Answers.Body: <p>The Ethical Guidelines for Trustworthy AI list four principles:</p>
  <ol>
  <li>Respect for human autonomy</li>
  <li>Prevention of harm</li>
  <li>Fairness</li>
  <li>Explicability</li>
  </ol>
  <p>In fairness, two dimensions are considered.</p>
  <blockquote>
  <p>The substantive dimension implies a commitment to: ensuring equal and
  just distribution of both benefits and costs, and ensuring that
  individuals and groups are free from unfair bias, discrimination and
  stigmatisation. If unfair biases can be avoided, AI systems could even
  increase societal fairness. Equal opportunity in terms of access to
  education, goods, services and technology should also be fostered.
  Moreover, the use of AI systems should never lead to people being
  deceived or unjustifiably impaired in their freedom of choice.
  Additionally, fairness implies that AI practitioners should respect
  the principle of proportionality between means and ends, and consider
  carefully how to balance competing interests and objectives.</p>
  <p>The procedural dimension of fairness entails the ability to contest
  and seek effective redress against decisions made by AI systems and by
  the humans operating them. In order to do so, the entity accountable
  for the decision must be identifiable, and the decision-making
  processes should be explicable.</p>
  </blockquote>
  <p><a href="https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai" rel="nofollow noreferrer">https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-22T16:47:21.143Z
Answers.Text: Thanks for the answer! My question specifically mentioned Fairness in machine learning. How does this definition translate to definitions/applications in machine learning?
Answers.UserDisplayName: Robin van Hoorn
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-22T18:46:52.397Z
Answers.Text: @RobinvanHoorn consider a ML system (e.g. classifier) used in banking to rank possibly bad future/current clients. You don't want such a system to base its predictions solely or mostly on features like gender, age, race, etc.
Answers.UserDisplayName: Luca Anzalone
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-22T19:25:17.653Z
Answers.Text: I understand the abstract definition and how i'd want my system to behave accordingly. But when can i say that my model is fair? Can i test it? Can i give my model incentives to converge to a 'fair' state? How is this incorporated into machine learning?
Answers.UserDisplayName: Robin van Hoorn
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-05-22T15:42:23.637Z
Answers.LastActivityDate: 2023-05-22T15:42:23.637Z
Answers.UserDisplayName: Jaume Oliver Lafont
Body: <p>How does one define the concept of fairness in machine learning? I've seen the term lots of times but never used it myself in research (<a href="https://fairmlbook.org/pdf/fairmlbook.pdf" rel="nofollow noreferrer">1</a>, <a href="https://dl.acm.org/doi/abs/10.1145/3457607?casa_token=L1X2a8FI0SIAAAAA:xVo0kmwoeG6CtxW4faM2R_-5lNj0h6WXPqB1dp4YctON81ZIJNkJ9nOPGB7LNv7otWWhdpGRjs_z" rel="nofollow noreferrer">2</a>). Is there a generally agreed-upon definition of fairness in machine learning? What are the different aspects of fairness? Or the intuition behind the concept of fairness?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-05-21T18:27:16.713Z
Comments.Text: It might be a good idea to provide 1-2 links/quotes that use this term. I've seen it in the context of explainable AI.
Comments.UserDisplayName: nbro
Tags: <machine-learning><definitions>
Title: What is 'fairness' in machine learning?
UserDisplayName: Robin van Hoorn
ThreadText: # What is 'fairness' in machine learning?
  
  (Asked by: username_0 on 2023-05-21)
  
  How does one define the concept of fairness in machine learning? I've seen the term lots of times but never used it myself in research ([1](https://fairmlbook.org/pdf/fairmlbook.pdf), [2](https://dl.acm.org/doi/abs/10.1145/3457607?casa_token=L1X2a8FI0SIAAAAA:xVo0kmwoeG6CtxW4faM2R_-5lNj0h6WXPqB1dp4YctON81ZIJNkJ9nOPGB7LNv7otWWhdpGRjs_z)). Is there a generally agreed-upon definition of fairness in machine learning? What are the different aspects of fairness? Or the intuition behind the concept of fairness?
  
  **Tags:** machine-learning, definitions
  
  > **Comments:**
  > **username_1**: It might be a good idea to provide 1-2 links/quotes that use this term. I've seen it in the context of explainable AI.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2023-05-22. Score: 0
  
  The Ethical Guidelines for Trustworthy AI list four principles:
  
  1. Respect for human autonomy
  2. Prevention of harm
  3. Fairness
  4. Explicability
  
  In fairness, two dimensions are considered.
  
  > The substantive dimension implies a commitment to: ensuring equal and
  > just distribution of both benefits and costs, and ensuring that
  > individuals and groups are free from unfair bias, discrimination and
  > stigmatisation. If unfair biases can be avoided, AI systems could even
  > increase societal fairness. Equal opportunity in terms of access to
  > education, goods, services and technology should also be fostered.
  > Moreover, the use of AI systems should never lead to people being
  > deceived or unjustifiably impaired in their freedom of choice.
  > Additionally, fairness implies that AI practitioners should respect
  > the principle of proportionality between means and ends, and consider
  > carefully how to balance competing interests and objectives.
  >
  > The procedural dimension of fairness entails the ability to contest
  > and seek effective redress against decisions made by AI systems and by
  > the humans operating them. In order to do so, the entity accountable
  > for the decision must be identifiable, and the decision-making
  > processes should be explicable.
  
  <https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai>
  
  > **Comments:**
  > **username_0**: Thanks for the answer! My question specifically mentioned Fairness in machine learning. How does this definition translate to definitions/applications in machine learning?  
  > **username_3**: @username_0 consider a ML system (e.g. classifier) used in banking to rank possibly bad future/current clients. You don't want such a system to base its predictions solely or mostly on features like gender, age, race, etc.  
  > **username_0**: I understand the abstract definition and how i'd want my system to behave accordingly. But when can i say that my model is fair? Can i test it? Can i give my model incentives to converge to a 'fair' state? How is this incorporated into machine learning?  
  
  ---
  
  