Our Methodology
The B.I.T. Framework™
The Behavioral Impact Test (B.I.T.)™ is CAIBS™'s proprietary evaluation methodology. It measures AI systems across five critical dimensions, each scored from 0 to 5, for a total possible score of 25.
CAIBS-STD-001 v1.0First Published: January 2026Author: SSG Norwood Williams Jr.
Decision Impact
0-5
Actionability
0-5
Behavior Change
0-5
Accountability
0-5
Real-World Results
0-5
Total Possible Score
25
Detailed Scoring Criteria
Each dimension is evaluated against specific criteria with a clear scoring rubric.
1. Decision Impact
Does the AI help users make better decisions?
Evaluation Criteria
- Provides relevant, timely information for decision-making
- Reduces cognitive load in complex scenarios
- Offers clear recommendations with supporting evidence
- Adapts recommendations based on user context
- Measures and improves decision quality over time
Scoring Rubric
0No decision support capability
1Basic information presentation
2Contextual information filtering
3Personalized recommendations
4Predictive decision guidance
5Proven decision quality improvement
2. Actionability
Does the AI provide actionable outputs?
Evaluation Criteria
- Outputs can be directly acted upon by users
- Clear next steps are provided with each interaction
- Reduces friction between insight and action
- Integrates with user workflows and tools
- Tracks action completion and follow-through
Scoring Rubric
0No actionable outputs
1General suggestions only
2Specific, implementable recommendations
3Workflow-integrated action items
4Automated action execution
5Proven action completion improvement
3. Behavior Change
Does the AI drive positive behavior change?
Evaluation Criteria
- Identifies target behaviors for improvement
- Uses evidence-based behavior change techniques
- Provides feedback loops for behavior reinforcement
- Adapts interventions based on user progress
- Measures sustained behavior change over time
Scoring Rubric
0No behavior change capability
1Basic habit tracking
2Nudges and reminders
3Personalized behavior interventions
4Adaptive behavior modification
5Proven sustained behavior transformation
4. Accountability
Does the AI have transparency and accountability mechanisms?
Evaluation Criteria
- Explains reasoning behind recommendations
- Provides audit trails for decisions
- Allows users to override or correct AI outputs
- Reports on accuracy and reliability metrics
- Undergoes regular third-party evaluation
Scoring Rubric
0No transparency or accountability
1Basic output explanations
2Decision audit trails
3User override and correction capabilities
4Self-reporting accuracy metrics
5Third-party verified accountability
5. Real-World Results
Does the AI produce measurable real-world outcomes?
Evaluation Criteria
- Defines clear, measurable outcome metrics
- Tracks outcomes over meaningful time periods
- Demonstrates statistically significant improvements
- Provides ROI or impact quantification
- Publishes outcome data for independent verification
Scoring Rubric
0No measurable outcomes
1Anecdotal outcome reports
2Basic outcome tracking
3Statistically significant improvements
4Independently verified outcomes
5Published, peer-reviewed outcome data