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Learn about the ethical challenges and principles of AI, such as machine ethics, robot ethics, algorithmic biases, and existential risks. Explore the topics, applications, and examples of AI ethics from various perspectives and sources.
A draft text of a Recommendation on the Ethics of AI of the UNESCO Ad Hoc Expert Group was issued in September 2020 and included a call for legislative gaps to be filled. [63] UNESCO tabled the international instrument on the ethics of AI for adoption at its General Conference in November 2021; [56] this was subsequently adopted. [64]
AIES is an annual academic conference on the societal and ethical aspects of artificial intelligence, organized by AAAI and ACM. It covers topics such as bias, fairness, regulation, and policy of AI, and features keynote speakers from various fields.
Machine ethics (or machine morality, computational morality, or computational ethics) is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. [1]
The Artificial Intelligence Act is a European Union regulation that establishes a common framework for AI across sectors and risk levels. It covers AI applications from unacceptable to minimal risk, with different obligations and conformity assessments, and creates a European Artificial Intelligence Board.
Learn about the origins, development, and applications of artificial intelligence (AI), a field of computer science that creates machines with intelligence. Explore the subfields, techniques, and challenges of AI research, from reasoning and knowledge representation to ethics and philosophy.
Computer ethics is a branch of practical philosophy that deals with the professional and social conduct of computing professionals. Learn about the origin, development and challenges of computer ethics, as well as its relation to information ethics and artificial intelligence.
Marvin Minsky et al. raised the issue that AI can function as a form of surveillance, with the biases inherent in surveillance, suggesting HI (Humanistic Intelligence) as a way to create a more fair and balanced "human-in-the-loop" AI. [58] Modern complex AI techniques, such as deep learning, are naturally opaque. [59]