Author: Will Knight; Publisher: Wired; Publication Year: 2019. The following article touches on the idea of bias “proxies,” like address information as a proxy for race. It talks about how the Apple Card was offering lower credit limits for women compared to men even though gender was specifically excluded as a variable. The author argues that we should be including those specific variables so that the…
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: 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: Tony Sun, Andrew Gaut, Shirlyn Tang, et al.; Publisher: Arxiv; Publication Year: N/A. The following article discusses how despite their success in modeling various applications, natural language processing (NLP) models propagate and may even amplify gender bias found in text corpora. While the study of bias in artificial intelligence is not new, methods to reduce gender bias in NLP are still in their early stages. The authors of this paper…
Author: N/A; Publisher: Synced; Publication Year: 2020. The following news article talks about Genderify, an artificial intelligence (AI)-powered tool designed to identify a person’s gender by analyzing their name, username or email address — has been completely shut down. Genderify was met with fervent criticism on Twitter, with many decrying what they saw as built-in biases. Entering the word “scientist”…
Author: Sonja Kelly, Mehrdad Mirpourian; Publisher: Women’s World Banking; Publication Year: 2021. The following report discussed where gender-based bias originates and how to mitigate such biases in the emerging digital credit space. The report discussed the data collection process on emerging credit platforms and what data would be collected. It also introduced 3 potential origins of bias in algorithms, including sampling bias, labeling bias…
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: Olga Akselrod; Publisher: ACLU; Publication Year: 2021. The following article presents a direct call to action to law makers to increase legislation to prevent against bias and discrimination in artificial intelligence (AI) algorithms. The author describes how, intentionally or not, AI algorithms can perpetuate inequalities that exist among the populations that they deal with. The author, and the group they are…
Author: Niral Sutaria; Publisher: Isaca Journal; Publication Year: 2022. The following article discusses how bias is a large concern in artificial intelligence (AI) that is often overlooked due to the growth and excitement within the AI field. Examples of this bias recently are an AI-based recruiting tool had to be retired as it was shown to be biased against women, or a facial recognition software being shutdown after treating certain…
Author: Nello Cristianini; Publisher: European Research Council; Publication Year: 2021. The following article features Nello Cristianini, a professor of artificial intelligence from the University of Bristol, studied gender bias in media at a macro scale in order to see if they can remove bias from the media. They were able to remove bias and claim that their methods can be applied to other use cases. He also ran a sentiment analysis on Twitter to…