Author: Damini Gupta, T. S. Krishnan
Publisher: California Review Management
Publication Year: 2020
Summary: The following article highlights the importance and the need to reduce algorithmic bias. The article starts by recognizing that human bias will always play a role in decision making. It goes on to say that because of the outreach of artificial intelligence (AI) and the amount of people impacted by these algorithms and AI in general, that reducing bias as much as possible when developing these algorithms is paramount. This article links outside resources as well as introducing the topic and stating the importance of the topic. It links important resources to other, outside studies that have been done on algorithmic bias and how to access those resources. The article goes on to say that the real bias doesn’t lie in the algorithms inherently, but in the data that is being fed into these algorithms. It states that algorithms learn and update their model based on trends in real data and when real data is biased, that is a major problem and must be accounted for. Finally, the article ends by listing certain organizations that are aiming to help alleviate this problem within the data science community.
