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Algorithmic Bias and Machine Learning

Nivel C2

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As automated machine learning models assume critical roles in judicial sentencing, credit scoring, and employment screening, algorithmic bias has emerged as a crucial ethical challenge. Neural networks trained on historical datasets inevitably codify, amplify, and perpetuate systemic social prejudices under the veneer of mathematical objectivity, necessitating rigorous algorithmic auditing and fairness metrics.

🧠 Cuestionario de Comprensión

Responde a las preguntas según el texto que has leído.

1. Why do machine learning models perpetuate systemic social prejudices?

A) Because developers intentionally program biased rules B) Because they are trained on historical datasets containing existing prejudices C) Because computer hardware is inherently flawed D) Because mathematical formulas reject objective logic

2. What measure is proposed to combat algorithmic bias?

A) Banning all machine learning applications B) Rigorous algorithmic auditing and fairness metrics C) Replacing neural networks with manual paper records D) Ignoring historical data in software development