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: Tolga Bolukbasi, Kai-Wei Chang, James Zou, et al.; Publisher: Arxiv; Publication Year: 2016. The following article discusses how machine learning applied blindly runs the risk of amplifying biases in data. Word embedding, a popular framework for representing text data as vectors that has been used in many machine learning and natural language processing tasks, poses such a risk. The authors demonstrate that even word embeddings…
Author: Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford; Publisher: Microsoft; Publication Year: 2019. The following proposal identifies that as the field of machine learning has grown, we continually see misuse of algorithmic processes or data that reinforces biases or has other ethical and legal concerns. The proposal here is that similar to the electronics agency, each “component” in data should have a description, prompted by questions in 7 categories…
Author: Steven Tiell; Publisher: Ethics of Data; Publication Year: 2020. The following visualization focuses on how a lot of terms surrounding the ethical considerations of data seem interchangeable, yet educating ourselves on what really constitutes ethical data is not something we always consider. Each term has nuance that makes it unique, but they are also all interconnected in a data ethics framework. At the core…
Author: Stephen Zorio; Publisher: Amazon: Science; Publication Year: 2020. In the following article, panelists discuss the tradeoff between accuracy and privacy when dealing with sensitive situations and machine learning (ML) models. They provide a relevant example with the pandemic outbreak, where accurate contact tracing can be more important than respecting user privacy and this highlights two problems: bias and…
Author: N/A; Publisher: statistics.com; Publication Year: N/A. The following article talks about the legal side of ethical data science. It talks about how the European Union passing the General Data Protection Regulation (GDPR) in 2016 has set a stepping stone for ethical data science. However at the same time, the United States is still lacking a very comprehensive federal regulation defining what is artificial…
Author: Sean Michael Kerner; Publisher: VentureBeat; Publication Year: 2022. The following article discusses how when labeling data for modeling and machine learning, the person labeling the data can accidently introduce bias to the models. This article argues that an open-source way to model (i.e. let a group of people label the data) can help to reduce these biases. This is an interesting idea: while labeling things yourself is a…
Author: Saurabh Mishra; Publisher: Medium; Publication Year: 2020. The following article discusses how data is very important when it comes to artificial intelligence (AI) and machine learning as it acts as the fuel source for these methods. One of the few unethical examples that the article made reference to was an unethical use of facial recognition where the U.S.’s Immigration and Customs Enforcement (ICE) used facial…
Author: Sarah E. Bon, Nyasha Junior; Publisher: Hyperallergic; Publication Year: 2020. The following article explores the use of artificial intelligence (AI) and machine learning in artistic research and reconstructions. Specifically, the authors explore the hidden human bias behind these technologies that are seemingly objective and scientific. Throughout the article, the authors name numerous examples when unethical and biased…
Author: Sam Gilbert; Publisher: Bennett Institute of Public Policy, Cambridge; Publication Year: 2020. The following article discusses the following topics with respect big tech companies: privacy and surveillance; bias; discrimination; and injustice in algorithmic decisioning; encoding of ethical assumptions in autonomous vehicle systems; artificial general intelligence as an existential risk to humanity; software user interface design as an…