When data is gathered, it may reflect socially constructed biases, or contain inaccuracies, errors and mistakes.

Data sets used by AI systems (both for training and operation) may suffer from the inclusion of inadvertent historic bias, incompleteness and bad governance models.

The continuation of such biases could lead to (in)direct discrimination.

Harm can also result from the intentional exploitation of (consumer) biases or by engaging in unfair competition.

the way in which the programming code of an algorithm is written) may also suffer from bias.