Bias in AI

Sampling bias, representation and fairness checks

Class 10 · Guided experience

VISUAL CONCEPT MAP · ETHICS

Bias in AI

Explore how choices about data and model design affect people and outcomes.

Input / evidence Processing Output / insight

Mission briefing

A hiring model is trained on historical data from only one group.

Explore the concept, change an input, observe the result and explain what it means.

⚖ RESPONSIBLE AI REVIEW DEPLOYMENT CHECKLIST

FairnessCompare errors across relevant groups. PrivacyMinimise and protect personal data. Human reviewAllow challenge and human intervention. TransparencyExplain purpose and limitations.

Safeguards selected 2 / 4Needs review

✎ Observation log

Record what you observed, what changed and one limitation.

CBSEMOCK AI & ML Virtual Labs · Educational conceptual model. Outputs are illustrative, not evidence of a validated or trained production AI system. Confirm class-wise syllabus alignment before assessment.

Teacher review · Practical rubric

Suggested assessment framework. Teacher may adjust marks to suit the class and task.

Concept understanding · /2Experiment and method · /3Analysis and interpretation · /3Conclusion and reflection · /2

Teacher rubric score: 4 / 10

Complete record includes student details, practical notes, experiment title, date and teacher rubric. Existing lab-specific notebook and activities are retained.