Author: lnbressa
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Weapons of Math Destruction
Author: Cathy O’Neil; Publisher: Crown; Publication Year: 2016. The following book discusses “weapons of math destruction”: algorithms that try to quantify subjective qualities or judgment metrics. For example, an algorithm that tries to quantify “creditworthiness”…
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The Era of Blind Faith in Big Data Must End
Algorithmic Audit, Algorithmic Models, Algorithms, Big Data, Blind Trust, Data Collection, Unconscious BiasAuthor: Cathy O’Neil; Publisher: TED Talks; Publication Year: 2017. The following video highlights the dangers of blind trust in algorithmic models. O’Neil suggests that algorithms are much less scientific and objectively truthful than commonly perceived.…
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The Ethical Data Scientist
Author: Cathy O’Neil; Publisher: Slate; Publication Year: 2016. The following article discusses when deciding which variables to include in a model, some factors (race, etc.) can be easily excluded to ensure that racial bias in…
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Iran’s Sweeping Internet Blackouts are a Serious Cause for Concern
Censorship, Human Rights, Human Rights Violations, Iran, Iranian Government, Police Brutality, Protests, Public Access, WhatsAppAuthor: Catherine Thorbecke; Publisher: CNN; Publication Year: 2022. Summary: In the following article, Thorbecke informs the public of how the Iranian government has censored the Iranian people by cutting off their access to wifi in…
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Data Feminism
Binaries, Context, Data Collection, Hierarchies, Indigenous Peoples, Intersectionality, Power Distribution, Power StructuresAuthor: 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
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
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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On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints
Author: Catherine D’Ignazio and Lauren Klein Publisher: MIT Press Publication Year: 2020 Summary: The following book chapter from “Data Feminism” can be beautifully summed up by words from Black feminist sociologist Patricia Hill Collins: “Neither…
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Data Feminism Presents an Intersectional Feminist Way to Think About Data Science and Ethics
Author: Catherine D’Ignazio and Lauren Klein; Publisher: Ms. Magazine; Publication Year: 2020. The following article discusses how the dominant group of data scientists today are straight, white, cisgender man who has formal credentials and is…