Historical bias
A source of harm in which the training data accurately records a world that was already unjust, so a correctly built model faithfully reproduces the injustice. There is no data defect to repair; the fix is a governance decision about the objective or about whether to automate the decision at all.
Defined in 2 GAGE programs, which carry 3 distinct definitions of it. The wording above is taught in AI Governance: Applied Mastery.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A source of harm in which the training data accurately records a world that was already unjust, so a correctly built model faithfully reproduces the injustice. There is no data defect to repair; the fix is a governance decision about the objective or about whether to automate the decision at all.
Bias entering because the training data accurately records a past that was unfair, even when the data is complete and correct (Suresh and Guttag, 2021).
A skew that arises when data accurately records an unfair real world, so the AI learns the unfairness as a pattern; not caught by accuracy checks.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- AI Ethics: Bias, Fairness, and Accountability · Ethical and Responsible AI and Operational Governance, AI Literacy & Professional Conduct
- Data Literacy for AI: Datasets, Quality, and Pipeline Basics · Advanced AI Literacy, AI Literacy & Professional Conduct
- Where the bias came from: tracing a bad prediction to its data · Build Before You Govern, AI Governance: Applied Mastery
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.