Author: Robin Hauser; Publisher: TED; Publication Year: N/A. In the following talk, Robin Hauser talks about unconscious bias in artificial intelligence (AI) algorithms. As part of producing her new movie about unconscious bias, she became interested in finding out whether it would be possible to create AI without bias. As she came to find out, it is oftentimes harder to create unbiased algorithms for multiple…
Author: Phaedra Boinodiris; Publisher: IBM Technology; Publication Year: 2021. The following article discusses how artificial intelligence (AI) is making decisions that directly impact all of us. People assume that because AI is a machine, the AI is unbiased and creates a “correct” decision. There are 5 pillars to earning trust in an AI decision. The first pillar is fairness. Is AI fair to everyone? In particular, is AI being proper for historically…
Author: Mia Shah-Dand; Publisher: AI Ethics Diaries: Keeping it Real Podcast; Publication Year: 2022. The following podcast episode features Milena Pribic, a senior artificial intelligence (AI) designer at IBM, who gives advice on building ethical AI. Her team digs into team practices and uses an ethical approach to understand the team as she wants her team to understand the context and repercussions of what they are building. Additionally, Pribic…
Author: Martin Barakov; Publisher: Medium; Publication Year: 2020. The following article discusses how racial recognition technologies have been criticized for their lack of usefulness and ability to generate true positives for years now. This article provides multiple real world examples, such as New York Police Department, South Wales Police, and Clearview artificial intelligence (AI), to emphasis the ethical concerns in…
Author: Marie Wallace; Publisher: TED; Publication Year: 2014. The following video is a TED talk discussing the issue of privacy in data collection. The speaker gives an overview of the issue of privacy, how it affects people, the role that companies and governments play in it, and how it is a difficult problem to solve that can not have a one-size-fits-all solution. She then relates the issue of privacy to that of transparency…
Author: Karen Hao; Publisher: MIT Technology Review; Publication Year: 2019. The following article discusses how in March of 2019, NBC released a story with the headline “Facial recognition ‘dirty little secret’: Millions of online photos scraped without consent.” The story expands on a data set released by IBM of one million photos of faces to help fairer facial recognition algorithms. However, those photos were scraped from Flickr…
Author: Joy Buolamwini, Timnit Gebru; Publisher: MIT Media Lab; Publication Year: 2018. The following video discusses the Gender Shades project which evaluates the bias of 3 commercial artificial intelligence (AI) gender classification products (Microsoft, Face++, and IBM). Their focus and motivations are to show the need for increased transparency in the performance of any AI products/services that focus on human subjects. They found that the…
Author: Joy Buolamwini, Aaina Agarwal, and Sasha Costanza-Chock; Publisher: The Medium; Publication Year: 2020. The following article applauds IBM for the decision to restrict sales of facial recognition technology in response to bias and abuse. It’s an example of a company genuinely, if belatedly, demonstrating a commitment to basic artificial intelligence ethics. The article calls on more tech companies to do the same, as well as for companies to commit…
Author: Joy Buolamwini
Publisher: SpeakInc
Publication Year: 2018
Summary: In the following video, Joy Buolamwini expands on her mission of the ‘Coded Gaze’, her term for algorithmic bias which can lead to discriminatory practices or exclusionary experiences. She recalls her experience of her face only being recognized after she wore a white mask. She provides more examples of how 130 million people in the U.S…
Author: Joy Buolamwini; Publisher: Time Magazine; Publication Year: 2019. In the following article, notable data ethics advocate Joy Buolamwini shares her journey combating gender and racial bias in artificial intelligence (AI). After encountering biased facial analysis software that could not recognize dark-skinned faces, the author was motivated to seek similar examples of discriminatory AI in the industry. After recognizing the…