Contributing USMA Research Unit(s)
Electrical Engineering and Computer Science, Robotics Research Center
2019 IEEE International Symposium on Technologies for Homeland Security (HST)
A proposal is presented to facilitate machine self-learning of ethical behavior via human-curated training using online human behavioral data such as that found on social media and related sites. The proposed training data set is a mixture of human behavioral data found on social media and related sites that exhibit a wide variety of both ethical and unethical behavior which can help an artificially intelligent machine make ethical decisions during the process of solving real-world problems. The rapid proliferation of artificial intelligence (AI) applications worldwide highlights the need for normativity to protect individual rights, such as privacy, and the promotion of the common good; in other words, ethics. Governance of such widespread applications of AI as speech recognition, facial recognition, tracking of individuals using their personal electronic devices, etc., is needed to prevent abuses of such technologies by corporations or national governments. This paper presents a systemic view of the complexity of using principle-based governance to promote the ethical use of AI without unnecessarily hindering technological innovations needed to advance the state of the art in AI technology.
J. Buenfil, R. Arnold, B. Abruzzo and C. Korpela, "Artificial Intelligence Ethics: Governance through Social Media," 2019 IEEE International Symposium on Technologies for Homeland Security (HST), Woburn, MA, USA, 2019, pp. 1-6, doi: 10.1109/HST47167.2019.9032907.