-
Why the World Needs More Women Data Scientists
lnbressa
Author: Ugonma Nwankwo, Michael Pisa; Publisher: Center for Global Development; Publication Year: 2021. The following article discusses how evidence-based policy making is ineffective when it relies on biased information, a potential source for this is bias in the datasets. In the U.S., women make up 18% of data scientist jobs and in lower-income countries, that…
-
Fellowship Recipient Uses Data Science to Fight for LGBTQ Rights
lnbressa
Author: N/A; Publisher: Berkeley School of Information; Publication Year: 2019. The following article talks about how one student is using data science to better the world. Master of Information and Data Science student and 2019 recipient of the Paul Fasana LGBTQ Studies Fellowship, Christina Papadimitriou is using the fellowship for her research for LGBTQ equality…
-
Algorithmic Bias Explained
lnbressa
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…
-
Simple Analysis of Police Policies in the U.S.
lnbressa
Author: Trisha Sanghal, Coco Sun, and Sina Ghandian; Publisher: Medium; Publication Year: 2020. The following article discusses findings regarding policies related to deaths caused by police departments. Is crime a predictor for violent force policies? Well, researchers found that violent crime does not explain restrictive policy. While policing is a complicated topic, this does point…
-
The Human Insights Missing from Big Data
lnbressa
Author: Tricia Wang; Publisher: TED; Publication Year: N/A. In the following video, Wang suggests that insight through an emotional lens often eludes big-data findings. Wang explains that while big data is important, the subsequent decisions made with it are better when paired with thick data. Thick data refers to “stories and interactions that cannot be…
-
Mitigating Gender Bias in Natural Language Processing: Literature Review
lnbressa
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…
-
Ethical Data Analytics — What Every Business Needs to Know
lnbressa
Author: Tom Jongen; Publisher: Medium; Publication Year: 2021. The following article discusses how if any company uses customer data to make customer-facing decisions, there are ethical issues that need to be considered. Also, it asks us to consider “When ‘Know Your Customer’ goes too far;” and the issues related to Facebook/Cambridge Analytica political influence scandal…
-
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
lnbressa
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,…
-
DAIR Research Institute
lnbressa
Author: Timnit Gebru; Publisher: DAIR Research Institute; Publication Year: N/A. The following website features an interdisciplinary and globally distributed artificial intelligence (AI) research institute that was founded by Timnit Gebru who was fired by Google in December 2020 for raising issues of discrimination in the workplace as was was the co-lead for Google’s Ethical AI…
-
Doing Data Science for Social Good, Responsibly
lnbressa
Author: Rachel Thomas; Publisher: fast.ai; Publication Year: 2021. The following article focuses on some of the ethical considerations not-for-profits need to consider before beginning a data science project. In addition to the general privacy and ethical considerations for profit companies need to consider, there are also issues of paternalism, power imbalance, and emphasizing flashy high…
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