The term bias mitigation techniques originates from the fields of artificial intelligence, big data and smart data, and industry and Industry 4.0. Biases often arise when data is collected, analysed or processed by computers. This can lead to unwanted errors, prejudices or imbalances that influence the outcome \u2013 for example, with an AI that automatically pre-sorts applications.<\/p>\n
Distortion reduction techniques are methods developed to detect and reduce such errors. The goal is for evaluations or decisions to remain objective and fair. For example, data is prepared in such a way that it considers all groups equally or removes erroneous patterns.<\/p>\n
A vivid example: A company uses AI to select job applicants. Without bias mitigation techniques, the AI could discriminate against women if it was trained on old, biased data. By applying bias mitigation techniques, these differences are identified and corrected \u2013 the AI evaluates all candidates more objectively. <\/p>\n
Anyone who values fair results in digitalisation or automation should always keep bias reduction techniques in mind.<\/p>","protected":false},"excerpt":{"rendered":"