The student analyzes data used to train AI models to reduce biased and inaccurate outputs (levels 2 & 3).
Contributing Skills
- RSD 1: AI Data Quality Review | Review data quality for Artificial Intelligence (AI) models
- RSD 2: Biases in AI Systems and Mitigation Strategies Analysis | Analyze biases in Artificial Intelligence (AI) systems and how they can be addressed.
Evidence Statements
Level 2:
- ES 1: Given information about a dataset used to train an AI model, the student identifies potential sources of bias or misrepresentation.
- ES 2: The student discusses solutions for reducing the identified sources bias and inaccuracies in the datasets.
Level 3:
- ES 1: Given information about a dataset used to train an AI model, the student evaluates data for potential bias and inaccuracies.
- ES 2: The student proposes methods on training AI datasets to reduce bias and inaccuracies in outputs.
Associated Learning Resources and Student Activities
Forthcoming
Associated AI Literacy Competency Framework for Educators:
#2: Data Fluency
- List potential sources of bias or misrepresentation in datasets (level 1).
- Evaluate the completeness, consistency, timeliness, accuracy, and relevance of data (level 2).
- Engage in discussions, debates, or decisions, using data as a foundation to influence outcomes and drive informed decision-making (level 3).