<span class="mw-page-title-main">Fairness (machine learning)</span>

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Fairness (machine learning)

<p><b>Fairness</b> in <a href="/en/Machine_Learning" title="Machine Learning" class="mw-redirect wl">machine learning</a> refers to the various attempts at correcting <a href="/en/Algorithmic_bias" title="Algorithmic bias" class="wl">algorithmic bias</a> in automated decision processes based on machine learning models. Decisions made by computers after a machine-learning process may be considered unfair if they were based on <a href="/en/Dependent_and_independent_variables" title="Dependent and independent variables" class="wl">variables</a> considered <b>sensitive</b>. Examples of these kinds of variable include <a href="/en/Gender" title="Gender" class="wl">gender</a>, <a href="/en/Ethnicity" title="Ethnicity" class="mw-redirect wl">ethnicity</a>, <a href="/en/Sexual_orientation" title="Sexual orientation" class="wl">sexual orientation</a>, <a href="/en/Disability" title="Disability" class="wl">disability</a> and more. As it is the case with many ethical concepts, definitions of fairness and bias are always controversial. In general, fairness and bias are considered relevant when the decision process impacts people's lives. In <a href="/en/Machine_learning" title="Machine learning" class="wl">machine learning</a>, the problem of <a href="/en/Algorithmic_bias" title="Algorithmic bias" class="wl">algorithmic bias</a> is well known and well studied. Outcomes may be skewed by a range of factors and thus might be considered unfair with respect to certain groups or individuals. An example would be the way social media sites deliver personalized news to consumers.</p>

<div class="shortdescription nomobile noexcerpt noprint searchaux" style="display:none" about="#mwt1">Measurement of algorithmic bias</div>
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Fairness in machine learning refers to the various attempts at correcting algorithmic bias in automated decision processes based on machine learning models. Decisions made by computers after a machine-learning process may be considered unfair if they were based on variables considered sensitive. Examples of these kinds of variable include gender, ethnicity, sexual orientation, disability and more. As it is the case with many ethical concepts, definitions of fairness and bias are always controversial. In general, fairness and bias are considered relevant when the decision process impacts people's lives. In machine learning, the problem of algorithmic bias is well known and well studied. Outcomes may be skewed by a range of factors and thus might be considered unfair with respect to certain groups or individuals. An example would be the way social media sites deliver personalized news to consumers. 