rolemapr: A package to produce roles and responsibility maps

Data Science Ethics Checklist

[ ] A.1 Informed consent: If there are human subjects, have they given informed consent, where subjects affirmatively opt-in and have a clear understanding of the data uses to which they consent?
[ ] A.2 Collection bias: Have we considered sources of bias that could be introduced during data collection and survey design and taken steps to mitigate those?
[ ] A.3 Limit PII exposure: Have we considered ways to minimize exposure of personally identifiable information (PII) for example through anonymization or not collecting information that isn't relevant for analysis?

[ ] B.1 Data security: Do we have a plan to protect and secure data (e.g., encryption at rest and in transit, access controls on internal users and third parties, access logs, and up-to-date software)?
[ ] B.2 Right to be forgotten: Do we have a mechanism through which an individual can request their personal information be removed?
[ ] B.3 Data retention plan: Is there a schedule or plan to delete the data after it is no longer needed?

[ ] C.1 Missing perspectives: Have we sought to address blindspots in the analysis through engagement with relevant stakeholders (e.g., checking assumptions and discussing implications with affected communities and subject matter experts)?
[ ] C.2 Dataset bias: Have we examined the data for possible sources of bias and taken steps to mitigate or address these biases (e.g., stereotype perpetuation, confirmation bias, imbalanced classes, or omitted confounding variables)?
[ ] C.3 Honest representation: Are our visualizations, summary statistics, and reports designed to honestly represent the underlying data?
[ ] C.4 Privacy in analysis: Have we ensured that data with PII are not used or displayed unless necessary for the analysis?
[ ] C.5 Auditability: Is the process of generating the analysis well documented and reproducible if we discover issues in the future?

[ ] D.1 Proxy discrimination: Have we ensured that the model does not rely on variables or proxies for variables that are unfairly discriminatory?
[ ] D.2 Fairness across groups: Have we tested model results for fairness with respect to different affected groups (e.g., tested for disparate error rates)?
[ ] D.3 Metric selection: Have we considered the effects of optimizing for our defined metrics and considered additional metrics?
[ ] D.4 Explainability: Can we explain in understandable terms a decision the model made in cases where a justification is needed?
[ ] D.5 Communicate bias: Have we communicated the shortcomings, limitations, and biases of the model to relevant stakeholders in ways that can be generally understood?

[ ] E.1 Redress: Have we discussed with our organization a plan for response if users are harmed by the results (e.g., how does the data science team evaluate these cases and update analysis and models to prevent future harm)?
[ ] E.2 Roll back: Is there a way to turn off or roll back the model in production if necessary?
[ ] E.3 Concept drift: Do we test and monitor for concept drift to ensure the model remains fair over time?
[ ] E.4 Unintended use: Have we taken steps to identify and prevent unintended uses and abuse of the model and do we have a plan to monitor these once the model is deployed?

Data Science Ethics Checklist generated with deon.

whjelmar/rolemapr documentation built on Aug. 8, 2022, 1:32 p.m.

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