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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 problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it. [35] Some open-sourced tools are looking to bring more awareness to AI biases. [36]
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).
If a machine acts as intelligently as a human being, then it is as intelligent as a human being. One criticism of the Turing test is that it only measures the "humanness" of the machine's behavior, rather than the "intelligence" of the behavior. Since human behavior and intelligent behavior are not exactly the same thing, the test fails to ...
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
The Machine Question: Critical Perspectives on AI, Robots, and Ethics is a 2012 nonfiction book by David J. Gunkel that discusses the evolution of the theory of human ethical responsibilities toward non-human things and to what extent intelligent, autonomous machines can be considered to have legitimate moral responsibilities and what legitimate claims to moral consideration they can hold.
Using machine learning to detect bias is called, "conducting an AI audit", where the "auditor" is an algorithm that goes through the AI model and the training data to identify biases. [161] Ensuring that an AI tool such as a classifier is free from bias is more difficult than just removing the sensitive information from its input signals ...
The Alignment Problem: Machine Learning and Human Values is a 2020 non-fiction book by the American writer Brian Christian.It is based on numerous interviews with experts trying to build artificial intelligence systems, particularly machine learning systems, that are aligned with human values.