The fixed fee is nearly signed. The model demoed cleanly against sample data. Then counsel sends the note that rewrites the procurement conversation:
Can we explain what this model is doing in terms a regulator or auditor will accept, and is that standard written into the contract before build?
Accuracy is not the issue. The issue is whether the agreement makes the explanation standard enforceable. What format counts? Who reviews it? How fast must it be available? Which decision records do you own at handoff?
Write those answers into the Statement of Work (SOW) before code starts. Then the build has a definition of done counsel can defend. Leave them as vendor assurances, and procurement closes while the audit path stays open.
Board approval and discovery move fast. Legal and compliance join while scope is still negotiable, and counsel’s question sets the tone for everything that follows. For executives in financial services, healthcare, and other regulated industries, the contract has to name the decisions that will be challenged:
- Loan decisions that can be justified to regulators in plain language
- Patient triage recommendations that can be audited decision by decision
- Hiring filters that can survive legal review with a clear rationale trail
- Automated recommendations your business can stand behind when a customer or counsel asks why
- Personalized offers, audience segments, and content paths that can be explained when counsel asks why someone saw a specific recommendation
Growth and marketing leaders feel the same pressure on a different surface. Personalization engines, offer targeting, audience scoring, and content recommendations look like marketing wins until counsel asks whether you can explain each recommendation in plain language a customer, brand partner, or auditor will accept. If the answer lives only in a vendor dashboard, the channel lift becomes a trust problem the first time someone pushes back.
Boards and counsel expect a written explainable AI standard when AI moves from pilot to production. That is not a promise that “the model is explainable.” It is a written answer for what explanation looks like, who reviews it, and when it must be available.
Responsible AI requirements are tightening across the U.S., the EU, and other major markets. Board-level AI mandates now come with real deadlines. Organizations that scale well spell out the explanation standard clearly enough that a delivery partner can build to it, and unosquare can encode into the SOW before build begins.
What These Terms Actually Mean for Your Contract
Counsel’s note only becomes enforceable once legal, compliance, and the delivery partner share the same meaning for three ideas behind explainable AI (XAI): explainability, interpretability, and transparency. Vendor decks often blur them. The table below keeps the business definitions separate, because the difference matters the moment someone has to approve acceptance criteria.
| Term | Business Definition | One-Line Example |
|---|---|---|
| Explainability | Can someone outside the model team read why a decision happened and act on that answer? | Plain-language reasons for an automated credit decision, reviewed by compliance |
| Interpretability | Can a reviewer follow the logic the model used, not just the score it produced? | An interpretable scoring formula with visible inputs and weights that regulators can review |
| Transparency | Can counsel see where the data came from, how the system was built, and how it runs in production? | A contract-backed inventory of data inputs and processing steps, signed off by legal counsel |
Transparency is not a dashboard. It is a written record of what training data the model used, how that data was prepared, how the model was built, and how individual decisions connect to business outcomes.
Contract-level transparency means your compliance team can read that record, your auditors can verify it, and your legal counsel can cite it when a regulator asks why a decision was made. NIST guidance on trustworthy AI{2} treats that visibility as a practical requirement, not a slide claim.
“The model will be explainable” is not a deliverable. “The model will produce a plain-language justification for each decision, reviewable by the compliance team within 48 hours” is. Soft promises leave you renegotiating at signing. The measurable explainable AI standard is what the fixed fee has to cover, or the fee only buys a budget ceiling.
Why Fixed Pricing Alone Does Not Make Explainability Enforceable
A fixed fee sets the budget boundary. It does not, by itself, turn that 48-hour justification into a definition of done for explainable AI. Finance may celebrate the fixed fee. Counsel inherits whatever explanation package the contract actually ships.
When acceptance criteria name the explanation format, the review window, the decision record, and production readiness early, the fixed fee covers the explanation outcome the business meant to buy. That outcome then sits beside the price as an enforceable obligation.
In practice, a partner delivers a working prototype in week one, grounded in your data and your constraints, including a sample of how decisions will be explained. By week two, explanation formats, compliance review windows, and model documentation sit in the acceptance criteria. All of that lives in the original contract before build begins, so legal, compliance, and delivery share one written definition of done.
