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In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies.
Bootstrap prediction interval estimation for wind speed forecasting
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Fair inference on outcomes
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Smart meter privacy using a rechargeable battery: Minimizing the rate of information leakage
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On Simpson’s paradox and the sure-thing principle
Blyth, C. R. (1972) · 1972
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Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · 1974
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Sex bias in graduate admissions: Data from Berkeley
Bickel, P. J., Hammel, E. A., and O’Connell, J. W. (1975) · 1975
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On the robustness of information-theoretic privacy measures and mechanisms
Diaz, M., Wang, H., Calmon, F. P., and Sankar, L. (2019) · 1978
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Algorithm as 136: A k-means clustering algorithm
Hartigan, J. A., and Wong, M. A. (1979) · 1979
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The central role of the propensity score in observational studies for causal effects
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Probabilistic encryption
Goldwasser, S., and Micali, S. (1984) · 1984
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Principal component analysis
Wold, S., Esbensen, K., and Geladi, P. (1987) · 1987
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The pyramid of corporate social responsibility: Toward the moral management of organizational stakeholders
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Nobel lecture: The economic way of looking at behavior
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Extracting tree-structured representations of trained networks
Craven, M., and Shavlik, J. W. (1996) · 1996
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Estimating prediction intervals for artificial neural networks
Ungar, L. H., De Veaux, R. D., and Rosengarten, E. (1996) · 1996
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Confidence intervals and prediction intervals for feed-forward neural networks.
Dybowski, R., and Roberts, S. J. (2001) · 2001
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Neural network modeling with confidence bounds: a case study on the solder paste deposition process
Ho, S., Xie, M., Tang, L., Xu, K., and Goh, T. (2001) · 2001
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Speed violation survey of the new jersey turnpike: Final report.
Lange, J. E., Blackman, K. O., and Johnson, M. B. (2001) · 2001
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Race and speeding citations: Comparing speeding citations issued by air traffic officers with those issued by ground traffic officers
McConnell, E. H., and Scheidegger, A. R. (2001) · 2001
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k-anonymity: A model for protecting privacy
Sweeney, L. (2002) · 2002
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Efficient estimation of average treatment effects using the estimated propensity score
Hirano, K., Imbens, G. W., and Ridder, G. (2003) · 2003
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Adversarial classification
Dalvi, N., Domingos, P., Sanghai, S., and Verma, D. (2004) · 2004
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Can machine learning be secure?
Barreno, M., Nelson, B., Sears, R., Joseph, A. D., and Tygar, J. D. (2006) · 2006
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006) · 2006
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Zio, E. (2006) · 2006
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A collection of definitions of intelligence
Legg, S., Hutter, M., et al. (2007) · 2007
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Differential privacy: A survey of results
Dwork, C. (2008) · 2008
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Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J. V., Stephan, D. A., Nelson, S. F., and Craig, D. W. (2008) · 2008
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Visualizing data using t-sne
Maaten, L. v. d., and Hinton, G. (2008) · 2008
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Robust de-anonymization of large sparse datasets
Narayanan, A., and Shmatikov, V. (2008) · 2008
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Cyberbullying: Its nature and impact in secondary school pupils
Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., and Tippett, N. (2008) · 2008
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Visualizing higher-layer features of a deep network
Erhan, D., Bengio, Y., Courville, A., and Vincent, P. (2009) · 2009
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Fully homomorphic encryption using ideal lattices
Gentry, C. (2009) · 2009
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Effectiveness of cyber bullying prevention strategies: A study on students’ perspectives.
Kraft, E. M., and Wang, J. (2009) · 2009
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Prediction of indoor temperature and relative humidity using neural network models: model comparison
Lu, T., and Viljanen, M. (2009) · 2009
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Causality
Pearl, J. (2009) · 2009
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Testing for discrimination and the problem of” included variable bias”,”
Ayres, I. (2010) · 2010
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Three naive Bayes approaches for discrimination-free classification
Calders, T., and Verwer, S. (2010) · 2010
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You are known by how you vlog: Personality impressions and nonverbal behavior in youtube.
Biel, J.-I., Aran, O., and Gatica-Perez, D. (2011) · 2011
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Limiting the spread of misinformation in social networks
Budak, C., Agrawal, D., and El Abbadi, A. (2011) · 2011
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Finding deceptive opinion spam by any stretch of the imagination.
