6.6 Responsible analytical use

Data responsibility also concerns interpretation. An analysis can cause harm without revealing individual records. Poorly chosen groups can reinforce stereotypes. A model can systematically perform worse for an important population. A recommendation can disadvantage people who were underrepresented in the source data. A confident conclusion can hide uncertainty that matters to the decision.

Responsible review should therefore ask:

• Who is represented in the data, and who is missing?
• Which groups could be affected by errors?
• Does model performance differ across meaningful groups?
• Could a proxy variable reproduce sensitive distinctions?
• Are causal claims being made from associational evidence?
• Could the recommendation create an unfair burden or exclusion?