Responsible AI delivery writes the XAI standard into the contract before build begins:
- What “explained” means for each decision type (format, language, review window)
- Pass/fail acceptance tests counsel can run on sample explanations
- Who owns the decision records and explanation outputs at handoff
Those three items are what counsel is really asking for in the opening note. The next step is to turn them into five contract checkpoints so nothing gets lost between legal review and delivery.
TRACE: Five Answers Legal and Audit Need Before Build
The three items above expand into five TRACE answers counsel, compliance, and audit can hold a delivery partner to before code starts. TRACE walks those answers in the order procurement actually moves.
unosquare writes each XAI outcome into the contract before build begins when you commission an explainable AI build under the fixed-fee model. Bring the checkpoints into Discovery and contract negotiation, and into the prototype review, so you can confirm each answer already lives in the contract.
| Letter | Checkpoint | What gets settled in the SOW | Example contract language |
|---|---|---|---|
| T | Terms of acceptance | Pass/fail tests for explanation format, timing, and coverage by decision type | “For each adverse decision, the system produces a plain-language justification, available to compliance within 48 hours, validated against counsel-approved templates before acceptance.” |
| R | Rights transfer | Source code, model files, decision records, explanation outputs, the ability to regenerate those explanations after handoff, and monitoring tools transfer to your team | Signed handover checklist as a condition of final payment |
| A | Accountability map | Which explanation obligations apply and who designs, validates, and remediates | Explanation standards table (which decision types need which explanation, signed by counsel) in the contract annex before build |
| C | Client-environment proof | Explanation quality verified on your data, in your workflows, against counsel-approved templates | Acceptance tests for every in-scope decision type, template match, retrieval time, failure behavior, and monitoring coverage |
| E | Economic guardrails | Written change-order path when explanation standards evolve after week two locks design and price | Milestone payments tied to explanation deliverables; in-scope overruns stay with unosquare |
Each TRACE checkpoint turns a procurement question into contract language counsel can enforce. Soft promises become tests compliance can run. Counsel signs a sample set. The model summary stays readable to non-engineers. Outputs appear for every in-scope decision type. A monitoring view shows explanation generation on the workflows your team will operate.
Rights transfer and the accountability map settle who holds the trail when challenged. Depending on the use case, that map can mean adverse-action notice rules, sector-specific justification requirements, privacy rules such as GDPR, health-data rules such as HIPAA, credit-notice rules such as FCRA, or internal audit expectations for keeping decision records. unosquare puts that explanation standards table in the SOW for regulated engagements, with counsel review before the build begins.
After week two locks design and price, explanations must still pass on your data and workflows, not only in a partner-controlled demo. When counsel asks for a richer format, a tighter review window, or coverage of another decision type, the written change-order path is already in place.
Explainable AI is not one universal standard. In regulated industries, it carries specific legal and operational expectations that vary by sector and region. Those expectations belong in the contract as explanation measures, not as a separate policy exercise after go-live.
| Sector | Region | Compliance Action | Contractual Measure |
|---|---|---|---|
| Healthcare | EU | Limit data collected; track patient consent | Documented data flows, warranties, legal counsel signoff |
| Financial Services | US | Explain adverse actions; keep decision records auditable | Traceable decision inputs, archived decision records, indemnities tied to regulatory risk |
| Publishing and Media | Global | Show how personalization uses data; honor data-subject rights | Consent tracking, opt-out flows, documented mitigations in contracts |
Ethical AI considerations now sit alongside regulatory requirements. Regulators and investors look for organizations that can explain a decision and show that the decision does not systematically disadvantage protected groups{2}.
Fairness testing should sit in acceptance criteria alongside explanation requirements so you can address bias in the contract, before the system is live. unosquare folds those fairness obligations into the same explanation standards table counsel signs for regulated builds.
With TRACE written into the deal, the next question is whether your specific use case can pass counsel’s review. Credit decisioning is the clearest test; marketing personalization runs the same pattern on a different surface.
