When Data Misleads Us: Bias in Datasets and Models

This entry is part 4 of 4 in the series February 2026 - Bias and Blind Spots

Data carries an aura of authority. Numbers feel solid. Charts look persuasive. Models produce outputs with an air of precision. In technical contexts, it is easy to assume that data-driven decisions are inherently fairer, more rational, and less biased than human judgment alone. But data does not speak for itself. Every dataset is the product …
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Search Me, O God: Naming Our Blind Spots (Ps 139:23–24)

This entry is part 3 of 4 in the series February 2026 - Bias and Blind Spots

There is something deeply unsettling about being truly seen. Most of us are comfortable with partial visibility — being known in ways we can manage, understood on our own terms, seen when we are prepared. What we resist is exposure: the uncovering of what we have not noticed, what we have avoided, or what we …
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How Cognitive Bias Creeps Into Code

This entry is part 2 of 4 in the series February 2026 - Bias and Blind Spots

When we talk about bias in technology, the conversation often jumps straight to data. Training sets, sampling issues, skewed distributions — these are familiar and important concerns. But long before data enters the picture, bias has already been at work. It begins in the human mind. Every line of code is written by someone who …
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Bias and Blind Spots — An Invitation to Awareness

This entry is part 1 of 4 in the series February 2026 - Bias and Blind Spots

Most of us like to think of ourselves as fair-minded. We value evidence. We try to be reasonable. We believe we are open to correction. When bias is mentioned, we often imagine it as something obvious and external — a flaw in others, a problem “out there”, something that can be identified and fixed once …
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