Author: Vivek Katial; Publisher: Multitudes; Publication Year: N/A. The following article starts by introducing the profound effect that algorithms and, in particular, decisions from algorithms have on our life today. The author goes on to define algorithmic bias as “the ability of algorithms to systematically and repeatedly produce outcomes that benefit one particular group over another.” The article then goes on to state…
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: N/A; Publisher: The Greenlining Institute; Publication Year: 2021. The following article discusses algorithmic biases in credit and finance, healthcare, employment, government programs, education and housing. Focusing in on algorithmic bias in credit and finance, banks and the fintech industry have eagerly replaced loan officers with algorithms that are more complex and use more sources of data than ever before to make…
Author: Shahriar Akter, Grace McCarthy, Shahriar Sajib, Katina Michael, Yogesh Dwivedi, John D’Ambra; Publisher: International Journal of Information Management; Publication Year: 2021. The following paper provided a thorough framework for algorithmic biases in data driven innovation (DDI) phases. It listed the scope and impact of bias across different phases in a data driven project, and it stated that both humans and machines should be involved in the process without relying fully on artificial autonomy. The data driven process…
Author: Michael Kearns, Aaron Roth; Publisher: Brookings; Publication Year: 2020. The following article is focused on frameworks for thinking about algorithmic bias and how to address the “unanticipated consequence of following the standard methodology of machine learning: specifying some objective (usually a proxy for accuracy or profit) and algorithmically searching for the model that maximizes that objective using colossal amounts…
Author: Joy Buolamwini, Latanya Sweeney, and Darren Walker; Publisher: Radcliffe Institute for Advanced Study, Harvard University; Publication Year: 2019. The following video documents a conversation between Joy Buolamwini, Latanya Sweeney, and Darren Walker. Leading the discussion is Latanya Sweeney, founder of Public Interest Tech Lab. During this half-hour discussion these 3 subject matter professionals delve into many implications and some examples of algorithmic bias. Sweeney speaks from…
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: TED; Publication Year: N/A. The following speech discusses how algorithmic bias, or the “coded gaze,” can lead to discriminatory practices. Machine learning is being used for facial recognition but a lack of Black and Brown faces in training sets has led to an inability to identify them. Joy suggests that we incorporate 3 inclusionary practices in coding: 1). Using diverse teams, 2)…
Author: Damini Gupta, T. S. Krishnan; Publisher: California Review Management; Publication Year: 2020. 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…
Author: Charles Q. Choi.; Publisher: IEEE Spectrum; Publication Year: 2021. The following article details 7 common flaws in AI models and many real-world examples of these failures. It touches particularly on neural network models, but many topics are applicable broadly, particularly in classification scenarios. For example, it details model brittleness; how models can quickly be fooled by a pattern…