Philosophy Dictionary of Arguments

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Value alignment: Value alignment in AI refers to ensuring that artificial intelligence systems and their actions align with human values, ethics, and goals. See also Artificial Intelligence, Values, Ethics.
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Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments.

 
Author Concept Summary/Quotes Sources

Tom Griffiths on Value Alignment - Dictionary of Arguments

Brockman I 127
Value alignment/Artificial General Intelligence/values/ethics/Griffiths: Making inferences about what humans want is a prerequisite for solving the AI problem of value alignment - aligning the values of an automated intelligent system with those of a human being. Value alignment is important if we want to ensure that those automated intelligent systems have our best interests at heart. If they can’t infer what we value, there’s no way for them to act in support of those values - and they may well act in ways that contravene them. Value alignment is the subject of a small but growing literature in artificial-intelligence research. One of the tools used for solving this problem is inverse-reinforcement learning. >Reinforcement Learning/Griffiths.


Griffiths, Tom, “The Artificial Use of Human Beings” in: Brockman, John (ed.) 2019. Twenty-Five Ways of Looking at AI. New York: Penguin Press.


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Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments
The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition.
Griffiths, Tom
Brockman I
John Brockman
Possible Minds: Twenty-Five Ways of Looking at AI New York 2019


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Ed. Martin Schulz, access date 2024-04-29
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