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ethics.html
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<html>
<body>
<h1>
Data Science Ethics Checklist
</h1>
<br/>
<br/>
<a href="http://deon.drivendata.org/">
<img alt="Deon badge" src="https://img.shields.io/badge/ethics%20checklist-deon-brightgreen.svg?style=popout-square"/>
</a>
<br/>
<br/>
<h2>
A. Data Collection
</h2>
<hr/>
<ul>
<li>
<input type="checkbox"/>
<strong>
A.1 Informed consent:
</strong>
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?
</li>
<li>
<input type="checkbox"/>
<strong>
A.2 Collection bias:
</strong>
Have we considered sources of bias that could be introduced during data collection and survey design and taken steps to mitigate those?
</li>
<li>
<input type="checkbox"/>
<strong>
A.3 Limit PII exposure:
</strong>
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?
</li>
<li>
<input type="checkbox"/>
<strong>
A.4 Downstream bias mitigation:
</strong>
Have we considered ways to enable testing downstream results for biased outcomes (e.g., collecting data on protected group status like race or gender)?
</li>
</ul>
<br/>
<h2>
B. Data Storage
</h2>
<hr/>
<ul>
<li>
<input type="checkbox"/>
<strong>
B.1 Data security:
</strong>
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)?
</li>
<li>
<input type="checkbox"/>
<strong>
B.2 Right to be forgotten:
</strong>
Do we have a mechanism through which an individual can request their personal information be removed?
</li>
<li>
<input type="checkbox"/>
<strong>
B.3 Data retention plan:
</strong>
Is there a schedule or plan to delete the data after it is no longer needed?
</li>
</ul>
<br/>
<h2>
C. Analysis
</h2>
<hr/>
<ul>
<li>
<input type="checkbox"/>
<strong>
C.1 Missing perspectives:
</strong>
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)?
</li>
<li>
<input type="checkbox"/>
<strong>
C.2 Dataset bias:
</strong>
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)?
</li>
<li>
<input type="checkbox"/>
<strong>
C.3 Honest representation:
</strong>
Are our visualizations, summary statistics, and reports designed to honestly represent the underlying data?
</li>
<li>
<input type="checkbox"/>
<strong>
C.4 Privacy in analysis:
</strong>
Have we ensured that data with PII are not used or displayed unless necessary for the analysis?
</li>
<li>
<input type="checkbox"/>
<strong>
C.5 Auditability:
</strong>
Is the process of generating the analysis well documented and reproducible if we discover issues in the future?
</li>
</ul>
<br/>
<h2>
D. Modeling
</h2>
<hr/>
<ul>
<li>
<input type="checkbox"/>
<strong>
D.1 Proxy discrimination:
</strong>
Have we ensured that the model does not rely on variables or proxies for variables that are unfairly discriminatory?
</li>
<li>
<input type="checkbox"/>
<strong>
D.2 Fairness across groups:
</strong>
Have we tested model results for fairness with respect to different affected groups (e.g., tested for disparate error rates)?
</li>
<li>
<input type="checkbox"/>
<strong>
D.3 Metric selection:
</strong>
Have we considered the effects of optimizing for our defined metrics and considered additional metrics?
</li>
<li>
<input type="checkbox"/>
<strong>
D.4 Explainability:
</strong>
Can we explain in understandable terms a decision the model made in cases where a justification is needed?
</li>
<li>
<input type="checkbox"/>
<strong>
D.5 Communicate limitations:
</strong>
Have we communicated the shortcomings, limitations, and biases of the model to relevant stakeholders in ways that can be generally understood?
</li>
</ul>
<br/>
<h2>
E. Deployment
</h2>
<hr/>
<ul>
<li>
<input type="checkbox"/>
<strong>
E.1 Monitoring and evaluation:
</strong>
Do we have a clear plan to monitor the model and its impacts after it is deployed (e.g., performance monitoring, regular audit of sample predictions, human review of high-stakes decisions, reviewing downstream impacts of errors or low-confidence decisions, testing for concept drift)?
</li>
<li>
<input type="checkbox"/>
<strong>
E.2 Redress:
</strong>
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)?
</li>
<li>
<input type="checkbox"/>
<strong>
E.3 Roll back:
</strong>
Is there a way to turn off or roll back the model in production if necessary?
</li>
<li>
<input type="checkbox"/>
<strong>
E.4 Unintended use:
</strong>
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?
</li>
</ul>
<br/>
<br/>
<em>
Data Science Ethics Checklist generated with
<a href="http://deon.drivendata.org">
deon.
</a>
</em>
</body>
</html>