Author: Jeff Saltz
Publisher: Data Science Process Alliance
Publication Year: 2022
Summary: The following article provides 10 questions for a data science team to incorporate ethics into their projects. These questions address various aspects of ethics in data science. It is important to consider the laws surrounding the scope of projects. This includes the legal rights of individuals being affected by their data’s usage. In addition to the legal considerations of an individual, an individual’s privacy must also be protected. In terms of data collection, data scientists need to consider if the data was ethically collected and if the data is representative and accurate. When creating models, data scientists need to identify ways to minimize bias and mitigate it. This can help to minimize the harmful effects a model can have in use. In addition, considerations for model transparency can alleviate discontent with outcomes, by allowing understanding for results. As well as, making decisions to prevent misinterpretations of results. At the end of all projects, data scientists need a framework for ethical accountability so that ethics are not just talked about, but seriously ingrained in their work.