lnbressa Author: N/A; Publisher: ParsonsTKO; Publication Year: 2021. The following video discusses how data storytelling is essential for any good data scientist. This makes it even more important for data scientists to be aware of privacy concerns, and avoid bias, and misinterpretation. This video includes panelists that all have expertise in the data ethics and regarding public relations. For example, if you publish a…
Author: N/A; Publisher: YimmyTalks; Publication Year: 2020. The following video starts off by discussing the importance of data and data scientists in today’s society outlining their power and influence. It then shifts to talking about the responsibility that comes with this power. Since data scientists and their models have…
Author: Yanmin Xu, Yitao Tao, Chunjiong Zhang, Mingxing Xie, Wengang Li, Jianjiang Tai; Publisher: Hindawi; Publication Year: 2022. The following article talks about how ethical considerations also need to be taken when using data for use of digital economies. This article discusses how digital economies are being introduced in parts of China, and what ethical issues can arise…
Author: Y. Gil and B. Selman; Publisher: Computing Community Consortium (CCC) and Association for the Advancement of Artificial Intelligence (AAAI); Publication Year: 2019. The following paper touches on such a broad spectrum of societal facets artificial intelligence (AI) affects and should be considered more closely. The different areas stimulated ideas such as highlighting the AI Open Knowledge network need. Currently the major tech companies have access to a majority of this resource so it is imperative to open…
Author: Xiaofeng Tang, Eduardo Mendieta, Thomas A. Litzinger; Publisher: Science and Engineering Ethics; Publication Year: 2022. The following article discusses how a lack of training in ethical theories and related pedagogy has kept many engineering faculty members from teaching data ethics, an important aspect of engineering research that has become more salient in recent years. This paper describes the development of a module, which includes concepts, cases, policies, and…
Author: N/A; Publisher: Workday; Publication Year: 2022. The following article discusses a survey from PwC shows that while diversity is a stated value or priority area for 75% of organizations, 32% of respondents still feel diversity is a barrier to employee progression. To optimize analytics for diversity, equity, inclusion, and belonging (DEIB), the author recommended companies to take the following 3 actions: 1)…
Author: N/A; Publisher: The Wise Marketer; Publication Year: 2022. The following article talks about how businesses could benefit from customer data and pointed out the 4 main ethical issues (ownership, transparency, consent, and equitable value exchange) that need to be aware of when processing customer data. The goal is to ensure that customers understand, agree, and benefit from the process…
Author: N/A; Publisher: Wired; Publication Year: 2018. The following video discusses how cops in Los Angeles (& many places) are using predictive algorithms in policing. Proponents say it is effective at reducing crime, but many people argue that it helps police justify racial profiling. The data is biased because policing statistics have always disproportionately targeted minorities, so these algorithms can not…
Author: Winston Thomas; Publisher: CDO Trends; Publication Year: 2022. The following article starts by questioning who the authority on data ethics will be, especially as cultural and legal definitions vary by region and nation. Jay Upchurch, the CIO of SAS, says concerns about how to address and solve data ethics problems led the company to create its Data Ethics Practice (DEP). The DEP is a cross-functional global team to…
Author: William A. Tanenbaum, Kiyong Song, Linda A. Malek; Publisher: Reuters; Publication Year: 2022. The following article outlines the 2 basic theories/ideas regarding who is responsible or liable for the actions taken and decisions made by an artificial intelligence (AI). The first argument is that the organization that utilizes the AI is solely responsible for the effects of its outcomes. The second contends that those developed the AI should have to…