Bias and discrimination through AI and algorithms
Objective:
Learners critically examine the supposed neutrality of algorithms and understand how AI systems can reproduce and cement social inequalities.
Content and methods:
The worksheet provides a theoretical introduction to the problems of machine learning and illustrates these using a case study. By analyzing factual texts and working through reflection questions, learners examine mechanisms such as the “black box problem,” proxy variables, and the ethical consequences of algorithmic decisions.
Skills:
- Recognizing bias in technical systems and questioning the objectivity of data.
- Discussing responsibility and accountability in the context of automated processes.
Target group:
Grade 11 and above.
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Target group and level
Grade 11 and above
Subjects
Bias and discrimination through AI and algorithms
Introduction
This worksheet takes a critical look at the supposed neutrality of artificial intelligence (AI). We examine how human biases are incorporated into mathematical models and what social consequences this “digital discrimination” can have.