Resources that discuss ways to address prejudice.
Author: Vince Lynch; Publisher: TechCrunch+; Publication Year: 2018. The following article discusses how bias in artificial intelligence (AI) can cause problems both because AI outputs are often blindly trusted, so any missed human bias could be spread, and because AI used in automated functions could spread bias without knowledge. The article names 3 ways to avoid bias in machine learning. 1). Choose the right…
Author: Ville Vakkuri, Pekka Abrahamsson; Publisher: IEEE Xplore; Publication Year: 2018. The following article seeks to find out what the reoccurring themes are in artificial intelligence (AI) ethics discourse as a way of understanding the current state of AI ethics as well as determining a direction for AI ethics going forward by using these themes to create a framework. 1062 papers were analyzed and 37 reoccurring keywords words related to AI…
Author: Vidhi Chugh; Publisher: Medium; Publication Year: 2021. The following article describes how as a data scientist, if you want the product you are working on to be “[artificial intelligence (AI)] for good,” then it is inevitable to encounter some situations such as biased data, biased models, or biased implementation. This article illustrated forms of what data bias can be and there is no one solution for all. But…
Author: Valerie Schneider; Publisher: Columbia Human Rights Law Review; Publication Year: 2020. The following article is a very granular deep dive into how artificial intelligence (AI) can lead to discrimination in housing and how this relates to Fair Housing Act “disparate impact” jurisprudence. The motivation for the article was a proposed rule from HUD that would have immunized landlords using algorithms from disparate impact fair housing claims…
Author: N/A; Publisher: UNESCO; Publication Year: 2023. The following article discusses how bias is very much prevalent in today’s artificial intelligence (AI)-systems shown through many examples. When searching for school in a search system the results often show women in promiscuous outfits, skinny, and white. However, when searching for school boy, the pictures are perfectly ordinary. There was…
Author: N/A; Publisher: United Nations News; Publication Year: 2020. The following article discusses how artificial intelligence (AI) development is especially problematic for countries where governments do not have the ability to effectively regulate consumer privacy or misinformation. International AI regulation will probably be necessary to curb the problem. Social media companies are incentivized to supply…
Author: Ugonma Nwankwo, Michael Pisa; Publisher: Center for Global Development; Publication Year: 2021. The following article discusses how evidence-based policy making is ineffective when it relies on biased information, a potential source for this is bias in the datasets. In the U.S., women make up 18% of data scientist jobs and in lower-income countries, that stat is even worse. In the data value chain, which includes collection, publication, uptake and…
Author: N/A; Publisher: TRT World; Publication Year: 2018. The following video discusses how because algorithms are written by humans, they are not any more objective than we are. Some examples of algorithmic bias include: Amazon’s Alexa failing to recognize different accents or Google Translate associating certain jobs with certain genders. Both machine learning and deep learning are dependent on huge…
Author: Trisha Sanghal, Coco Sun, and Sina Ghandian; Publisher: Medium; Publication Year: 2020. The following article discusses findings regarding policies related to deaths caused by police departments. Is crime a predictor for violent force policies? Well, researchers found that violent crime does not explain restrictive policy. While policing is a complicated topic, this does point to inherent biases in the system which must be accounted…
Author: Tricia Bisoux; Publisher: AACSB; Publication Year: 2019. The following article discusses how graphs can be highly misleading. As an ethical data scientist, it is important to both create visuals that convey the accurate information and not be a part of misinformation and also know how to interpret visuals well. It is important to be aware of biases when making graphs and to know when reading them, that they are not…