The term bias mitigation techniques originates from the fields of artificial intelligence, big data and smart data as well as Industry and Factory 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 result - for example in the case of an AI that automatically pre-sorts job applications.<\/p>\n
Bias reduction techniques are methods that have been developed to recognise and reduce such errors. The aim is to ensure that analyses or decisions remain objective and fair. For example, the data is prepared in such a way that it takes all groups into account equally or erroneous patterns are removed.<\/p>\n
An illustrative example: A company uses an AI to select applicants for a job. Without bias minimisation techniques, the AI could discriminate against women if it has been trained with old, biased data. By applying bias mitigation techniques, these differences are recognised and eliminated - 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":"