Author: Wendy Hui Kyong Chun; Publisher: MIT Press; Publication Year: 2021. The following book discusses the innate bias that comes from dirty data and how removing indicators of race will not remove bias from artificial intelligence. The author explains several concepts including: alternative algorithms, defaults, and interdisciplinary coalitions to foster more ethical/democratic data…
Author: Nina H. Di Cara, Natalie Zelenka, Huw Day, Euan D.S. Bennet, Vanessa Hanschke, Valerio Maggio, Ola Michalec, Charles Radclyffe, Roman Shkunov, Emma Tonkin, Zoë Turner, Kamilla Wells; Publisher: National Library of Medicine; Publication Year: 2022. The following article discusses how awareness and management of ethical issues in data science are becoming crucial skills for data scientists. Discussion of contemporary issues in collaborative and interdisciplinary spaces is an engaging way to allow data-science work to be influenced by those with expertise in sociological fields and so improve the ability…
Author: Angela Daly, S. Kate Devitt, Monique Mann; Publisher: Insitute of Network Cultures (Google Books); Publication Year: 2019. The following book moves away from critiquing bad data and instead brainstorms a more optimistic vision of the field of data. They seek to promote conversations between many disciplines and stakeholders in order to raise awareness for good & ethical data practices. They combine expertise from many fields to get a…