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Ethics Principles


  • Its my job to understand, mitigate and communicate the presence of bias in algorithms.
  • Be responsible for maximizing social benefit and minimizing harm.
  • Practice humility and openness.
  • I will know my data and help future users know it as well.
  • Make reasonable efforts to know and document its origins and document its transformation.
  • Bias will exist. Measure it. Plan for it.
  • Thou shalt document transparently, accessibly, responsibly, reproducibly, and communicate.
  • Engaging the whole community. Do you have all relevant individuals engaged?
  • People before data - data scientists should use a question driven approach rather than a data-driven or methods approach. Consider personal safety and treat others the way they want to be treated.
  • Exercise ethical imagination.
  • Open by default - use of data should be transparent and fair.
  • I will not over/under represent findings.
  • You are part of an ecosystem understand context and provenance.
  • Respecting human dignity.
  • Respect their data even more than your own. Understand where its source is and think about the consequences of your actions.
  • Protecting individual and institutional privacy.
  • Diversity for inclusivity.
  • Attention to bias.
  • Respect for others/persons.
  • Be intentional as you work to create value.
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