. Wang, A. Kale, H. Nori, P. Stella, M. Nunnally, D. H. Chau, M. Vorvoreanu, J. W. Vaughan, and R. Caruana · 2021
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On value-laden science
Z. B. Ward · 2021
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A human-in-the-loop framework to construct context-aware mathematical notions of outcome fairness
M. Yaghini, A. Krause, and H. Heidari · 2021
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Interpretable machine learning: Moving from mythos to diagnostics
V. Chen, J. Li, J. S. Kim, G. Plumb, and A. Talwalkar · 2022
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Jury learning: Integrating dissenting voices into machine learning models
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How transparency modulates trust in artificial intelligence
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