https://www.insurerbrain.com/w/index.php?action=history&feed=atom&title=Definition%3AAlgorithmic_bias 2026-06-13T10:03:15Z Revision history for this page on the wiki MediaWiki 1.43.8 https://www.insurerbrain.com/w/index.php?title=Definition:Algorithmic_bias&diff=6583&oldid=prev 2026-03-09T16:21:40Z

<p>Bot: Creating new article from JSON</p> <p><b>New page</b></p><div>🤖 &#039;&#039;&#039;Algorithmic bias&#039;&#039;&#039; is the systematic and repeatable skew in the outputs of a computational model that produces unfair outcomes for particular groups of people. In [[Definition:Insurance | insurance]], where [[Definition:Pricing model | pricing]], [[Definition:Underwriting | underwriting]], and [[Definition:Insurance claim | claims]] decisions increasingly rely on [[Definition:Machine learning | machine learning]] and [[Definition:Predictive analytics | predictive analytics]], biased algorithms can lead to unjustified [[Definition:Premium | premium]] disparities, wrongful coverage denials, or inequitable [[Definition:Claims handling | claims settlement]] — often along lines of race, gender, income, or geography.<br /> <br /> 🔍 Bias can enter a model at multiple stages. Training data may reflect historical discrimination — for example, if past [[Definition:Underwriter | underwriting]] decisions unfairly penalized certain zip codes, a model trained on those decisions will learn to replicate the pattern. Feature selection can introduce proxies for [[Definition:Protected class | protected characteristics]]: credit scores, occupation categories, or even consumer purchase data may correlate strongly with race or ethnicity without explicitly including those variables. Even a technically accurate model can produce disparate impact if its predictions are applied without regard for the social context in which they operate.<br /> <br /> ⚖️ [[Definition:Insurance regulator | Regulators]] and consumer advocates are paying close attention. Several U.S. states have issued guidance requiring [[Definition:Insurance carrier | insurers]] to test algorithms for [[Definition:Unfair discrimination | unfair discrimination]] before deployment, and the NAIC&#039;s model bulletin on [[Definition:Artificial intelligence | artificial intelligence]] pushes carriers to document model governance practices. For [[Definition:Insurtech | insurtech]] companies building the next generation of digital products, proactively auditing models for bias is not just a compliance exercise — it is a precondition for earning the trust of [[Definition:Policyholder | policyholders]], distribution partners, and the regulators who grant market access.<br /> <br /> &#039;&#039;&#039;Related concepts&#039;&#039;&#039;<br /> {{Div col|colwidth=20em}}<br /> * [[Definition:Predictive analytics]]<br /> * [[Definition:Artificial intelligence]]<br /> * [[Definition:Unfair discrimination]]<br /> * [[Definition:Machine learning]]<br /> * [[Definition:Insurance regulation]]<br /> * [[Definition:Model risk management]]<br /> {{Div col end}}</div>

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