Upload wildfire_ethics_checklist.md

wildfire_ethics_checklist.md

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+# Data Science Ethics Checklist
+
+[![Deon badge](https://img.shields.io/badge/ethics%20checklist-deon-brightgreen.svg?style=popout-square)](http://deon.drivendata.org/)
+
+## A. Data Collection
+ - [ ] **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?
+ - [ ] **A.4 Downstream bias mitigation**: Have we considered ways to enable testing downstream results for biased outcomes (e.g., collecting data on protected group status like race or gender)?
+
+## B. Data Storage
+ - [ ] **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. Analysis
+ - [ ] **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. Modeling
+ - [ ] **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. Deployment
+ - [ ] **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](http://deon.drivendata.org).*