Ott, M., Choi, Y., Cardie, C., and Hancock, J. T. (2011) · 2011
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Common sense reasoning for detection, prevention, and mitigation of cyberbullying
Dinakar, K., Jones, B., Havasi, C., Lieberman, H., and Picard, R. (2012) · 2012
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. (2012) · 2012
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Syntactic stylometry for deception detection
Feng, S., Banerjee, R., and Choi, Y. (2012) · 2012
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Fairness-aware classifier with prejudice remover regularizer
Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J. (2012) · 2012
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Risk factors for involvement in cyber bullying: Victims, bullies and bully–victims
Mishna, F., Khoury-Kassabri, M., Gadalla, T., and Daciuk, J. (2012) · 2012
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Containment of misinformation spread in online social networks
Nguyen, N. P., Yan, G., Thai, M. T., and Eidenbenz, S. (2012) · 2012
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Learning from bullying traces in social media
Xu, J.-M., Jun, K.-S., Zhu, X., and Bellmore, A. (2012) · 2012
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Controlling attribute effect in linear regression
Calders, T., Karim, A., Kamiran, F., Ali, W., and Zhang, X. (2013) · 2013
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Improving cyberbullying detection with user context
Dadvar, M., Trieschnigg, D., Ordelman, R., and de Jong, F. (2013) · 2013
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Detecting cyberbullying: query terms and techniques
Kontostathis, A., Reynolds, K., Garron, A., and Edwards, L. (2013) · 2013
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Disinformation: Former Spy Chief Reveals Secret Strategy for Undermining Freedom, Attacking Religion, and Promoting Terrorism
Pacepa, I. M., and Rychlak, R. J. (2013) · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps.
Simonyan, K., Vedaldi, A., and Zisserman, A. (2013) · 2013
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Probabilistic forecasting of wind power generation using extreme learning machine
Wan, C., Xu, Z., Pinson, P., Dong, Z. Y., and Wong, K. P. (2013) · 2013
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The algorithmic foundations of differential privacy.
Dwork, C., Roth, A., et al. (2014) · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Erlingsson, Ú., Pihur, V., and Korolova, A. (2014) · 2014
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., and Ristenpart, T. (2014) · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
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Improving the quality of prediction intervals through optimal aggregation
Hosen, M. A., Khosravi, A., Nahavandi, S., and Creighton, D. (2014) · 2014
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Cyberbullying detection using social and textual analysis
Huang, Q., Singh, V. K., and Atrey, P. K. (2014) · 2014
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Extremal mechanisms for local differential privacy
Kairouz, P., Oh, S., and Viswanath, P. (2014) · 2014
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Constructing optimal prediction intervals by using neural networks and bootstrap method
Khosravi, A., Nahavandi, S., Srinivasan, D., and Khosravi, R. (2014) · 2014
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Particle swarm optimization for construction of neural network-based prediction intervals
Quan, H., Srinivasan, D., and Khosravi, A. (2014) · 2014
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An advanced approach for construction of optimal wind power prediction intervals
Zhang, G., Wu, Y., Wong, K. P., Xu, Z., Dong, Z. Y., and Iu, H. H.-C. (2014) · 2014
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The tiger mom tax: Asians are nearly twice as likely to get a higher price from princeton review
Angwin, J., and Larson, J. (2015) · 2015
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Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers
Ateniese, G., Mancini, L. V., Spognardi, A., Villani, A., Vitali, D., and Felici, G. (2015) · 2015
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Cyberbullying among young adults in malaysia: The roles of gender, age and internet frequency
Balakrishnan, V. (2015) · 2015
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https://www.washingtonpost.com/news/the-intersect/wp/2015/07/06/googles-algorithm-shows-prestigious-job-ads-to-men-but-not-to-women-heres-why-that-should-worry-you/
Carpenter, J. (2015) · 2015
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Real-time prediction intervals for intra-hour dni forecasts
Chu, Y., Li, M., Pedro, H. T., and Coimbra, C. F. (2015) · 2015
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Computational fact checking from knowledge networks
Ciampaglia, G. L., Shiralkar, P., Rocha, L. M., Bollen, J., Menczer, F., and Flammini, A. (2015) · 2015
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Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination
Datta, A., Tschantz, M. C., and Datta, A. (2015) · 2015
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Certifying and removing disparate impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S. (2015) · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T. (2015) · 2015
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https://www.usatoday.com/story/tech/2015/07/01/google-apologizes-after-photos-identify-black-people-as-gorillas/29567465/
Guynn, J. (2015) · 2015
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Inceptionism: Going deeper into neural networks.
