-
5 Ethical Questions in Data Science
Author: N/A; Publisher: Future Learn; Publication Year: N/A. The following article discusses how data science has increased rapidly; however, the concerns around the ethical use of it by organizations have risen alongside it. We have seen examples of concerns in different realms, algorithms accepting and denying bank loans, scanning resumes, cookies to monitor behaviors, and…
-
Challenges of AI Ethics in Insurance
lnbressa
Author: Michelle Seng Ah Lee; Publisher: Medium; Publication Year: 2022. The following article discusses the ethical concerns associated with the implementation of artificial intelligence (AI) programs in the insurance industry. Beyond privacy issues arising from the collection and use of sensitive insurance-resulted data, concerns regarding fairness and equality have also been brought up concerning these…
-
HireVue Assessments and Preventing Algorithmic Bias
lnbressa
Author: Loren Larson; Publisher: HireVue; Publication Year: 2018. The following article discusses how HireVue is committed to good science that creates a level playing field for all candidates. Without deliberately working to reduce bias that may reside in an algorithm’s training data or its data scientist creators, algorithms are absolutely at risk of inheriting the…
-
Data Science and Its Diversity Problems
lnbressa
Author: Lauren Church, Carol Stabile; Publisher: Ms. Magazine; Publication Year: 2021. The following article discusses the issue of diversity in the data science field, which has adverse effects on the outcomes and range of issues addressed by the data. Authors present statistics which show gender and racial discrimination in the industry. For example, only 15…
-
Ethical and Social Risks of Harm from Language Models
lnbressa
Author: Laura Weidinger et. al.; Publisher: DeepMind; Publication Year: 2021. The following research paper helps organize the risk landscape associated with large-scale Language Models (LMs). To advance responsible innovation, a thorough understanding of the potential risks posed by these models is required. This report discusses 21 different types of ethics risks, as well as the…
-
Data Science Ethics – What Could Go Wrong and How to Avoid It
lnbressa
Author: Kylie Ying; Publisher: freeCodeCamp; Publication Year: 2021. The following article discusses how the notion of informed consent quickly fades in a business setting, where it may be in fine print or even without our knowledge. Consumers are often unaware of how their data is being used or may be used in the future. This…
-
How to Tackle Microaggressions in the Digital Workplace
Author: Kaya Ismail; Publisher: Reworked; Publication Year: 2022. The following article describes how microaggressions are subtle acts of discrimination that are not overt. Especially gender-based or racial-based microaggressions, such as pathologizing cultural values or communication styles. The author gives tips for handling microaggressions and they include: 1). Noticing when they happen; 2). Tackling…
-
Facebook’s Ad-Serving Algorithm Discriminates by Gender and Race
lnbressa
Author: Karen Hao; Publisher: MIT Technology Review; Publication Year: 2019. The following article describes how people may have seen personalized Facebook advertisements on their feed. As it turns out, that algorithm, based on machine learning, is discriminatory. It locates patterns within data and re-applies them to make decisions, allowing bias to enter the equation. This…
-
Artificial Intelligence Has a Problem With Gender and Racial Bias. Here’s How to Solve It
2019, Bias, Diversity, Equity & Inclusion, News Article, Notable People, Social Justice, Underrepresented Authorlnbressa
Author: Joy Buolamwini; Publisher: Time Magazine; Publication Year: 2019. In the following article, notable data ethics advocate Joy Buolamwini shares her journey combating gender and racial bias in artificial intelligence (AI). After encountering biased facial analysis software that could not recognize dark-skinned faces, the author was motivated to seek similar examples of discriminatory AI in…
-
8 Types of Data Bias That Can Wreck Your Machine Learning Models
lnbressa
Author: Joanna Kaminska; Publisher: Statice; Publication Year: 2022. The following article discusses how it is important to know that while working with data there is a possibility that the data is biased; this is a big problem due to the fact that if this biased data is used to create an machine learning (ML) model,…
Archive
Categories
- 1979
- 2002
- 2004
- 2006
- 2007
- 2010
- 2011
- 2012
- 2013
- 2014
- 2015
- 2016
- 2017
- 2018
- 2019
- 2020
- 2021
- 2022
- 2023
- Agriculture
- Automobiles
- Aviation
- Banking & Finance
- Bias
- Blog Article
- Book
- Cartography
- Code of Ethics
- Communities of Practice
- Criminal Justice
- Digital Media
- Diversity, Equity & Inclusion
- Education
- Education & Training
- Encyclopedia
- Environment
- Film, Arts & Entertainment
- Frameworks
- Glossary
- Healthcare
- Industry-Specific
- Insurance
- Legal & Policy
- News Article
- Notable People
- Oil & Gas
- Podcast
- Privacy
- Renewable Energy
- Research Article
- Social Justice
- Sports
- Tool
- Uncategorized
- Underrepresented Author
- Video
- Visualization
- White Paper