The Cambridge Handbook of Artificial Intelligence

By Keith Frankish, W. Ramsey. 2014

434 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “One aspect of explainable systems is related to ethical issues (Bostrom and Yudkowsky, 2014).”
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].”
Towards A Rigorous Science of Interpretable Machine Learning (2017, background): “…performance but also other important criteria such as safety [Otte, 2013, Amodei et al., 2016, Varshney and Alemzadeh, 2016], nondiscrimination [Bostrom and Yudkowsky, 2014, Ruggieri et al., 2010, Hardt et al., 2016], avoiding technical debt [Sculley et al., 2015], or providing the right to…”
Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study (2019, background): “Artificial intelligence (AI) is an umbrella term for computer software consisting of a complex mathematical algorithm that processes input information to produce any specific pre-defined outputs, which lead to relevant outcomes.(1) AI systems, which utilise large datasets, can be designed to enhance”
A Roadmap for a Rigorous Science of Interpretability (2017, background): “…performance but also other important criteria such as safety [Otte, 2013, Amodei et al., 2016, Varshney and Alemzadeh, 2016], nondiscrimination [Bostrom and Yudkowsky, 2014, Ruggieri et al., 2010, Hardt et al., 2016], avoiding technical debt [Sculley et al., 2015], or providing the right to…”
Artificial Intelligence and Journalism (2019, background): “Much like communication, AI is a concept without concrete definition that has been debated for more than 70 years as the hyperbolic promises of the future of AI (see Ekbia, 2008) have been weighed against existing technological capabilities (see Boden, 2016; Frankish & Ramsey, 2014; Kaplan, 2016).”
Artificial intelligence and communication: A Human–Machine Communication research agenda (2019, background): “The term AI is polysemous, encompassing efforts to understand human intelligence by recreating a mind within a machine and to develop technologies that perform tasks associated with some level of human intelligence (Broussard, 2018; Frankish and Ramsey, 2014).”
Artificial Intelligence and the Public Sector—Applications and Challenges (2018, background): “In this context, Bostrom and Yudkowsky (2014) highlight the importance and necessity that AI technology be resilient against adverse manipulation by humans.”
Artificial Intelligence: the global landscape of ethics guidelines (2019, background): “A new wave of machine learning research The Royal Society UK Prof. Association/Society xx-Apr2017 unspecified Citation chaining The Ethics of Code: Developing AI for Business with Five Core Principles The Ethics of Code: Developing AI for Business with Five Core Principles Sage UK Company 27-Jun2017”
Exploring the impact of artificial intelligence on teaching and learning in higher education (2017, background): “For example, the complex set of algorithms and software that power iPhone ’ s Siri is a typical example of artificial intelligence solutions that became part of everyday experiences (Bostrom and Yudkowsky 2011; Luckin 2017).”