Mordvintsev, A., Olah, C., and Tyka, M. (2015) · 2015
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Honestly looks to combat cyberbullying on ios, android
Shaul, B. (2015) · 2015
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Cybercrime detection in online communications: The experimental case of cyberbullying detection in the twitter network
Al-garadi, M. A., Varathan, K. D., and Ravana, S. D. (2016) · 2016
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Machine bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L. (2016) · 2016
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Designing cyberbullying mitigation and prevention solutions through participatory design with teenagers
Ashktorab, Z., and Vitak, J. (2016) · 2016
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How to be fair and diverse?.
Celis, L. E., Deshpande, A., Kathuria, T., and Vishnoi, N. K. (2016) · 2016
Cited alongside, same era.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., and Wernsing, J. (2016) · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N. (2016) · 2016
Cited alongside, same era.
Impartial predictive modeling: Ensuring fairness in arbitrary models.
Johnson, K. D., Foster, D. P., and Stine, R. A. (2016) · 2016
Cited alongside, same era.
Comparison of methods used for quantifying prediction interval in artificial neural network hydrologic models
Kasiviswanathan, K., and Sudheer, K. (2016) · 2016
Cited alongside, same era.
Examples are not enough, learn to criticize! criticism for interpretability
Responsible data science
Getoor, L. (2019) · 2019
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Fairness-aware ranking in search & recommendation systems with application to linkedin talent search
Geyik, S. C., Ambler, S., and Kenthapadi, K. (2019) · 2019
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Less than you think: Prevalence and predictors of fake news dissemination on facebook
Guess, A., Nagler, J., and Tucker, J. (2019) · 2019
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Data dignity at radicalxchange - the art of research
Hart, V. (2019) · 2019
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Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., and Müller, H. (2019) · 2019
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Responsible data science
Jagadish, V. H. (2019) · 2019
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Kim, B., Khanna, R., and Koyejo, O. O. (2016) · 2016
Cited alongside, same era.
Prediction interval based on type-2 fuzzy systems for wind power generation and loads in microgrid control design
Marín, L. G., Valencia, F., and Sáez, D. (2016) · 2016
Cited alongside, same era.
“Why should I trust you?” explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
Cited alongside, same era.
Bullyblocker: Towards the identification of cyberbullying in social networking sites
Silva, Y. N., Rich, C., and Hall, D. (2016) · 2016
Cited alongside, same era.
Stealing machine learning models via prediction apis
Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., and Ristenpart, T. (2016) · 2016
Cited alongside, same era.
Hierarchical attention networks for document classification
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., and Hovy, E. (2016) · 2016
Cited alongside, same era.
A causal framework for discovering and removing direct and indirect discrimination.
Zhang, L., Wu, Y., and Wu, X. (2016) · 2016
Cited alongside, same era.
Cross-domain failures of fake news detection
Janicka, M., Pszona, M., and Wawer, A. (2019) · 2019
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Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads
Lambrecht, A., and Tucker, C. (2019) · 2019
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Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms.
Lee, N. T., Resnick, P., and Barton, G. (2019) · 2019
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Understanding artificial intelligence ethics and safety.
Leslie, D. (2019) · 2019
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Generative counterfactual introspection for explainable deep learning.
Liu, S., Kailkhura, B., Loveland, D., and Han, Y. (2019) · 2019
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EPIC: efficient private image classification (or: Learning from the masters)
Makri, E., Rotaru, D., Smart, N. P., and Vercauteren, F. (2019) · 2019
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Mobile sensor data anonymization.
Malekzadeh, M., Clegg, R. G., Cavallaro, A., and Haddadi, H. (2019) · 2019
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A survey on bias and fairness in machine learning.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2019) · 2019
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People, power and technology: The tech workers’ view
Miller C, C. R. (2019) · 2019
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Model cards for model reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., and Gebru, T. (2019) · 2019
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Explaining visual models by causal attribution.
