What measurable results are you looking to achieve?

[mostakimvip06]

Based on the desired specific improvements in responsible AI, here are the measurable results I would be looking to achieve. These metrics aim to quantify progress across safety, fairness, interpretability, and governance, recognizing that many aspects still require qualitative assessment.

1. Enhanced Safety & Alignment (Quantifying Harm Reduction & Alignment)
Reduction in Harmful/Policy-Violating Outputs (e.g., for Generative AI):
Metric: Decrease in the rate of identified harmful, biased, or policy-violating outputs from AI models (e.g., hate speech, misinformation, self-harm, deepfakes, discriminatory content).
Measurement: This would be measured through automated detection buy telemarketing data systems (e.g., safety classifiers, prompt/response filters) and human evaluation (e.g., red teaming exercises, human rater evaluations).
Target: A year-over-year reduction of X% in critical safety violations, striving towards a near-zero incidence rate for highly egregious harms.
"Jailbreak" and Adversarial Robustness:
Metric: Reduction in the success rate of adversarial attacks or "jailbreak" attempts designed to bypass safety filters or elicit harmful responses.
Measurement: Quantified through red-teaming exercises and internal security audits.
Target: Achieve a Y% reduction in the success rate of known adversarial techniques within a specified timeframe (e.g., 6-12 months post-deployment for a major model update).
Alignment Score Improvement:
Metric: Development and improvement of internal "alignment scores" that quantitatively assess how well an AI model's behavior aligns with established ethical principles and human values across various scenarios.
Measurement: These scores would be derived from structured human evaluations, simulated environments, and potentially novel automated alignment evaluation techniques.
Target: A Z% increase in the average alignment score across key AI models within the next year.