This resource explains the importance of incorporating ethical review and responsible data science practices into projects. The post explains how to embed an ethical review process into each stage of the project rather than limiting this work to checkpoints at a project’s beginning , middle, and end. The summary of best practices and lessons learned in responsible data science mirrors DataKind’s six-step project process, and emphasizes their ongoing commitment to continuous improvement with feedback from the broader community.
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Using your Data Responsibly and Ethically
Data is a powerful resource that many organizations have but it needs to be managed and stewarded with care. We must ensure that our data practices are in alignment with our overall organizational values, and that we do not increase the vulnerability of the people that we serve.
Resources from Responsible Data, IDEO and +6