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The Butterfly Effect, a concept originating from chaos theory, underscores how small changes can have significant and unpredictable impacts on complex systems.
Deterministic nonperiodic flow
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Addressing the curse of imbalanced training sets: one-sided selection
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SMOTE: Synthetic Minority Over-sampling Technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · 2002
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Learning from imbalanced data
He, H., & Garcia, E. A. (2009) · 2009
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The Filter Bubble: What the Internet Is Hiding from You
Pariser, E. (2011) · 2011
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Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · 2012
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Exploring the filter bubble: The effect of using recommender systems on content diversity
Nguyen, T. T., Hui, P.-M., Harper, F. M., Terveen, L., & Konstan, J. A. (2014) · 2014
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Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016) · 2016
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Big data’s disparate impact
Barocas, S., & Selbst, A. D. (2016) · 2016
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On the (im) possibility of fairness
Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2016) · 2016
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Deep learning
Goodfellow, I., Bengio, Y., & Courville, A. (2016) · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., & Srebro, N. (2016) · 2016
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To predict and serve?
Lum, K., & Isaac, W. (2016) · 2016
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The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · 2016
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Why should I trust you?: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · 2016
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2017) · 2017
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On detecting adversarial perturbations
Metzen, J. H., Genewein, T., Fischer, V., & Bischoff, B. (2017) · 2017
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Ensemble adversarial training: Attacks and defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2017) · 2017
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The space of transferable adversarial examples
Tramèr, F., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2017) · 2017
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Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Zafar, M. B., Valera, I., Gomez-Rodriguez, M., & Gummadi, K. P. (2017) · 2017
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Certified adversarial robustness via randomized smoothing
Cohen, J. M., Rosenfeld, E., & Kolter, J. Z. (2019) · 2019
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Model cards for model reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., … & Gebru, T. (2019, January) · 2019
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Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · 2019
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The butterfly effect in knowledge graphs: Predicting the impact of changes in the evolving web of data
Pernischová, R. (2019, October) · 2019
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Gender Bias in Contextualized Word Embeddings
Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordonez, V., & Chang, K. W. (2019) · 2019
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Fairness in criminal justice risk assessments: The state of the art
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J., & Gebru, T. (2018) · 2018
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Amazon scraps secret AI recruiting tool that showed bias against women
Dastin, J. (2018) · 2018
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The accuracy, fairness, and limits of predicting recidivism
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Runaway feedback loops in predictive policing
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GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
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Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning
Grgić-Hlača, N., Zafar, M. B., Gummadi, K. P., & Weller, A. (2018) · 2018
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Mitigating bias in algorithmic hiring: Evaluating claims and practices
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020, January) · 2020
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No classification without representation: Assessing geodiversity issues in open data sets for the developing world
Shankar, S., Halpern, Y., Breck, E., Atwood, J., Wilson, J., & Sculley, D. (2020) · 2020
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Fairness through robustness: Investigating robustness disparity in deep learning
Nanda, V., Dooley, S., Singla, S., Feizi, S., & Dickerson, J. P. (2021, March) · 2021
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Robust fairness under covariate shift
Rezaei, A., Liu, A., Memarrast, O., & Ziebart, B. D. (2021, May) · 2021
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Słowik, A., & Bottou, L. (2021) · 2021
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Ethical and social risks of harm from language models
Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P. S., … & Gabriel, I. (2021) · 2021
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Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models
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Calibrated Chaos: Variance Between Runs of Neural Network Training is Harmless and Inevitable
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Generative Agents: Interactive Simulacra of Human Behavior
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