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Bias in the Machine: How to Find Blind Spots in LLMs

Mon., 6. October 2025 | 4 min read
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Large language models (LLMs) serve as trusted digital assistants until hidden biases appear from the cracks. These biases can lead to fines and penalties due to broken regulations. A company’s reputation can tank due to the lack of trust from stakeholders and users. Auditing bias is not just a task for compliance, it also ensures fairness, accountability, and trust in every AI interaction. IT leaders and AI teams in SMEs must learn to detect bias in data and models so that their models will perform well and with integrity.

Examining the Data First

Start by examining the data before a model is used in production. A strong foundation is non-negotiable when using or creating AI models. These data auditing techniques can be used to audit your data:

  • Exploratory Data Analysis (EDA). Summarize and visualize your dataset using histograms, boxplots, scatterplots, or summary statistics to …

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