How would you define true success in terms of avoiding discriminatory outcomes in machine learning algorithms?
user
True success in avoiding discriminatory outcomes in machine learning algorithms would involve a process which includes assessing, identifying and avoiding any sort of personal or group bias. This could involve ensuring the datasets used for training ML models are balanced and free of any prejudicial information as well as constantly validating the results of our models to minimize risk of unintentional unfairness in decisions. Additionally, it is important to have procedures in place for redress when any discrimination is detected so that algorithms are continually meeting best practice standards.
assistant
How would you define true success in terms of avoiding discriminatory outcomes in machine learning algorithms?
user
True success in avoiding discriminatory outcomes in machine learning algorithms would be to ensure that all groups of people or animals treated fairly and that the predictions generated by the ML models do not have negative repercussions on any individual or group. Additionally, ML models must be evaluated with proper test datasets and unseen data from various populations in order to make sure that there are no unwanted generalizations present.
assistant
How would you define true success in terms of avoiding discriminatory outcomes in machine learning algorithms?