Algorithmic Biases: When Technology Learns Our Inequality

   1 December 2025  Leave a Comment on Algorithmic Biases: When Technology Learns Our Inequality  Posted in 4. ‘Algorithmic Biases’ Or ‘The male gaze’

Algorithmic biases are not simply technical glitches; they are mirrors of society. When an AI system is trained on historical data, it learns patterns that reflect past human decisions. If those decisions were discriminatory, the model absorbs them. Algorithms become conduits of inequality, not because they intend harm, but because they cannot reason beyond the world we show them.

A striking example is the COMPAS criminal risk assessment tool in the United States. Designed to evaluate the likelihood of reoffending, it consistently flagged Black defendants as “high risk” at almost twice the rate of white defendants, even when actual recidivism did not support this prediction. This was not a failure of logic but of history: datasets encoding policing patterns, social inequalities, and sentencing practices created a digital echo of systemic prejudice.

Bias is also evident in facial recognition. A 2018 study by Joy Buolamwini and Timnit Gebru demonstrated that commercial systems had extremely low error rates for light-skinned men—under 1%—but misclassified darker-skinned women more than 35% of the time. To a machine trained on predominantly white, male faces, “accuracy” simply meant similarity to the majority. Underrepresentation became technical failure, and technical failure became social harm.

The misconception lies in our uncritical trust. We tend to treat algorithmic outputs as objective, forgetting that neutrality requires intention. Most models optimize for efficiency or prediction accuracy, not fairness. When the goal is prediction, the machine rewards correlations that best fit past outcomes—even if those correlations are rooted in bias.

To create fairer systems, we must rethink how AI is built. Diverse and balanced datasets reduce the risk of exclusion, but diversity is more than numerical representation. It requires understanding context: socioeconomic conditions, cultural variance, and the lived realities of affected communities. Transparency in model design allows society to question and audit decisions rather than passively accept them. Accountability means admitting that fairness is not a default state but a continuous negotiation.

Technology should not be a passive archive of the world as it was—it should help shape the world as it ought to be. The danger of algorithmic bias is not only that machines get things wrong, but that they may convince us those errors are truth.

Angwin, J., Larson, J., Mattu, S. and Kirchner, L., 2016. Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica, 23 May.

Buolamwini, J. and Gebru, T., 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, pp.1–15.

Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P. and Roth, D., 2019. A comparative study of fairness-enhancing interventions in machine learning. Proceedings of the Conference on Fairness, Accountability, and Transparency, pp.329–338. ACM.

Noble, S.U., 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press.

O’Neil, C., 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown Publishing.

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