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== Definitions ==
  
== Definitions ==
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=== Artificial intelligence ===
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'''Accuracy'''
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{{Quote|pertains to an [[AI system]]'s ability to make correct judgements, for example to correctly classify [[information]] into the proper categories, or its ability to make correct predictions, recommendations, or decisions based on [[data]] or [[model]]s. An [[explicit]] and well-formed development and evaluation process can support, [[mitigate]] and correct unintended [[risk]]s from inaccurate predictions. When occasional inaccurate predictions cannot be avoided, it is important that the [[system]] can indicate how likely these [[error]]s are. A high level of accuracy is especially crucial in situations where the [[AI system]] directly affects human lives.<ref>[[Ethics Guidelines for Trustworthy AI]], at 17.</ref>}}
   
 
=== Data ===
  
=== Data ===
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* "[c]loseness of the agreement between the result of a [[measurement]] and the true value of the [[measurand]].<ref>[[Framework for Cyber-Physical Systems]], at 5.</ref>
  
* "[c]loseness of the agreement between the result of a [[measurement]] and the true value of the [[measurand]].<ref>[[Framework for Cyber-Physical Systems]], at 5.</ref>
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{{Quote|(1) [a] [[qualitative assessment]] of [[correctness]] or freedom from [[error]]. (2) A [[quantitative]] [[measure]] of the magnitude of [[error]]. Contrast with [[precision]]. (3) The [[measure]] of an instrument's capability to approach a true or absolute value. It is a function of [[precision]] and [[bias]].<ref>[[Glossary of Computer System Software Development Terminology]].</ref>}}
   
 
== Overview ==
  
== Overview ==
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* [[Consistency]]
  
* [[Consistency]]
 
* [[Correctness]]
  
* [[Correctness]]
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* [[Exactness]]
 
* [[Precision]]
  
* [[Precision]]
 
[[Category:Data]]
  
[[Category:Data]]

Revision as of 19:57, 10 October 2019

Definitions

Artificial intelligence

Accuracy

“ pertains to an AI system's ability to make correct judgements, for example to correctly classify information into the proper categories, or its ability to make correct predictions, recommendations, or decisions based on data or models. An explicit and well-formed development and evaluation process can support, mitigate and correct unintended risks from inaccurate predictions. When occasional inaccurate predictions cannot be avoided, it is important that the system can indicate how likely these errors are. A high level of accuracy is especially crucial in situations where the AI system directly affects human lives.[1] ”

Data

Accuracy is

“ the degree of closeness of a measured or calculated quantity to its actual (true) value. When used in connection with databases, it refers to the correctness of the data contained in that database. ”

General

Accuracy is

  • "conformity to fact, or the degree to which the recorded value represents the 'correct' value."[2]
  • "[t]he degree to which a measured value conforms to true or accepted values."[3]
  • "[c]loseness of the agreement between the result of a measurement and the true value of the measurand.[4]
“ (1) [a] qualitative assessment of correctness or freedom from error. (2) A quantitative measure of the magnitude of error. Contrast with precision. (3) The measure of an instrument's capability to approach a true or absolute value. It is a function of precision and bias.[5] ”

Overview

"Accuracy is a measure of correctness. It is distinguished from precision, which measures exactness."[6]

References

  1. ↑ Ethics Guidelines for Trustworthy AI, at 17.
  2. ↑ Georeferencing: The Geographic Associations of Information, Glossary, at 227.
  3. ↑ Wiki.GIS.com, GIS Glossary (full-text).
  4. ↑ Framework for Cyber-Physical Systems, at 5.
  5. ↑ Glossary of Computer System Software Development Terminology.
  6. ↑ Wiki.GIS.com, GIS Glossary (full-text).

See also

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