Author: lnbressa

3 Ways to Spot a Bad Statistic

Accountability, Data Collection, Incentives, Misinformation, Misleading Statistics

Author: Mona Chalabi; Publisher: TED; Publication Year: N/A. The following article warns us of the dangers of misleading statistics and the lack of accountability and incentives that private companies have to produce reliable stats. It…
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The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts

Big Data, Big Data Gap, Epistemology, Ethical Assessment, Governance Framework

Author: Brent Daniel Mittelstadt, Luciano Floridi; Publisher: National Library of Medicine; Publication Year: 2015. The following paper provides an ethical assessment of emerging Big Data practices and a framework for governance. The authors discuss 2…
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Racial Equity and Data Science

Big Data, Discriminatory Practices, Racial Discrimination, Racial Equality

Author: MIT IDSS; Publisher: MIT Political Science; Publication Year: 2022. The following article features the Research Initiative on Combating Systematic Racism (ICSR) who’s goal is to seed and coordinate research used to identify racially discriminatory…
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Misleading Statistics & Data: How to Protect Yourself Against Bad Statistics

Creditable Data, Information Presentation, Misleading Statistics

Author: Hassan Ud-deen; Publisher: Klipfolio; Publication Year: 2020. The following article is about the severity of misleading statistics and how to avoid them. The first of these ways is to get creditable data. The second…
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Fairlearn

Algorithms, Artificial Intelligence, Fairlearn, Fairness, Unfairness Mitigation

Author: Miro Dudik; Publisher: Fairlearn; Publication Year: N/A. The following tool is Fairlearn which is a open-source project that helps data scientists improve fairness of their artificial intelligence (AI) systems. It includes a python library…
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Algorithms Are Making Economic Inequality Worse

Algorithms, Economic Inequality, Gig Laborers, Surveillance, Worker Exploitation

Author: Mike Walsh; Publisher: Harvard Business Review; Publication Year: 2020. The following article discusses how the algorithms we design can be used to exploit workers and create an underclass of gig laborers who have no…
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Ethics and Data Science

Consent, Consequences, Control, Data Collection, Data Interpretation, Data Modeling, Guidelines, Model Implementation

Author: Mike Loukides, Hilary Mason, DJ Patil; Publisher: O’Reilly; Publication Year: 2018. The following book focuses on implementing ethical guidelines into data scientists’ daily work. The authors provide 5 framing guidelines: consent, clarity, consistency, control,…
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Social Media, Big Data, and Mental Health: Current Advances and Ethical Implications

Big Data, Mental Health, Natural Language Processing Technology, Public Health, Social Media

Author: Mike Conway, Daniel O’Connor; Publisher: Current Opinion in Psychology; Publication Year: 2016. The following article discusses how social media big data being combined with natural language processing (NLP) technology can address public health research…
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Lies, Racism and Sexism: The Power of Data Stories

Algorithms, Artificial Intelligence, Blind Faith, Facial Recognition Systems, Storytelling

Author: Mike Bugembe; Publisher: TED; Publication Year: N/A. The following talk discusses how algorithms inherently have bias and there is a common problem of blind faith in data. Blind faith in data is trusting whatever…
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