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Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers.
Wordnet: a lexical database for english
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Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., and Bovik, A. C · 2003
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Complementary computing: policies for transferring callers from dialog systems to human receptionists
Horvitz, E. and Paek, T · 2007
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Combining human and machine intelligence in large-scale crowdsourcing
Kamar, E., Hacker, S., and Horvitz, E · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Near-optimally teaching the crowd to classify
Singla, A., Bogunovic, I., Bartók, G., Karbasi, A., and Krause, A · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Brown, T. B., Mané, D., Roy, A., Abadi, M., and Gilmer, J · 2017
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Proposal flow: Semantic correspondences from object proposals
Ham, B., Cho, M., Schmid, C., and Ponce, J · 2017
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Evaluating effects of user experience and system transparency on trust in automation
Yang, X. J., Unhelkar, V. V., Li, K., and Shah, J. A · 2017
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Fine-grained visual recognition with salient feature detection
Feng, H., Wang, S., and Ge, S. S · 2018
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Interpreting recurrent and attention-based neural models: a case study on natural language inference
Ghaeini, R., Fern, X. Z., and Tadepalli, P · 2018
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Teaching categories to human learners with visual explanations
Mac Aodha, O., Su, S., Chen, Y., Perona, P., and Yue, Y · 2018
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Prolific. ac—a subject pool for online experiments
Palan, S. and Schitter, C · 2018
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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Papernot, N. and McDaniel, P · 2018
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Neighbourhood consensus networks
Rocco, I., Cimpoi, M., Arandjelović, R., Torii, A., Pajdla, T., and Sivic, J · 2018
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The inaturalist species classification and detection dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., and Belongie, S · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
Alcorn, M. A., Li, Q., Gong, Z., Wang, C., Mai, L., Ku, W.-S., and Nguyen, A · 2019
Cited alongside, same era.
Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Barbu, A., Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenbaum, J., and Katz, B · 2019
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This looks like that: deep learning for interpretable image recognition
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., and Su, J. K · 2019
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https://www.technologyreview.com/2021/04/14/1022676/robert-williams-facial-recognition-lawsuit-aclu-detroit-police/
The new lawsuit that shows facial recognition is officially a civil rights issue | mit technology review · 2021
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https://www.detroitnews.com/story/news/politics/2021/07/13/house-panel-hear-michigan-man-wrongfully-accused-facial-recognition/7948908002/
Michigan man wrongfully accused with facial recognition urges congress to act · 2021
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https://www.nytimes.com/2020/12/29/technology/facial-recognition-misidentify-jail.html
Flawed facial recognition leads to arrest and jail for new jersey man - the new york times · 2021
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Does the whole exceed its parts? the effect of ai explanations on complementary team performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., and Weld, D · 2021
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Explaining latent representations with a corpus of examples
Crabbé, J., Qian, Z., Imrie, F., and van der Schaar, M · 2021
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What can ai do for me? evaluating machine learning interpretations in cooperative play
Feng, S. and Boyd-Graber, J · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
Cited alongside, same era.
Twin-systems to explain artificial neural networks using case-based reasoning: Comparative tests of feature-weighting methods in ann-cbr twins for xai
Kenny, E. M. and Keane, M. T · 2019
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The algorithmic automation problem: Prediction, triage, and human effort
Raghu, M., Blumer, K., Corrado, G., Kleinberg, J., Obermeyer, Z., and Mullainathan, S · 2019
Cited alongside, same era.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C · 2019
Cited alongside, same era.
Visualizing deep similarity networks
Stylianou, A., Souvenir, R., and Pless, R · 2019
Cited alongside, same era.
Learning robust global representations by penalizing local predictive power
Wang, H., Ge, S., Lipton, Z., and Xing, E. P · 2019
Cited alongside, same era.
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Deformable protopnet: An interpretable image classifier using deformable prototypes
Donnelly, J., Barnett, A. J., and Chen, C · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Fel, T., Colin, J., Cadène, R., and Serre, T · 2021
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Partial success in closing the gap between human and machine vision
Geirhos, R., Narayanappa, K., Mitzkus, B., Thieringer, T., Bethge, M., Wichmann, F. A., and Brendel, W · 2021
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Human-ai complementarity in hybrid intelligence systems: A structured literature review
Hemmer, P., Schemmer, M., Vössing, M., and Kühl, N · 2021
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Convolutional hough matching networks
Min, J. and Cho, M · 2021
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The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Nguyen, G., Kim, D., and Nguyen, A · 2021
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Interpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., and Zhong, C · 2021
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These do not look like those: An interpretable deep learning model for image recognition
Singh, G. and Yow, K.-C · 2021
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Towards interpretable deep metric learning with structural matching
Zhao, W., Rao, Y., Wang, Z., Lu, J., and Zhou, J · 2021
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https://github.com/jhayes14/adversarial-patch
jhayes14/adversarial-patch: Pytorch implementation of adversarial patch · 2022
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https://github.com/M-Nauta/ProtoTree
M-nauta/prototree: Prototrees: Neural prototype trees for interpretable fine-grained image recognition, published at cvpr2021 · 2022
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https://github.com/pytorch/vision/blob/master/torchvision/models
vision at master · pytorch/vision · 2022
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Deepface-emd: Re-ranking using patch-wise earth mover’s distance improves out-of-distribution face identification
Hai Phan, A. N · 2022
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On the effect of information asymmetry in human-ai teams
Hemmer, P., Schemmer, M., Kühl, N., Vössing, M., and Satzger, G · 2022
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Structure-aware visualization retrieval
Li, H., Wang, Y., Wu, A., Wei, H., and Qu, H · 2022
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Should i follow ai-based advice? measuring appropriate reliance in human-ai decision-making
Schemmer, M., Hemmer, P., Kühl, N., Benz, C., and Satzger, G · 2022
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You complete me: Human-ai teams and complementary expertise
Zhang, Q., Lee, M. L., and Carter, S · 2022
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