What term describes unfair or prejudiced decisions caused by biased training data in AI systems?

What term describes unfair or prejudiced decisions caused by biased training data in AI systems?

• A. Algorithmic injustice
• B. Algorithmic discrimination
• C. Data skewing
• D. Model bias

Answer: (B)

The term that describes unfair or prejudiced decisions caused by biased training data in AI systems is algorithmic discrimination. This concept specifically addresses how AI algorithms can perpetuate or exacerbate existing biases found in the training data. When an AI system is trained on data that reflects societal biases—whether related to gender, race, socioeconomic status, or other factors—the resulting model can make decisions that unfairly disadvantage certain groups. Algorithmic discrimination highlights the ethical and social implications of AI, emphasizing that the technology can replicate and even amplify systemic inequalities if not carefully managed. This term encapsulates the broader concern of ensuring fairness in AI applications and the importance of addressing bias right from the data-gathering stage through to model deployment. Other options, while related to the theme of fairness and bias in AI, do not convey the exact nature of unfair treatment resulting from bias in training data. Algorithms carry a risk of injustice and discrimination, but the direct link is more accurately encapsulated in the concept of algorithmic discrimination itself.

The term that describes unfair or prejudiced decisions caused by biased training data in AI systems is algorithmic discrimination. This concept specifically addresses how AI algorithms can perpetuate or exacerbate existing biases found in the training data. When an AI system is trained on data that reflects societal biases—whether related to gender, race, socioeconomic status, or other factors—the resulting model can make decisions that unfairly disadvantage certain groups.

Algorithmic discrimination highlights the ethical and social implications of AI, emphasizing that the technology can replicate and even amplify systemic inequalities if not carefully managed. This term encapsulates the broader concern of ensuring fairness in AI applications and the importance of addressing bias right from the data-gathering stage through to model deployment.

Other options, while related to the theme of fairness and bias in AI, do not convey the exact nature of unfair treatment resulting from bias in training data. Algorithms carry a risk of injustice and discrimination, but the direct link is more accurately encapsulated in the concept of algorithmic discrimination itself.