Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Press/Media
Datasets
Activities
Fellowships, Honors, and Prizes
Search by expertise, name or affiliation
Auditing the AI Auditors: A Framework for Evaluating Fairness and Bias in High Stakes AI Predictive Models
Richard N. Landers
, Tara S. Behrend
Psychology (Twin Cities)
Research output
:
Contribution to journal
›
Article
›
peer-review
56
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Auditing the AI Auditors: A Framework for Evaluating Fairness and Bias in High Stakes AI Predictive Models'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Auditors
100%
Artificial Intelligence
100%
Predictive Models
100%
High Stakes
100%
Human Behavior
33%
Cultural Contexts
33%
Psychological Characteristics
33%
Individual Attitudes
33%
Standardized Approach
33%
Ethical Aspects
33%
Source Data
33%
Government Ethics
33%
Watchdog
33%
Artificial Intelligence Systems
33%
Respect for Persons
33%
Data Design
33%
Disciplinary Perspectives
33%
Prototypicality
33%
Interdisciplinary Understanding
33%
Design Output
33%
Third-party Observers
33%
Artificial Intelligence Models
33%
Development Features
33%
Data Development
33%
Decision Tool
33%
Bias in AI
33%
Technical Domain
33%
Psychology
Artificial Intelligence
100%
Cultural Contexts
16%