THE ETHICS OF ARTIFICIAL INTELLIGENCE

By J. Horvat. 2016

1,256 citations recorded by the source corpus.

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Citations in the corpus

Learning Fair Rule Lists (2019, background): “In fact, understanding these models can be considered as a prerequisite towards quantitatively evaluating other criteria such as fairness, reliability and robustness [7, 20].”
Deep Neural Decision Trees (2018, background): “This is particularly so in applications where there are ethical (Bostrom & Yudkowsky, 2014) or safety concerns and models’ predictions should be explainable in order to verify the correctness of their reasoning process or justify their decisions.”
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories (2021, background): “These are also the parts of the trajectories where more senior data scientists will be involved, assuming higher awareness and training on ethical issues [70] than other more technical, less senior data scientists or team members.”
Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices (2019, background): “Transferring the approach of implicit associations from human subjects to information retrieval systems on natural text was initially suggested by Caliskan et al. (2017), who reported some basic effects of the Word Embedding Association Test (WEAT).”
Ethical Decision Making during Automated Vehicle Crashes (2014, background): “Bostrom and Yudkowsky have argued that opaque systems cannot be inspected, are unpredictable, and can be manipulated easily (49).”
Explainable artificial intelligence (XAI), the goodness criteria and the grasp-ability test (2018, unknown): “1This hypothetical scenario is based on [8]”
Ethics of artificial intelligence and robotics (2020, background): “Classic automation replaced human muscle; digital automation replaces human thought or information-processing – and unlike physical machines digital automation is cheap to duplicate (Bostrom and Yudkovski 2014).”
Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021, background): “…public governance questions raised by AI use in government are also intermingled with complex “ wicked problems ” faced by governments — rising perceptions of threat by societies and digital-era political turbulence, where AI is taking the central stage (Bostrom & Yudkowsky, 2014; Fountain, 2019).”
Integrating Ethics and Career Futures with Technical Learning to Promote AI Literacy for Middle School Students: An Exploratory Study (2022, background): “The main ethical issues related to the impact of AI on human society are the prevention of existential risks to humanity, the impact of AI technologies on our privacy, the impacts of bias in AI, and the development of AI systems that meet our ethical standards (Gordon & Wrenn, 2020).”
Value Alignment or Misalignment - What Will Keep Systems Accountable? (2017, background): “Without the boundaries provided by a game like chess or Go, AI systems are projected to be possible existential threats: with increased power, they could learn to manipulate their own learning and control functions (Bostrom 2014; Bostrom and Yudkowsky 2014).”