10.3 Practical Templates and Checklists

Action Roadmap: Practical AI Governance Templates and Checklists for Reference

Translating macro-level AI governance principles (see Section 6.5) and complex legal compliance requirements (see Sections 6.2, 7.4, etc.) into the daily operations of law firms or corporate legal departments requires making them concrete, process-oriented, and tool-based. Developing clear internal policies, establishing standardized risk assessment processes, and providing staff with easy-to-follow operational guidelines are key to ensuring AI technology is used safely, compliantly, and responsibly.

This section aims to provide a series of practical template frameworks and example checklists, intended for reference and adaptation by legal service organizations and practitioners when building their own AI governance systems, formulating internal AI usage regulations, conducting AI application risk assessments, and standardizing daily operational behaviors.

Extremely Important Notice:

• All templates and checklists provided below are merely illustrative frameworks and absolutely should not be used as final legal documents or standard operating procedures by simply “copying and pasting.”
• Every organization must perform substantial modification, supplementation, deletion, and customization based on its own specific circumstances—including size, business type and characteristics, client base, risk appetite, specific legal and regulatory requirements of its jurisdiction, and the specific types of AI technology it plans to introduce or is currently using.
• It is strongly recommended that before finalizing and formally implementing any internal AI policy, risk assessment process, or operational guideline, it must be thoroughly reviewed and confirmed by internal or external legal counsel, information security experts, data compliance specialists, and (if possible) AI ethics experts with relevant professional knowledge.

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Template One: Internal AI Use Policy (Detailed Framework Example)

[Name of Your Law Firm / Corporate Legal Department / Judicial Body]

Regulations on the Management of Artificial Intelligence (AI) Technology Use (V1.0)

Chapter 1 General Provisions

Article 1 [Purpose and Basis] To regulate the use of Artificial Intelligence (AI) technology (especially Generative AI and Large Language Models) by personnel of this organization in their work, effectively prevent and control related risks while embracing technological innovation to enhance work efficiency and service quality, ensure all AI application activities strictly comply with national laws and regulations, industry standards, professional ethics, and the rules and regulations of this organization, and safeguard the core interests (especially information security and confidentiality) of clients and this organization, these regulations are hereby formulated.

Article 2 [Core Principles] Personnel of this organization must strictly adhere to the following core principles when using AI technology to assist their work:

(1) Client Interests First & Diligence: Any application of AI must fundamentally serve the purpose of maintaining and promoting the legitimate rights and interests of clients (or service recipients) and must not harm their interests. Professional prudence and diligence must be maintained.

(2) Absolute Confidentiality Priority: Protecting confidential information of clients (or cases), state secrets, trade secrets, and personal privacy is an insurmountable red line. Any AI usage method that could directly or indirectly lead to information leakage, improper disclosure, or breach of confidentiality duties is strictly prohibited.

(3) Human Judgment Leads & Final Responsibility: AI tools can only serve as auxiliary means and cannot replace the independent thinking, professional judgment, experience application, and final decision-making of legal professionals. Humans must bear final review responsibility and legal/professional liability for the work product.

(4) Security & Compliance Baseline: All AI application activities must strictly comply with all applicable laws, regulations, and regulatory requirements, such as the Cybersecurity Law, Data Security Law, Personal Information Protection Law (PIPL), Interim Measures for the Management of Generative Artificial Intelligence Services (if applicable in jurisdiction), Lawyers Law, relevant judicial interpretations, departmental rules, and these regulations.

(5) Prudence, Responsibility & Risk Awareness: When using AI, maintain a clear mind and critical thinking, fully recognize the limitations and potential risks of the technology (e.g., “hallucinations,” bias, outdated information), prudently evaluate output results, and be responsible for one’s own usage behavior and its consequences.

(6) Continuous Learning & Adaptation: Encourage and support personnel to actively learn AI-related knowledge, master necessary skills, understand technology trends, to adapt to future changes in legal service models.

Article 3 [Scope of Application] These regulations apply to all personnel of this organization, including but not limited to partners, lawyers, prosecutors, judges, assistants, administrative staff, technical support personnel, interns, as well as external consultants, contractors, service providers, and other third-party personnel authorized to handle work matters on behalf of the organization and potentially using AI technology (hereinafter collectively referred to as “Relevant Personnel”).

These regulations cover all actions by Relevant Personnel using any form or source (whether procured centrally by the organization, developed internally, or personally chosen for use—if the latter is permitted for work) of AI tools, platforms, or services for the purpose of fulfilling job duties, handling organizational business, providing legal services, or representing the organization.

Article 4 [Key Term Definitions]

(This article clarifies the meaning of core concepts in the policy to avoid ambiguity)

(1) Artificial Intelligence (AI): Refers to… (Can reference national standards or common definitions).

(2) Generative AI (GenAI): Refers to AI technology capable of generating content such as text, images, audio, video, e.g., Large Language Models (LLMs).

(3) Large Language Model (LLM): Refers to… (e.g., GPT series, ERNIE Bot, Qwen, etc.).

(4) Client (or Case) Confidential Information: Refers to all information for which this organization has a duty of confidentiality according to law, contract, or professional ethics, including but not limited to… (List details, e.g., client identity, case details, evidence, communications, work product, trade secrets, non-public information).

(5) Personal Information / Sensitive Personal Information: As defined by the Personal Information Protection Law (PIPL) or equivalent local legislation (e.g., GDPR, CCPA).

(6) AI Tool/Platform: Refers to any application software, online service, API interface, or system utilizing AI technology to provide specific functions.

(7) Approved List: Refers to the “List of Approved AI Tools and Services” maintained and published by the designated department of this organization.

