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We are Data, Responsibly. We study the foundations of responsible data science, and build tools that translate our insights into data science practice.

Fairness

Algorithms discriminate just like humans do, but at a larger scale. Technology must be informed by ethical and legal considerations.

Diversity

Ensuring different kinds of objects are represented in the output of an algorithmic process.

Transparency

Users and regulators must be able to understand how raw data was selected, and what operations were performed during analysis.

Equality

Equality of opportunity and equality of outcomes enforce the similar treatment for similar people, believing the current dissimilarity is the result of past injustice.

Data protection

Responsibility by design, managed at all stages of the lifecycle of data-intensive applications.

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