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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]
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 Dartmouth proposal: "Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it." [ 7 ] Allen Newell and Herbert A. Simon 's physical symbol system hypothesis: "A physical symbol system has the necessary and sufficient means of general intelligent action."
In an article in AI & Society, Boyles and Joaquin maintain that such AIs would not be that friendly considering the following: the infinite amount of antecedent counterfactual conditions that would have to be programmed into a machine, the difficulty of cashing out the set of moral values—that is, those that are more ideal than the ones human ...
Igor Aleksander suggested 12 principles for artificial consciousness: [34] the brain is a state machine, inner neuron partitioning, conscious and unconscious states, perceptual learning and memory, prediction, the awareness of self, representation of meaning, learning utterances, learning language, will, instinct, and emotion. The aim of AC is ...
Although people subjectively think that more than 20% of their daily conversations touch on morality, close examination of everyday language, using machine learning models, has shown that people do not actually talk much about morality (as measured by moral foundations) often. [56]
AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence (AI) systems. It encompasses machine ethics and AI alignment, which aim to ensure AI systems are moral and beneficial, as well as monitoring AI systems for risks and enhancing their reliability.
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.