Blog Article

An informational writing piece, typically conversational and informal in style, that is published in the blog section of a website.

Best Practices: Optimizing Analytics for Diversity, Equity, Inclusion, and Belonging

by

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)…

Determining the Best and Most Ethical Use of Customer Data

by

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…

Enterprise Data Ethics Framework

by

Author: N/A; Publisher: The University of Queensland; Publication Year: N/A. The following article is written for the University of Queensland in Australia, but the ethical principles outlined in the document are best practice for anyone who collects, maintains, and utilizes data. The first principle states that data usage must be defined, and a cost-benefit analysis must be done for individuals affected by the data. Likewise…

Privacy Wars Fueled by the GDPR

by

Author: Will Horvath; Publisher: Data Science Ethics; Publication Year: 2019. The following article discusses how with countries fighting against the growing threat of privacy invasion (and many losing), Europe implemented in 2018 the General Data Protection Regulation (GDPR) that provides the ‘most sweeping protection of user privacy rights yet passed in the world.’ France’s CNIL used the GDPR to fine Google for $57 million…

Ethical Dilemmas: How Scandals Damage Companies

by

Author: N/A; Publisher: Western Governors University; Publication Year: 2021. The following article discusses how companies with higher ethical standards have been shown to perform better than companies who fail to consider the ethics of their practice. “73% of professionals say they take an organization’s values into account.” It is important for employees in deciding where to work if the company has values that align with…

The Data Equity Framework

by

Author: N/A; Publisher: We All Count; Publication Year: N/A. The following article discusses how anytime data is involved, decisions are being made and those decisions have consequences. Equity needs to be at the forefront, and this 7-step framework is a systematic approach to doing so. Each step has equity-impacting decision points that must be evaluated. These steps are 1). Funding (this is the stage that…

Global Megatrends 2022

by

Author: Wanda Curlee; Publisher: Project Management Institute; Publication Year: 2022. The following article covers the application of data ethics in projects and it provides a framework that this website approves of. The author provides an overview of guidelines that they followed when creating their framework. One that was eye-catching was to go beyond the letter of the law. Laws have not caught up to data science and so it’s up to…

Data Ethics & Mitigating Algorithmic Bias

by

Author: Vivek Katial; Publisher: Multitudes; Publication Year: N/A. The following article starts by introducing the profound effect that algorithms and, in particular, decisions from algorithms have on our life today. The author goes on to define algorithmic bias as “the ability of algorithms to systematically and repeatedly produce outcomes that benefit one particular group over another.” The article then goes on to state…

Three Ways to Avoid Bias in Machine Learning

by

Author: Vince Lynch; Publisher: TechCrunch+; Publication Year: 2018. The following article discusses how bias in artificial intelligence (AI) can cause problems both because AI outputs are often blindly trusted, so any missed human bias could be spread, and because AI used in automated functions could spread bias without knowledge. The article names 3 ways to avoid bias in machine learning. 1). Choose the right…

Handling Data Bias

by

Author: Vidhi Chugh; Publisher: Medium; Publication Year: 2021. The following article describes how as a data scientist, if you want the product you are working on to be “[artificial intelligence (AI)] for good,” then it is inevitable to encounter some situations such as biased data, biased models, or biased implementation. This article illustrated forms of what data bias can be and there is no one solution for all. But…