Evaluating AI Contract Terms When Standards Don't Exist

Why Traditional Scoring Fails for AI Clauses

TermScout has built its reputation on objective contract evaluation. The platform analyzes over 750 data points in every agreement, scores them algorithmically, and produces ratings that legal, procurement, and sales teams trust.

But there's a category of provisions conspicuously absent from this scoring: AI clauses in contracts. Provisions about training data usage, model transparency, algorithmic accountability, and AI-specific security don't currently factor into favorability ratings.

This isn't an oversight. It's a deliberate choice rooted in how contract intelligence should work when underlying standards are still forming.

Scoring requires benchmarks. For traditional provisions, these benchmarks exist. For AI clauses, they're still taking shape.

The Missing Benchmark Problem

TermScout's favorability scoring compares each contract against thousands of real-world agreements. When analyzing a liability cap, the platform determines whether that provision is vendor-favorable, customer-favorable, or balanced.

This comparison only works when market practices have stabilized enough to create meaningful benchmarks. Most SaaS contracts cap liability at 12 months of fees. Deviation from this norm signals vendor favorability or customer favorability.

Why AI clauses break this model

AI clauses lack this maturity. Consider training data usage:

• Some vendors explicitly prohibit using customer data to train models
• Others reserve broad rights for "service improvement"
• Still others allow training on aggregated data, but not individual customer information
• Many contracts remain completely silent on training data

Which approach represents the market standard? There isn't one. The market is still figuring out appropriate boundaries. Vendors experiment with different approaches. Buyers develop preferences based on their specific risk tolerances.

When different doesn't mean wrong

Even within specific industries, AI clauses show remarkable variation. Two enterprise vendors with similar products might take completely opposite approaches to data training rights.

Neither approach is objectively wrong. They reflect different business models and value propositions. A buyer wanting data to improve the product might prefer broad usage rights. A buyer concerned about proprietary information leaking into competitor-accessible models would prefer strict prohibitions.

Traditional favorability scoring assumes more permissive customer terms are "better." With AI clauses, this assumption breaks down. Sometimes restrictions protect both parties by reducing liability. Sometimes broad permissions enable functionality customers actually want.

The silence problem

Many contracts simply don't address AI-specific issues yet. When TermScout analyzes a contract silent on training data usage, how should that silence be scored?

Possible interpretations:

• Vendor-favorable because it leaves the vendor free to train on customer data
• Customer-favorable because no explicit permission was granted
• Neutral because neither party contemplated AI functionality when drafting
• Impossible to determine without additional context

For mature provisions, silence carries clear implications. If a contract lacks liability caps, that's vendor-favorable. For AI clauses, silence might mean anything from "we're not using AI at all" to "we're using AI extensively but haven't updated contracts yet."

A Different Evaluation Framework

Disclosure beats favorability

The key insight: AI clauses require different evaluation. Instead of asking "which party gets more favorable terms?" the question becomes "has the vendor clearly disclosed their AI practices and committed to specific standards?"

This shift reflects the nature of AI risk. Traditional provisions allocate known risks. Both sides understand what liability caps do and what termination rights enable.

AI clauses often address risks still being understood. Questions about how training data affects model behavior, whether AI outputs create copyright liability, or how to audit algorithmic decision-making lack settled answers. In this environment, disclosure matters more than allocation.

The seven disclosure principles

Safe AI certification evaluates whether contracts address key disclosure categories:

1. Use restrictions. What customers can and cannot do with the AI service
2. Transparency and explainability. How AI systems work and decisions are understood
3. Data usage and ownership. How customer data trains models or creates outputs
4. Security and incident response. Protections against AI-specific vulnerabilities
5. Transparent communication. Commitments to disclosure about changes or incidents
6. Best practice commitment. Ongoing adherence to evolving standards
7. Legal compliance. Alignment with applicable AI laws and regulations

The certification validates that vendors addressed each category clearly enough for buyers to make informed decisions. It doesn't rate whether the vendor's approach is more or less favorable than competitors.

The Two-Tier Certification Structure 

Why core certification comes first

TermScout requires vendors to earn core Certify certification before pursuing safe AI certification. This sequencing is intentional.

Core certification validates that traditional provisions (liability, indemnification, warranties, payment terms) meet fairness standards and contain no deal breakers.

A contract could have excellent AI disclosure but terrible liability clauses. Or it might be perfectly balanced on traditional terms while completely silent on AI concerns. Comprehensive evaluation requires both lenses.

When standards mature

As market practices around AI clauses mature, some aspects might eventually migrate into traditional scoring. If industry consensus emerges around specific practices (e.g., training data requires opt-in consent), TermScout's methodology could begin evaluating whether specific contracts meet that benchmark.

But this transition happens selectively and only when data supports it. Different AI provisions will mature at different rates.

Who Benefits

Legal teams

Gain a disclosure-focused framework that replaces vague favorability ratings with hard facts. Counsel can verify if specific AI commitments meet company standards instead of guessing.

Procurement

Enable apples-to-apples comparisons between complex AI contracts. Standardized disclosure principles let teams evaluate vendor transparency without oversimplified scores.

Sales teams

Combine balanced contract terms with clear AI disclosures. This proactive approach reduces deal friction and offers competitive advantage over vendors with vague policies.

Building Trust Through Honesty

Excluding AI clauses from favorability scoring isn't a limitation. It's a feature.

Contract intelligence that acknowledges when benchmarks don't exist yet builds more trust than analysis that manufactures scores where comparison lacks meaning.

AI clauses represent genuinely new territory where market practices are still forming. Forcing these provisions into frameworks designed for mature terms produces unreliable results that mislead users.

Safe AI certification provides what AI clauses actually need: systematic evaluation of whether vendors disclosed practices clearly, committed to specific standards across risk categories, and addressed concerns that sophisticated buyers are asking about.

For vendors, this approach offers credibility. Earning both certifications proves commitment to fairness in traditional terms and transparency in AI practices. For buyers, it provides structure for systematic evaluation without imposing premature standardization on provisions where legitimate diversity remains.

Frequently Asked Questions

Why don't you score AI clauses like traditional contract provisions? Market benchmarks for AI clauses don't exist yet. Forcing them into favorability scoring would produce unreliable results. Disclosure matters more than scoring when standards are still forming.

What is safe AI certification? An evaluation framework focused on disclosure rather than favorability. It validates whether vendors address seven key risk categories with clear, transparent commitments.

Do I need both core and safe AI certification? Core certification validates traditional provisions. Safe AI certification addresses AI-specific concerns. Vendors whose value proposition centers on AI capabilities should pursue both.

How is disclosure-based evaluation different from scoring? Scoring asks "which party gets better terms." Disclosure asks "has the vendor been transparent about practices and committed to standards." For AI, transparency is more important than favorability allocation.

Will AI clauses eventually get favorability scores? Yes, selectively, as market standards mature and clear benchmarks emerge. Different AI provisions will mature at different rates.