From 5f94c5ad0b75a34c2b336d4b13499ec70145ac47 Mon Sep 17 00:00:00 2001 From: annie171261 Date: Wed, 11 Feb 2026 17:02:42 +0000 Subject: [PATCH] image --- .../posts/2026/02/11/algorithmic_bias_credit_scoring.qmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/applied-insights/case-studies/posts/2026/02/11/algorithmic_bias_credit_scoring.qmd b/applied-insights/case-studies/posts/2026/02/11/algorithmic_bias_credit_scoring.qmd index 4e93059b..448512ad 100644 --- a/applied-insights/case-studies/posts/2026/02/11/algorithmic_bias_credit_scoring.qmd +++ b/applied-insights/case-studies/posts/2026/02/11/algorithmic_bias_credit_scoring.qmd @@ -32,7 +32,7 @@ Elsewhere, researchers examined the effects of using large language models to ev Sources of bias in credit scoring data and algorithms are more common than you might think. They can include: -![](images/infogg.png){width=80% fig-align="center"} +![](images/infographic.png){width=80% fig-align="center"} One straightforward way to identify bias in training data is to be aware of the most common types and build algorithms to be less reliant on them when possible. Regularly reviewing the training data is similarly effective because it can catch biases before they have real-life effects.