Put the Explanation Standard in Writing Before Counsel Blocks the Build
If procurement is nearly signed and counsel still asks whether the explanation will hold up to a regulator, do not price the build on the demo alone. In week one, a prototype against your regulatory constraints surfaces the explanation standard counsel will accept, so it can sit in the Statement of Work before anyone builds.
Example: Credit Decisioning AI Your Board Can Explain
Credit decisioning is where counsel’s review stops being theoretical. A mid-market lender gets a board mandate to put AI into credit decisions because the model can score applications faster than the current manual path.
The build holds or fails on one question: can every adverse action{3} be explained in terms counsel, auditors, and regulators will accept? And is that standard written into the contract before build?
Explainable AI here is a contract deliverable, not a model feature. For adverse actions, that means a plain-language justification reviewable by compliance within a defined window, plus ownership of the decision records and proof that those justifications pass on your lending workflows before acceptance.
If the scoring path relies on black box models or deep learning, the SOW can still require SHAP evidence behind that plain-language record so regulatory compliance teams can see which inputs most influenced the decline.
unosquare‘s week-one prototype surfaces those regulatory exposures and draft explanation acceptance criteria against your real constraints, so acceptance criteria and price sit in the SOW before build begins. Your team leaves with full ownership of the system, including the explanation trail.
A growth leader feels the same pressure from the launch calendar. The personalization or offer engine is ready to scale, but sign-off waits until someone can explain each recommendation in plain language a customer or auditor will accept. So name that standard in the SOW before the build is priced, the same way you would for an adverse credit decision.
Credit decisioning is one concrete case. The same five TRACE answers apply whenever legal, compliance, or the board will ask how a decision was made, and whether that answer was scoped before code started. What the SOW must name next is the business outcome each explanation has to serve, not the technical method that produces it.
What to Write in the SOW: Business Need, Not Method Choice
You do not need to choose which machine learning algorithm produces the explanation. You need to name the business outcome the explanation must serve, write that outcome into the SOW, and define a pass/fail acceptance test. That is the same outcome counsel is testing in the credit and marketing cases above.
The delivery partner selects the technical approach, including whether interpretable machine learning models or other XAI methods are the better fit. Data scientists may care about the method; counsel cares whether the justification is reviewable by non-engineers.
That rule holds when the build uses black box models (systems whose internal logic is hard to read directly), including deep learning stacks or neural networks. The SOW still names the business outcome first.
Optional techniques the SOW can require evidence from include SHAP (a method that shows which inputs most influenced a decision), LIME (a local explanation method for a single decision), feature importance summaries, post-hoc explanations applied after the model scores a case, and counterfactual explanations that state what would need to change for a different result. Decision trees and similar readable models can support model transparency without those add-ons. Counsel does not pick the toolkit; counsel signs the acceptance test the toolkit must satisfy.
NIST’s explainability principles{1} reinforce the same idea: explainable AI exists for people who must act on the answer, not as a research exercise.
| Business need | What to write in the SOW | Example acceptance test |
|---|---|---|
| Defend an individual decision to counsel or a regulator | Each in-scope decision produces a plain-language justification tied to the inputs that drove it | Compliance reviews a sample set against counsel-approved templates and signs off; where useful, SHAP or feature importance evidence can sit behind that plain-language record |
| Show what would need to change for a different outcome | For declined or adverse decisions, the system states what would need to change for a different result (in customer-safe language where required), including counterfactual explanations when the use case needs them | Legal and operations accept that the “what would need to change” language meets notice and customer-communication standards |
| Compare model versions before a go-wider decision | Explanation outputs stay consistent enough that auditors can compare decisions across versions | Audit sample shows comparable justification structure before and after a model update |
| Keep customer or brand communications defensible | High-risk recommendations (personalized offers, audience segments, content paths) leave a reviewable decision explanation for each recommendation type | Brand or compliance spot-checks a live sample within an agreed review window and confirms counsel can answer “why this person saw that offer or article” |
| Prove the explanation path still runs in production | Missing or failed explanations are visible to a named owner | Monitoring view shows explanation generation for every in-scope decision type during acceptance |
SHAP, formally Shapley Additive Explanations and grounded in Shapley values, is one optional evidence source for “which inputs mattered,” not a substitute for the plain-language justification counsel must approve. LIME, short for Local Interpretable Model-Agnostic Explanations, is another optional technique for a single decision when the SOW asks for local evidence.
