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Decisions made by machine learning systems have increasing influence on the world, yet it is common for machine learning algorithms to assume that no such influence exists.
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The Filter Bubble: What the Internet Is Hiding from You
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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., and Konstan, J. A · 2014
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Game Theory Through Examples
Prisner, E · 2014
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Exposure to ideologically diverse news and opinion on Facebook
Bakshy, E., Messing, S., and Adamic, L. A · 2015
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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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Knowledge does not protect against illusory truth
Fazio, L., Brashier, N., Keith Payne, B., and Marsh, E · 2015
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Filter bubbles, echo chambers, and online news consumption
Flaxman, S. and Goel, S · 2015
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Confirmation bias: Roles of search engines and search contexts
Kayhan, V · 2015
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Learning to learn by gradient descent by gradient descent
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Your echo chamber is destroying democracy, 2016
El-Bermawy, M. M · 2016
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Social media and fake news in the 2016 election
Allcott, H. and Gentzkow, M · 2017
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The alignment problem for bayesian history-based reinforcement learners
Everitt, T. and Hutter, M · 2018
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Learning an optimizer for image deconvolution
Gong, D., Zhang, Z., Shi, Q., van den Hengel, A., Shen, C., and Zhang, Y · 2018
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Scalable agent alignment via reward modeling: a research direction
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Stochastic hyperparameter optimization through hypernetworks
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Algorithms of Oppression: How Search Engines Reinforce Racism
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Building safe artificial intelligence: specification, robustness, and assurance, 2018
Ortega, P. A., Maini, V., et al · 2018
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Armstrong, S. and O’Rorke, X · 2017
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Indifference methods for managing agent rewards
Armstrong, S. and O’Rourke, X · 2017
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Deep reinforcement learning from human preferences, 2017
Christiano, P., Leike, J., Brown, T. B., Martic, M., Legg, S., and Amodei, D · 2017
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Filter bubbles and fake news
DiFranzo, D. and Gloria-Garcia, K · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Helping populism win? Social media use, filter bubbles, and support for populist presidential candidates in the 2016 us election campaign
Groshek, J. and Koc-Michalska, K · 2017
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Meta-learning for semi-supervised few-shot classification
Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., and Zemel, R. S · 2018
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The myth of the online echo chamber, 2018
Robson, D · 2018
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Bandit learning with positive externalities
Shah, V., Blanchet, J., and Johari, R · 2018
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The spread of low-credibility content by social bots
Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., and Menczer, F · 2018
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Filter bubble, 2018
Techopedia · 2018
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A strongly asymptotically optimal agent in general environments
Cohen, M. K., Catt, E., and Hutter, M · 2019
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Everitt, T. and Hutter, M · 2019
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Understanding agent incentives using causal influence diagrams. part i: Single action settings, 2019
Everitt, T., Ortega, P. A., Barnes, E., and Legg, S · 2019
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A research agenda: Dynamic models to defend against correlated attacks
Goodfellow, I. J · 2019
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Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions
MacKay, M., Vicol, P., Lorraine, J., Duvenaud, D., and Grosse, R · 2019
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Learning unsupervised learning rules
Metz, L., Maheswaranathan, N., Cheung, B., and Sohl-Dickstein, J · 2019
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Prior exposure increases perceived accuracy of fake news
Pennycook, G., Cannon, T. D., and Rand, D. G · 2019
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Meta-learners’ learning dynamics are unlike learners’
Rabinowitz, N. C · 2019
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Confirmation bias — Wikipedia, the free encyclopedia, 2018
Wikipedia contributors · 2019
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The incentives that shape behaviour
Carey, R., Langlois, E., Everitt, T., and Legg, S · 2020
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