Fairness or algorithmic fairness refers to different approaches to removing algorithmic bias from machine learning models. The process of data synthesization can be used to fix biases embedded in the data via upsampling minority groups, such as high earning women in a dataset. The challenge in creating fair algorithms is that fairness needs to be defined on a case-by-case basis, considering the social and economic context in all its complexity.
We use third-party web analytics tools to analyze website usage and measure the success of advertising campaigns. Cookies are set in the process and data is partly transferred to the USA. Further details can be found in our privacy policy. You can revoke or adjust your selection at any time under Settings.
Here you will find an overview of all cookies used. You can give your consent to whole categories or display further information and select certain cookies.