Switch to: References

Add citations

You must login to add citations.
  1. AI, Explainability and Public Reason: The Argument from the Limitations of the Human Mind.Jocelyn Maclure - 2021 - Minds and Machines 31 (3):421-438.
    Machine learning-based AI algorithms lack transparency. In this article, I offer an interpretation of AI’s explainability problem and highlight its ethical saliency. I try to make the case for the legal enforcement of a strong explainability requirement: human organizations which decide to automate decision-making should be legally obliged to demonstrate the capacity to explain and justify the algorithmic decisions that have an impact on the wellbeing, rights, and opportunities of those affected by the decisions. This legal duty can be derived (...)
    Download  
    Translate
     
     
    Export citation  
     
    Bookmark  
  • AI Ethics and the Banality of Evil.Payman Tajalli - 2021 - Ethics and Information Technology 23 (3):447-454.
    In this paper, I draw on Hannah Arendt’s notion of ‘banality of evil’ to argue that as long as AI systems are designed to follow codes of ethics or particular normative ethical theories chosen by us and programmed in them, they are Eichmanns destined to commit evil. Since intelligence alone is not sufficient for ethical decision making, rather than strive to program AI to determine the right ethical decision based on some ethical theory or criteria, AI should be concerned with (...)
    Download  
     
    Export citation  
     
    Bookmark  
  • The Explanation Game: A Formal Framework for Interpretable Machine Learning.David S. Watson & Luciano Floridi - 2021 - Synthese 198 (10):9211-9242.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...)
    Download  
     
    Export citation  
     
    Bookmark  
  • The Explanation Game: A Formal Framework for Interpretable Machine Learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–⁠32.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...)
    Download  
     
    Export citation  
     
    Bookmark   3 citations  
  • Anthropomorphism in AI.Arleen Salles, Kathinka Evers & Michele Farisco - 2020 - American Journal of Bioethics Neuroscience 11 (2):88-95.
    AI research is growing rapidly raising various ethical issues related to safety, risks, and other effects widely discussed in the literature. We believe that in order to adequately address those issues and engage in a productive normative discussion it is necessary to examine key concepts and categories. One such category is anthropomorphism. It is a well-known fact that AI’s functionalities and innovations are often anthropomorphized. The general public’s anthropomorphic attitudes and some of their ethical consequences have been widely discussed in (...)
    Download  
     
    Export citation  
     
    Bookmark   1 citation