A piece of writing that conveys research that was performed. The authors convey why and how the research was performed and its outcomes.
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: 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: Mark D. Wilkinson et al.; Publisher: Nature; Publication Year: 2016. The following article focuses on the need to improve data handling in order to ensure the reproducibility of results published in scientific literature. It sets forth the FAIR data principles (Findability, Accessibility, Interoperability, and Reproducibility). The article argues that FAIRness is a prerequisite for proper data management and stewardship. The…
Author: Jess Whittlestone et al.; Publisher: Conference on AI, Ethics, and Society; Publication Year: 2019. The following research article argues that there are tensions between common principles guiding ethical decision-making about artificial intelligence (AI). They explain 4 key tensions: service quality versus privacy, accuracy versus fair treatment, personalization versus solidarity, and convenience versus dignity. By analogy with bioethics, the authors also argue…
Author: Wenhong Chen, Anabel Quan-Haase; Publisher: Social Science Computer Review; Publication Year: 2018. The following article addresses new issues created by big data, such as biases, subjectivities, and forms of oppression. They define 4 major aspects of big data ethics and politics: 1). Potential biases in big data collection and interpretation, 2). Community and citizen concerns of big data (mis)use in public life and for journalistic purposes, 3). Media…
Author: Ville Vakkuri, Pekka Abrahamsson; Publisher: IEEE Xplore; Publication Year: 2018. The following article seeks to find out what the reoccurring themes are in artificial intelligence (AI) ethics discourse as a way of understanding the current state of AI ethics as well as determining a direction for AI ethics going forward by using these themes to create a framework. 1062 papers were analyzed and 37 reoccurring keywords words related to AI…
Author: Vicki Xafis, G. Owen Schaefer, Markus K. Labude, Iain Brassington, Angela Ballantyne, Hannah Yeefen Lim, Wendy Lipworth, Tamra Lysaght, Cameron Stewart, Shirley Sun, Graeme T. Laurie & E Shyong Tai; Publisher: Asian Bioethics Review; Publication Year: 2019. The following paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and Policy-relevant Ethics in Singapore (SHAPES) Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts…
Author: Valerie Schneider; Publisher: Columbia Human Rights Law Review; Publication Year: 2020. The following article is a very granular deep dive into how artificial intelligence (AI) can lead to discrimination in housing and how this relates to Fair Housing Act “disparate impact” jurisprudence. The motivation for the article was a proposed rule from HUD that would have immunized landlords using algorithms from disparate impact fair housing claims…
Author: Tristan Henderson; Publisher: Association for Computing (ACM) Machinery Digital Library; Publication Year: 2019. The following paper outlines a new Data Ethics & Privacy module that was introduced to computer science students in 2018. The module aims to raise student awareness of current debates in computer science such as bias in artificial intelligence, algorithmic accountability, filter bubbles and data protection, and practical mechanisms for…
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…