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CSEET · Economic and Business Environment · AI and Business Environment

A bank's AI loan-approval model rejects a higher share of applicants from one locality, because the historical data it learned from reflected past lending patterns. This challenge is best described as:

This is algorithmic bias arising from biased training data. The model learns patterns from historical lending decisions and reproduces the skew, leading to unfair outcomes for a locality. It is a fairness and ethics challenge, not a matter of data storage location, scale economies or unemployment.

  1. AAlgorithmic bias arising from biased training dataCorrect
  2. BData localisation requirement
  3. CEconomies of scale in computing
  4. DCyclical unemployment

Explanation

When a model learns from historical data that embeds past prejudices or skewed patterns, its outputs reproduce them; this is algorithmic bias. Data localisation concerns where data is stored, and the other options are unrelated to model fairness.

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