- 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.