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   What is fairness in AI?

   • What is fairness? In many ways, bias and fairness in AI are two sides of the same coin. While there is no universally agreed upon definition for fairness, we can broadly define fairness as the absence of prejudice or preference for an individual or group based on their characteristics.

   Understanding Bias and Fairness in AI Systems

   towardsdatascience.com/understanding-bias-and-fairness-in-ai-systems-6f7fbfe267f3

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   Is fairness a metric?

   • It is important to consider both individual and group notions of fairness. Fairness is far more than a metric. Fairness is a workflow of (a) identifying bias (the disparate outcomes of two or more groups); (b) performing root cause analysis to determine whether disparities are justified; and (c) employing a targeted mitigation strategy.

   What does it mean to be fair? Measuring and understanding fairness

   towardsdatascience.com/what-does-it-mean-to-be-fair-measuring-and-understanding-fairness-4ab873245c4c

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   What is fairness law?

   • Law: fairness includes protecting individuals and groups from discrimination or mis-treatment with a focus on prohibiting behaviors, biases and basing decisions on cer-tain protected factors or social group categories.

   What does “fairness” mean for machine learning systems?

   haas.berkeley.edu/wp-content/uploads/What-is-fairness_-EGAL2.pdf

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   What is fairness in machine learning?

   • While there is no universally agreed upon definition for fairness, we can broadly define fairness as the absence of prejudice or preference for an individual or group based on their characteristics. Keeping this in mind, let’s walk through an example of how a machine learning algorithm can run into issues with fairness.

   Understanding Bias and Fairness in AI Systems

   towardsdatascience.com/understanding-bias-and-fairness-in-ai-systems-6f7fbfe267f3

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2. link.springer.com › article › 10A Comprehensive Review of Bias in Deep Learning Models ...

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   May 8, 2024 · The literature review showcases a comprehensive discussion on the critical aspects of bias, fairness, and mitigation strategies in artificial intelligence (AI) and machine learning (ML) systems. Researchers have extensively explored various dimensions of bias, including dataset bias, domain bias, demographic bias, and modality bias, among others.

3. www.forbes.com › sites › forbestechcouncilFrom Bias To Fairness: Trustworthy AI Through Responsible ...

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   May 15, 2024 · This lack of transparency breeds distrust and fuels fears of algorithmic bias. That's why explainable AI is crucial. By demystifying the black box, we gain insights into how decisions...

4. healthitanalytics.com › features › overcomingOvercoming struggles to define algorithmic fairness in healthcare

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   2 days ago · May 28, 2024 - Potential use cases for AI in healthcare continue to grow as the technology rapidly advances. However, the potential for AI to enhance clinical decision support, chronic disease management and population health efforts has been checked by concerns over pitfalls like model bias and fairness. In an era where health systems in the ...

5. Videos

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     Algorithmic Bias and Fairness: Crash Course AI #18

     Dec 13, 2019

     160.6K Views

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     towardsdatascience.com

     Understanding Bias and Fairness in AI Systems

     Mar 24, 2021

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     33:00

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     Bias, Fairness, and their Implications for Machine Learning - Unit8 Talks

     Apr 20, 2022

     274 Views

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6. hogonext.com › how-to-navigate-ai-bias-and-fairnessHow to Navigate AI Bias and Fairness - HogoNext

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   3 days ago · AI bias refers to systematic and unfair discrimination in the outputs of AI algorithms, often arising from the biased data they are trained on or the way algorithms are designed and implemented. Bias can manifest in various forms, including race, gender, socioeconomic status, and more. Importance of Fairness in AI.

7. cdt.org › insights › report-navigating-demographicReport: Navigating Demographic Measurement for Fairness and ...

   cdt.org › insights › report-navigating-demographic

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   May 16, 2024 · Central to this effort is the complex and sensitive task of obtaining demographic data to measure fairness and bias within and surrounding these systems. This report provides methodologies, guidance, and case studies for those undertaking fairness and equity assessments — from approaches that involve more direct access to data to ones that ...

8. spotintelligence.com › 2024/05/14 › bias-mitigationBias Mitigation in Machine Learning [Practical How To Guide]

   spotintelligence.com › 2024/05/14 › bias-mitigation

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   May 14, 2024 · Define Fairness Objectives Establish clear definitions of fairness and equity that align with organizational values and ethical principles. Identify specific fairness objectives relevant to the ML application’s context and domain, such as demographic parity, equal opportunity, or disparate impact mitigation.

9. dl.acm.org › doi › fullHtmlFair Machine Guidance to Enhance Fair Decision Making in ...

   dl.acm.org › doi › fullHtml

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   May 11, 2024 · This concept is part of a debiasing framework called the mental correction process [ 85 ], which includes four key steps to effectively counter bias: awareness of the bias, motivation to correct it, understanding its direction and magnitude of the bias, and adjusting one's responses accordingly.