Category: 2020

Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework

Clinical Data, Ethical Data Usage, Health Information, Personal Data

Author: David B Larson, David C Magnus, Matthew P Lungren, Nigam H Shah, Curtis P Langlotz; Publisher: National Library of Medicine; Publication Year: 2020. The following article discusses how when we use data to serve…
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Incorporate Inclusivity

Gender, Gender Gap, Racial Gaps, StrengthsFinder

Author: Data Science Ethics Podcast Publisher: Spotify Publication Year: 2020 Summary: The following podcast episode discusses how gender and racial gaps exist in the field of data science as it tends to be dominated by…
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How Data Privacy Leader Apple Found Itself in a Data Ethics Catastrophe

Apple, Compliance, Consumer Trust, Goldman Sachs, Personal Data

Author: Daniel Lu, Mike Loukides; Publisher: O’Reilly; Publication Year: 2020. The following article uses a terribly unsuccessful product launch between Apple and Goldman Sachs to establish the difference between compliance and ethics, show that even…
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Algorithmic Bias: Why Bother?

Algorithmic Bias, Algorithms, Artificial Intelligence, Decision-Making

Author: Damini Gupta, T. S. Krishnan; Publisher: California Review Management; Publication Year: 2020. The following article highlights the importance and the need to reduce algorithmic bias. The article starts by recognizing that human bias will…
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Great Promise but Potential for Peril

Artificial Intelligence, Automation, Decision-Making, Discrimination, Healthcare, Hiring Software, Human Judgement, Machine Learning, Product Development, Surveillance, Unconscious Bias

Author: Christina Pazzanese Publisher: The Harvard Gazette Publication Year: 2020 Summary: The following article starts off by talking about all the different ways in which AI or machine learning can help improve society and business.…
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Your Big Data Responsibility: The Rise In Data Ethics

Algorithmic Audit, Company Profits, Consent, Customer Data, Design, Evaluation, Informed Consent, risk mitigation, Transparency

Author: Christian Ofori-Boateng; Publisher: Forbes; Publication Year: 2020. The following article discusses how, for businesses, data represents customers, and customers drive profit. It’s easy for a business to be distracted by company profits and less…
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Data Feminism

Binaries, Context, Data Collection, Hierarchies, Indigenous Peoples, Intersectionality, Power Distribution, Power Structures

Author: Catherine D’Ignazio and Lauren Klein; Publisher: MIT Press; Publication Year: 2020. In the following book D’Ignazio and Klein present a new lens for thinking about data science and ethics. Their ideas are based on…
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What Gets Counted Counts

Binaries, Classification, Exclusion, Hierarchies, Predictions

Author: Catherine D’Ignazio and Lauren Klein; Publisher: MIT Press; Publication Year: 2020. The following book chapter considers how within data science, practitioners are used to seeing the world in 1s and 0s – in fact,…
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The Numbers Don’t Speak for Themselves

Context, Data Collection, Inclusivity

Author: Catherine D’Ignazio and Lauren Klein; Publisher: MIT Press; Publication Year: 2020. The following book chapter discusses how context is everything. Numbers are just numbers and cannot speak for themselves. Future data scientists need to…
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