Ethics in Data Science
Exploring the social implications of data science
1 Introduction to Ethics in Data Science
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1.1 Section 1
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- Point 3: Sed do eiusmod tempor incididunt
1.2 Section 2
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1.2.1 Subsection A
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1.2.2 Subsection B
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1.3 Section 3
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Chapters to align with from learning data science textbook: * Ethics in asking questions * Eugenics example * Ethics in obtaining data * Ethics in understanding data * Ethics in understanding the world * Ethics in reports, decisions, and solutions * Designing and testing interventions
Tentative topics list: * what ethics means in data science * does ethics ever really change? * tech debt and documentation debt * defining and handling sensitive information * risks from data triangulation (i.e. re-identification, de-anonymization) * prediction as influence * what can and cannot (should/should not) be predicted * practices in data collection/generation * incomplete data, non-consensually collected data * too much / too invasive data * ad-hoc support of suspect decision-making