Parafita, Á., and Vitrià, J. (2019) · 2019
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The seven tools of causal inference, with reflections on machine learning
Pearl, J. (2019) · 2019
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Data-driven generation of synthetic load datasets preserving spatio-temporal features
Pinceti, A., Kosut, O., and Sankar, L. (2019) · 2019
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User behavior modelling for fake information mitigation on social web
Rajabi, Z., Shehu, A., and Purohit, H. (2019) · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, C. (2019) · 2019
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Fairness gan: Generating datasets with fairness properties using a generative adversarial network
Sattigeri, P., Hoffman, S. C., Chenthamarakshan, V., and Varshney, K. R. (2019) · 2019
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Causality for machine learning.
Schölkopf, B. (2019) · 2019
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Fairness and abstraction in sociotechnical systems
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., and Vertesi, J. (2019) · 2019
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dEFEND: Explainable fake news detection
Shu, K., Cui, L., Wang, S., Lee, D., and Liu, H. (2019) · 2019
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Beyond news contents: The role of social context for fake news detection
Shu, K., Wang, S., and Liu, H. (2019) · 2019
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Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness
Suter, R., Miladinovic, D., Schölkopf, B., and Bauer, S. (2019) · 2019
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Trustworthy machine learning and artificial intelligence
Varshney, K. R. (2019) · 2019
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Achieving causal fairness through generative adversarial networks.
Xu, D., Wu, Y., Yuan, S., Zhang, L., and Wu, X. (2019) · 2019
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Toward inclusive tech policy design: a method for underrepresented voices to strengthen tech policy documents
Young, M., Magassa, L., and Friedman, B. (2019) · 2019
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Adversarial examples: Attacks and defenses for deep learning
Yuan, X., He, P., Zhu, Q., and Li, X. (2019) · 2019
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The grey hoodie project: Big tobacco, big tech, and the threat on academic integrity.
Abdalla, M., and Abdalla, M. (2020) · 2020
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https://www.govtech.com/em/safety/Stanfords-Vaccine-Algorithm-Left-Frontline-Workers-at-Back-of-Line-.html
Asimov, N. (2020) · 2020
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A survey on privacy in social media: Identification, mitigation, and applications
Beigi, G., and Liu, H. (2020) · 2020
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Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty.
Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melancon, G. G., Krishnan, R., Stanley, J., Tickoo, O., et al. (2020) · 2020
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Disinformation in the online information ecosystem: Detection, mitigation and challenges.
Bhattacharjee, A., Shu, K., Gao, M., and Liu, H. (2020) · 2020
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A review of privacy-preserving techniques for deep learning
Boulemtafes, A., Derhab, A., and Challal, Y. (2020) · 2020
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Does facebook use sensitive data for advertising purposes?
Cabañas, J. G., Cuevas, Á., Arrate, A., and Cuevas, R. (2020) · 2020
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Operationalizing AI ethics principles
Canca, C. (2020) · 2020
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Fairness in machine learning: A survey.
Caton, S., and Haas, C. (2020) · 2020
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Henin: Learning heterogeneous neural interaction networks for explainable cyberbullying detection on social media.
Chen, H.-Y., and Li, C.-T. (2020) · 2020
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https://www.bloomberg.com/opinion/articles/2020-11-23/vaccine-distribution-shouldn-t-be-fair
Cowen, T. (2020) · 2020
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https://www.ibm.com/blogs/watson/2020/12/how-ibm-is-advancing-ai-governance-to-help-clients-build-trust-and-transparency/
Dobrin, S. (2020) · 2020
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AI and algorithmic bias: Source, detection, mitigation and implications.
Fu, R., Huang, Y., and Singh, P. V. (2020) · 2020
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https://www.martechadvisor.com/articles/data-governance/data-dignity-for-better-data-privacy/
Grover, V. (2020) · 2020
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A survey of learning causality with data: Problems and methods
Guo, R., Cheng, L., Li, J., Hahn, P. R., and Liu, H. (2020) · 2020
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Experiences with improving the transparency of AI models and services
Hind, M., Houde, S., Martino, J., Mojsilovic, A., Piorkowski, D., Richards, J., and Varshney, K. R. (2020) · 2020
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Identifying and correcting label bias in machine learning
Jiang, H., and Nachum, O. (2020) · 2020
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This company uses AI to outwit malicious AI
Knight, W. (2020) · 2020
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Privacy and utility preserving sensor-data transformations.