A simple visualization of those drivers can help non-engineers review a sample set during acceptance. None of these methods replace the written standard; they are ways the delivery partner can prove the standard was met.
Generative AI systems that draft summaries, recommendations, or customer communications still need a written standard, including builds that use large language models (LLMs) or natural language processing for customer-facing text. Name how outputs are validated, how errors are caught, and how unsupported claims{4} are handled. Name what decision record is kept and who reviews high-risk outputs before release.
Write those requirements into the SOW and you commission a system your teams can operate with confidence and regulators can review with a clear explanation trail. Where the use case needs interpretable machine learning rather than a generative AI wrapper alone, say so as a business outcome (reviewable logic, consistent justifications), not as a method shopping list.
unosquare writes those explainable AI commitments into the SOW before build begins. The delivery partner handles the technical work once the buyer has named what “explained” means.
Once those outcomes are named, the next job is to see when each one becomes contract proof rather than a delivery status update.
From Draft Language to a Defensible Timeline
The SOW language above carries forward from draft markup to a system counsel can defend. Each phase produces a different piece of contract proof for the TRACE answers already in the deal.
Week 1 (Discovery): Counsel and compliance review sample decision explanations generated from your data, not a partner-controlled demo set. The prototype delivers draft pass/fail acceptance criteria, a regulatory exposure register, and a first read on which explanation formats your auditors will expect, including whether post-hoc explanations or SHAP-backed samples help the review. unosquare‘s week-one outcome is language counsel can mark up before procurement moves forward, not a slide that says the model is explainable or that XAI is “built in.”
Week 2 (Architecture and Pricing Freeze): Design, explanation acceptance tests, and price lock in one SOW conversation. Changes after week two follow a written change-order path. Builds start as low as $100K, scoped before code is written.
Weeks 3–11 (Build): The explanation standards table (which decision types need which explanation) earns counsel sign-off mid-build. Milestone reviews run on your data and workflows: whether justifications match counsel-approved templates, whether decision records exist for every in-scope type, and whether missing explanations trigger alerts before an auditor would.
Week 12 (Deploy and Handoff): Rights transfer covers the full explanation trail. That includes source code, model files, and decision records. It also includes the ability to regenerate explanations without calling the vendor, plus the monitoring your operators need. Final acceptance runs in production against the SOW criteria. Counsel, compliance, and operations sign off together, and the handover checklist is a condition of final payment.
Total timeline from first call to production handoff is typically 8 to 12 weeks. unosquare has completed 2,500+ projects across 16 years, with a client NPS in the top 1% of B2B services. Regulated explanation builds ship on systems that meet SOC 2 and HIPAA where those standards apply, on the cloud platforms your team already runs.
By week 12, the explanation standard enforceable in the SOW is either in production or it is not. The readiness check below tells you whether to start that clock now.
Is This Explainability Build Ready to Scope?
The week-twelve clock only starts when the business already has an explainable AI outcome counsel will accept, budget authority, and someone appointed to own the engagement. That usually means at least two of these signals are present:
- Legal is already asking how board-mandated decisions will be explained
- A pilot needs production-grade explanation standards
- An audit is approaching and will require decision rationales
- A personalization, offer, or content engine is blocked because counsel cannot explain individual recommendations
- A competitor is shipping AI you cannot yet defend
- A manual process could move to a model if every decision can be justified
| Question | What You Are Checking |
|---|---|
| Is there an executive sponsor who can sign off on explanation acceptance criteria? | Commissioning ownership |
| Has legal reviewed what “explained” must mean for regulators and counsel? | Explainability readiness |
| Is there a named data-access contact who can support prototype work? | Discovery feasibility |
| Do you have a clear picture of what a finished explanation trail looks like? | Scope readiness |
| Is there budget authority to move within the current quarter? | Procurement timing |
| Has someone been appointed to retrieve and operate decision records after handoff? | Post-delivery readiness |
If any item is a “no,” close that gap before engaging a delivery partner. unosquare‘s Discovery phase is built to close those gaps with sample explanations and draft acceptance criteria rather than another round of meetings that leave the explanation standard still soft.
