Author: Kylie Ying
Publisher: freeCodeCamp
Publication Year: 2021
Summary: The following article discusses how the notion of informed consent quickly fades in a business setting, where it may be in fine print or even without our knowledge. Consumers are often unaware of how their data is being used or may be used in the future. This also applies to privacy within the data as ways of reidentifying individuals are being made. Ying discusses that we need a system similar to Fairness Accountability Transparency (FAT), a computer science topic, for data science. Ideally, models would be fair, transparent in every step of the process, and reproducible from documented results. Fairness may result in a trade-off of a worse model outcome in favor of avoiding discrimination against a group. The reason that these words do not work as well for analytics is that data science often contains data that cannot be shared and complex models that cannot be fully explained. Ying instead recommends a different proposed acronym: Findable, Accessible, Interoperable, Reusable (FAIR).
