Bias

What does bias mean?

In AI, bias refers to a systematic distortion or bias in the results of a model. Bias can arise when training data already contains prejudices or unequal distributions or when the learning algorithm itself develops certain preferences. This results in distorted and potentially discriminatory results, for example if a face recognition model recognizes people with a certain skin color less well because they were underrepresented in the training. Bias is undesirable as it impairs the accuracy and fairness of AI systems.

Types of bias and causes

AI bias can occur at different levels:

• Data bias: the training data reflects historical biases or is not representative. Example: A language model that was predominantly trained with texts by young authors could favor certain language styles.

• Algorithmic bias: The learning algorithm or model architecture unintentionally reinforces differences.

• Bias through interaction: In systems that learn through user feedback, active user groups can steer the model behavior.

Machine learning models often silently absorb the biases inherent in the training data. A classic example was an applicant selection tool that discriminated against women because the historical recruitment data was male-dominated – the algorithm implicitly learned this bias.

Strategies for reduction

Unaddressed bias can reinforce unfair decisions and harm certain groups (e.g. in lending, personnel selection or the judiciary) [1]. In addition, users' trust in AI systems suffers if they are perceived as unfair.

Several approaches are used to reduce bias: careful data pre-processing (e.g. balancing out imbalances in the data set), fairness metrics to check the model results and iterative tests with various user groups. Technical approaches such as adversarial debiasing are also used to minimize bias.
Finally, transparency is important: explainable AI can make it clear why a model generates certain outputs – which helps to detect and eliminate hidden bias.