Leaders often pause for bandwidth. The delivery partner owns the delivery schedule while your team provides scoped data access, appoints a single acceptance reviewer (often with counsel or compliance in the loop), and joins a limited set of structured decisions.
The prototype asks for minimal internal time, and the deliverables flow directly to procurement and legal.
If legal, compliance, or the board is asking whether you can explain the model in terms they will accept, and whether that standard is in the contract before build, start with the week-one prototype before you commit to anything. No commitment required. Get the free prototype and bring a defined, defensible explanation scope to your next procurement conversation. The questions below cover what that prototype returns and how the fixed-fee path protects the explanation standard once you move.
Frequently Asked Questions
What does the free prototype actually deliver?
In week one you get four things you can take to procurement and legal. A feasibility read on your actual data. Sample decision explanations against your constraints. A risk register focused on explanation and decision-record gaps. Draft acceptance language for what “explained” means.
Your team grants scoped data access and names one acceptance reviewer. unosquare handles the technical work. You leave with SOW-ready language before anyone signs a build.
How does a fixed-fee model protect us if the project takes longer than planned?
If the work stays inside the agreed scope, including the written explanation standard, and the build still runs long, unosquare absorbs the extra cost. That protection holds because week two locks design and price before code starts.
Timeline extensions on in-scope work do not add charges unless you approve a change order for new scope, such as a richer explanation format counsel requests after freeze.
How do we know the model will hold up to a regulatory audit?
Your contract includes an explanation standards table counsel signs: each decision type mapped to the explanation format applicable rules require, with dated legal reviews. When an auditor asks why a decision was made, counsel cites that table rather than rebuilding the story from email. For highly regulated deployments, counsel signs it before build begins.
How do we move from an AI pilot to production software?
Pilots prove a concept. Production systems in regulated contexts must also explain decisions in terms counsel and auditors will accept, with documentation, monitoring, and ownership your team can run. That is the gap between a demo score and an explainable AI (XAI) standard you can defend.
The week-one prototype closes that gap with explanation samples, a compliance map, and draft acceptance criteria most pilots never produce. Once that prototype is done, the path to an enforceable SOW is typically measured in weeks, not months.
How do you handle security and compliance for regulated industries?
unosquare builds to production-grade security standards, including security testing (penetration testing) before launch, SOC 2 compliance, and HIPAA-ready delivery where applicable, on the cloud platforms your team already runs. Practices are designed for regulated environments such as financial services and healthcare.
The explanation standards table in your contract maps each decision type to the explanation obligations that apply to your use case. For high-compliance deployments, legal counsel reviews and signs that table before the build begins.
If the explanation standard is already a board, audit, or contract requirement, unosquare turns TRACE into scoped prototype deliverables before build commitment. You get explanation acceptance criteria written into the SOW, visible regulatory exposure, and a delivery plan your team owns at handoff, including the explanation trail.
Start with a free prototype and see whether the system can be explained and accepted before production code begins.
References
- Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D. A., & Przybocki, M. (2021). Four principles of explainable artificial intelligence. NIST.
https://www.nist.gov/publications/four-principles-explainable-artificial-intelligence - National Institute of Standards and Technology. (2023). AI risks and trustworthiness. NIST AI Risk Management Framework.
https://airc.nist.gov/airmf-resources/airmf/3-sec-characteristics/ - Consumer Financial Protection Bureau. (2022). Consumer Financial Protection Circular 2022-03: Adverse action notification requirements in connection with credit decisions based on complex algorithms. Consumer Financial Protection Bureau.
https://www.consumerfinance.gov/compliance/circulars/circular-2022-03-adverse-action-notification-requirements-in-connection-with-credit-decisions-based-on-complex-algorithms/ - Federal Trade Commission. (2023). The luring test: AI and engineering of consumer trust. Federal Trade Commission.
https://www.ftc.gov/business-guidance/blog/2023/05/luring-test-ai-engineering-consumer-trust
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