Malekzadeh, M., Clegg, R. G., Cavallaro, A., and Haddadi, H. (2020) · 2020
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Six steps to bridge the responsible AI gap
Mills, S., Baltassis, E., Santinelli, M., Carlisi, C., Duranton, S., and Gallego, A. (2020) · 2020
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Privacy in deep learning: A survey.
Mirshghallah, F., Taram, M., Vepakomma, P., Singh, A., Raskar, R., and Esmaeilzadeh, H. (2020) · 2020
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Moral framing and ideological bias of news
Mokhberian, N., Abeliuk, A., Cummings, P., and Lerman, K. (2020) · 2020
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Interpretable Machine Learning
Molnar, C. (2020) · 2020
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Causal interpretability for machine learning-problems, methods and evaluation
Moraffah, R., Karami, M., Guo, R., Raglin, A., and Liu, H. (2020) · 2020
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An empirical characterization of fair machine learning for clinical risk prediction.
Pfohl, S. R., Foryciarz, A., and Shah, N. H. (2020) · 2020
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Google showed us the danger of letting corporations lead AI research
Rivero, N. (2020) · 2020
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Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., and Liu, H. (2020) · 2020
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Leveraging multi-source weak social supervision for early detection of fake news.
Shu, K., Zheng, G., Li, Y., Mukherjee, S., Awadallah, A. H., Ruston, S., and Liu, H. (2020) · 2020
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https://www.kdnuggets.com/how-machine-learning-works-for-social-good.html/
Siegel, E. (2020) · 2020
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Slack, D., Hilgard, S., Jia, E., Singh, S., and Lakkaraju, H. (2020) · 2020
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Evaluation of uncertainty quantification in deep learning
Ståhl, N., Falkman, G., Karlsson, A., and Mathiason, G. (2020) · 2020
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Does fair ranking improve minority outcomes? understanding the interplay of human and algorithmic biases in online hiring.
Sühr, T., Hilgard, S., and Lakkaraju, H. (2020) · 2020
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Trustworthy artificial intelligence.
Thiebes, S., Lins, S., and Sunyaev, A. (2020) · 2020
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A survey on explainable artificial intelligence (xai): Toward medical xai.
Tjoa, E., and Guan, C. (2020) · 2020
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Optimized score transformation for fair classification
Wei, D., Ramamurthy, K. N., and Calmon, F. d. P. (2020) · 2020
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A causal inference method for reducing gender bias in word embedding relations.
Yang, Z., and Feng, J. (2020) · 2020
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A survey on causal inference.
Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., and Zhang, A. (2020) · 2020
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What is transparency in AI?
Yeo, C. (2020) · 2020
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https://www.statnews.com/2021/03/08/a-pandemic-expert-weighs-in-on-the-long-road-ahead-for-covid-19-vaccine-distribution/
Branswell, H. (2021) · 2021
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Causal understanding of fake news dissemination on social media
Cheng, L., Guo, R., Shu, K., and Liu, H. (2021) · 2021
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Modeling temporal patterns of cyberbullying detection with hierarchical attention networks
Cheng, L., Guo, R., Silva, Y. N., Hall, D., and Liu, H. (2021) · 2021
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Mitigating bias in session-based cyberbullying detection: A non-compromising approach
Cheng, L., Mosallanezhad, A., Silva, Y. N., Hall, D. L., and Liu, H. (2021) · 2021
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Improving cyberbullying detection with user interaction
Ge, S., Cheng, L., and Liu, H. (2021) · 2021
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Facebook algorithm shows gender bias in job ads, study finds
Horwitz, J. (2021) · 2021
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https://bernardmarr.com/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/
Marr, B. (2021) · 2021
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‘this is bigger than just timnit’: How google tried to silence a critic and ignited a movement
Schwab, K. (2021) · 2021
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Trustworthy AI
Singh, R., Vatsa, M., and Ratha, N. (2021) · 2021
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