Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy

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On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “We have previously noted the dichotomy of Type A and Type B applications of machine learning and data science in [9], but did not pose them as consequences of safety definitions.”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Research on human subjects is regulated by law in many jurisdictions; and given that data elements in social datasets represent people or groups of people (Varshney, 2015; Diaz, 2016), research on social data is, arguably, human subjects research (Metcalf and Crawford, 2016).”
Increasing Trust in AI Services through Supplier's Declarations of Conformity (2018, background): “As AI services increasingly make inferences for people and about people [29], IRB requirements increasingly apply to them.”
Engineering safety in machine learning (2016, methodology): “This is the same nomenclature as in [6].”
The Limits of Abstract Evaluation Metrics: The Case of Hate Speech Detection (2017, background): “, friend recommendations) [5]; and user participation is important for both of these tasks.”
FactSheets: Increasing trust in AI services through supplier's declarations of conformity (2019, background): “As AI services increasingly make inferences for people and about people [33], IRB requirements increasingly apply to them.”