Will AI Soon Have Credit Scores of Trust?

Imagine checking your AI’s trust score before using it, much like checking a credit score before a loan. What if every AI system carried a dynamic trust index based on performance, fairness, user feedback, and regulatory compliance?

This isn't just theoretical. A growing number of indicators, surveys, emerging tools, and frameworks, suggest trust scores for AI are both possible and urgently needed.

Why We Need Trust Scores Now

• Trust gaps are real and widening.: Consumers may increasingly accept AI (e.g., for health or finance), but only under certain conditions. In the UK, nearly half of adults say they’d take health advice from AI, but insist on transparency and human oversight. Yet, trust as a concept remains fragile and volatile.

• Trust gaps vary by region: Globally, public confidence in AI varies dramatically. For example, 72% of people in China trust AI, compared to just 32% in the U.S. These gaps influence policy, adoption, and perception—and could be surfaced via trust indices.

Emerging Foundations for AI Trust Scoring

Research Tools & Industry Frameworks

• VizTrust: A real-time visual analytics tool that maps how user trust evolves during interactions, based on competence, integrity, predictability, and benevolence 

• Trust Calibration Maturity Model (TCMM): A robust scoring framework evaluating AI trust maturity across performance, bias, transparency, safety, and usability

• Multisource AI Scorecard Table (MAST): Validated via experiments, this checklist aligns system design attributes with user trust perceptions

Real-World Industry Tools

• Model Trust Scores (by Credo AI) help enterprises qualify foundational models against security and compatibility needs, acting as a rudimentary "score" for AI components credo.ai.

• AI Trust Index™ (Medium concept) combines dimensions like fairness, explainability, equity, and accountability into a public assessment framework—imagine ESG meets AI ratings 

  
  

How Trust Scores Could Transform AI Ecosystems

1. Consumer Guidance: Platforms could display AI trust badges, reflecting transparency, audit history, bias mitigation, etc. helping users quickly assess whether to accept recommendations.

2. Regulatory Alignment: Trust scores could tie directly into the EU’s AI Act framework, where high-risk and general-purpose AIs must meet transparency and conformity standards A trust score could be a dynamic compliance signal.

3. Competitive Differentiation: With regulation becoming the norm, AI providers could signal advantage by achieving higher trust ratings, boosting user adoption and market credibility.

4. Accountability & Feedback Loops: Trust scoring systems could evolve through continuous monitoring (like VizTrust) and user input, driving ongoing improvement in AI behavior and credibility.

The Future Is Score-Based Trust—and It’s Closer Than You Think

Component	Real-World Basis
Surveys & Behavior	Consumer trust trends show what matters (e.g., transparency, oversight)
Frameworks & Models	TCMM, MAST, Model Trust Scores, AI Trust Index—all foundational tools exist
Regulatory Fit	EU AI Act provides structure; trust scoring could become a compliance KPI
Feedback Dynamics	Real-time tools like VizTrust enable adaptive trust earning

Final Thoughts

Whilst THEMIS is not about trust scoring per se, incorporating a dynamic AI Trust Score into its toolkit could make AI trust meaningful, measurable, and actionable. After all we want everyday users being able to see, adjust, and understand an AI’s trust profile before, and as, they use it.

This shifts trust from an ephemeral concept into a tangible metric, one that spans ethics, user experience, regulation, and performance, all in one unified score.