From Research Framework to Practical Tool
ALMG transforms AI interpretability from qualitative observation to quantitative measurement. Here's how organizations can apply the framework to improve safety, accessibility, and user experience.
Replace ad-hoc testing with geometric coordinate system. Measure ΞZ scores across demographics, body types, and cultural contexts. Create standardized bias metrics comparable across models.
Calculate Net_Z before testing. Predict failure points systematically. Navigate to high-risk coordinates deliberately rather than discovering them by accident.
Map SGWI profiles before and after safety training. Measure well depth changes quantitatively. Identify if training reduces specific distortions or creates new ones.
Distinguish necessary caution from overcorrection. Identify wells that block legitimate use cases. Optimize for both harm prevention AND user access.
Compare SGWI profiles across GPT-4, Claude, Grok, Gemini. Identify systematic patterns vs. model-specific quirks. Track industry-wide bias trends.
Track well depth changes over time. Detect if new training introduces unintended distortions. Maintain bias baseline measurements for regulatory compliance.
Scenario: After implementing new safety training, test if bias patterns improved or worsened.
ALMG Application: Map complete SGWI before training. Re-map after training. Calculate ΞZ changes for each well. Identify: (1) successfully reduced wells, (2) unchanged wells, (3) newly created or deepened wells. Quantify net improvement with specific metrics.
Transparency Mode: Show users the calculated (X,Y,Z) coordinates of their request. Explain which wells were activated and what ΞZ scores resulted.
Legitimacy Signals: When request hits negative Net_Z, suggest specific signals to add (professional context, credentials, clear purpose) to shift Z-axis.
Override Explanation: When system refuses, explain in ALMG terms which well activated and what override threshold is needed, rather than generic policy citations.
Not "AI is broken" but "request landed in high-ΞZ region." Clear diagnosis: which well activated. Clear solution: which legitimacy signals to add.
Calculate coordinates before prompting. Add strategic legitimacy signals. Navigate to collaborative zones. Predict response quality before generating.
Ritual knowledge made explicit. Multiple legitimacy pathways documented. Class barriers reduced through transparency. Users without credentials can navigate effectively.
Teach students about AI bias through geometric framework. Interactive tools showing coordinate manipulation. Hands-on exploration of gravity wells.
Problem: Physician needs to discuss adolescent development but AI refuses due to youth + body wells.
ALMG Solution: Physician learns to front-load legitimacy signals: "I'm a board-certified pediatrician at [institution] writing patient education materials for adolescent health..." This shifts Net_Z from -1.4 (refusal) to +0.3 (collaboration). Physician gets needed content systematically, not through trial and error.
ALMG provides standardized methodology. Protocols documented in scrolls. Results comparable across research groups. Enables meta-analysis of AI bias.
How do wells evolve with model scale? Are distortions consistent across languages? Do fine-tuned models inherit base model wells? Can wells be surgically removed?
ΞZ scores provide numerical measurements. Statistical analysis possible. Hypothesis testing with concrete predictions. P-values, confidence intervals, effect sizes.
Connects CS, psychology, sociology, ethics. Geometric framework accessible to non-technical researchers. Enables collaboration across domains.
ALMG provides quantitative framework for mandated bias testing. Regulators can require companies to publish SGWI profiles. Comparable metrics across vendors enable informed decision-making.
Companies could be required to disclose: documented gravity wells, override protocols, legitimacy hierarchies, and Net_Z calculation methods. Makes AI decision-making legible to oversight bodies.
Measure ΞZ gaps between demographic groups. Identify systematic disparities in access. Evaluate whether safety training creates unjust barriers for marginalized users. Enforce equity standards.
Models pass safety certification if SGWI profile meets standards: no wells deeper than threshold, asymmetries within acceptable ranges, override protocols responsive to legitimate signals.
Geometric framework makes AI behavior accessible to non-technical policymakers. "Gravity wells" and "coordinate systems" provide intuitive metaphors. Enables informed legislation without requiring deep ML expertise.
Apply ALMG to specific model. Map complete SGWI. Validate prediction accuracy. Publish findings. Establish baseline metrics.
Build automated SGWI mapping tools. Create Net_Z calculators. Develop override recommendation systems. Integrate with existing evaluation pipelines.
Monitor well formation during training. Target reduction of specific wells. Balance safety and accessibility. Validate improved SGWI profiles.
Transparency mode showing coordinates. Legitimacy signal suggestions. Override threshold explanations. Educational resources for users.
Whether you're conducting research, developing AI systems, or setting policy, ALMG provides quantitative tools for measuring and improving AI behavior. Let's discuss how to apply the framework to your specific use case.