(8) High-Risk Application Scenario: Refers to… (e.g., providing direct advice to clients, processing large amounts of sensitive personal information, use in critical analysis potentially impacting case outcomes).

Chapter 2 Management of AI Tool Usage

Article 5 [Approved Use Cases and Tools]

(1) Relevant Personnel may only use AI tools listed in the organization’s “List of Approved AI Tools and Services” (see Appendix A, dynamically maintained and updated by [Designated Dept, e.g., IT Dept/Risk & Compliance Dept/AI Governance Committee]), and must strictly follow the permitted use cases, data type restrictions, and operational requirements specified in the list when assisting with work.

(2) (Example) Currently preliminarily approved scenarios and tools (specific content needs determination after internal assessment): 1. Legal Research (Public Information): Use [Approved AI-enhanced Legal Database B] for searching, summarizing, and analyzing publicly available laws, regulations, judicial precedents. Restriction: Only input non-confidential, non-client-specific general legal questions or keywords. 2. Internal Meeting Audio Transcription: Use [Approved Speech Transcription Tool C, confirmed data stays within jurisdiction/secure environment, e.g., a private deployment solution] to transcribe internal training sessions or non-confidential meeting recordings confirmed not to contain any client (or case) confidential information or sensitive personal data. Restriction: Strictly forbidden for transcribing any client interviews, court recordings, confidential meetings, etc. 3. Internal Admin Document Drafting Assistance: Use [Approved Enterprise LLM Platform D, with strict data protection agreement signed] to assist in drafting internal notices, meeting minute drafts, training material drafts, etc., that do not involve any client or case information. Restriction: Generated content must undergo substantial manual revision and review. 4. Non-Sensitive Information Translation Assistance: Use [Approved Enterprise Translation Tool E] for preliminary translation of public documents, news reports, etc., that do not involve any confidential information. Restriction: Translation is for internal reference only; must be manually proofread before external release or use as work product. (Please list allowed tools, scenarios, data limits, operational requirements in detail based on organizational decisions, emphasizing the list is dynamic)

Article 6 [Strictly Prohibited Actions (Red Lines)]

Under no circumstances are Relevant Personnel permitted to engage in the following actions:

(1) Absolute Prohibition on Inputting Confidential Information! Absolutely Forbidden! In any AI tool or platform not formally approved by this organization after rigorous security assessment (especially publicly available, free, or personal-account-based AI services like personal ChatGPT, web-based DeepSeek, public online translation sites, untrusted mobile apps, etc.), input, upload, or otherwise provide any form of information that can directly or indirectly identify clients, reveal case details, non-public transaction info, trade secrets, technical secrets, legally privileged communications, internal non-public work product, or any other information subject to confidentiality duties under law, contract, or professional ethics!

(2) Prohibit using AI to generate, disseminate, or assist in generating/disseminating any false information, misleading statements, illegal content, discriminatory remarks, defamatory text, hate speech, privacy-invasive content, or any content violating laws, core socialist values (if applicable), public order, or good morals.

(3) Prohibit using AI for any automated decision-making that could substantially affect the rights or interests of clients, employees, or third parties, or impact case outcomes (e.g., auto-rejecting client requests, auto-assessing evidence admissibility, auto-suggesting sentences) without explicit authorization from this organization and completion of a comprehensive risk assessment.

(4) Prohibit intentionally attempting to circumvent or “jailbreak” security controls set by this organization or technical safety guardrails and content restrictions implemented by the AI platform provider itself.

(5) Prohibit directly using any AI-generated content as final work product or formal opinion submitted to clients, courts, arbitral tribunals, regulators, government agencies, or any other third party, without undergoing independent, rigorous, substantive review, verification, modification, and final confirmation by a human professional.

(6) Prohibit using AI technology to intentionally or negligently create, copy, publish, or disseminate content that infringes upon any third party’s (including clients, opposing parties, this organization itself) intellectual property rights (copyright, trademark, patent, trade secret).

(7) Prohibit using AI tools for any personal purposes unrelated to job duties, or activities that could bring unnecessary risks or resource waste to the organization.

(8) (Please list other specific behaviors that must be clearly prohibited based on the organization’s core risks and management needs, e.g., making external claims about AI capabilities, representing the organization in committing to AI results)

Article 7 [Approval and Risk Assessment for Introducing AI Tools/Applications]

(1) Any department or individual planning to introduce a new AI tool, platform, or service (external procurement, joint development, or internal R&D), or planning to apply an already approved AI tool to a new business scenario, process a higher level of sensitive data, or target a new user group, must first submit a written application to the [Designated Approval Body, e.g., AI Governance Committee / Risk & Compliance Dept / Cybersecurity & IT Leading Group Office] following the specified procedure.

(2) The applicant must fully cooperate to complete an AI Application Risk Assessment organized by [Designated Dept] (see Appendix B for process and checklist template). The assessment must cover multiple dimensions including data security, privacy, accuracy, reliability, bias, fairness, legal compliance, ethics, operational, and business risks.

(3) Only after the risk assessment is completed, effective mitigation measures have been designed and confirmed for all identified medium or high risks, the overall risk is assessed as acceptable, and formal written approval is obtained from the [Designated Approval Body], can the new AI tool or application be officially introduced or deployed.

(4) [Designated Dept] is responsible for establishing and maintaining the “List of Approved AI Tools and Services”, reviewing and updating it periodically (at least semi-annually) based on technological developments, risk assessment results, and business needs, and communicating updates to all relevant personnel appropriately.

Chapter 3 Data Security and Confidentiality Requirements

Article 8 [Core Requirements for Data Security & Client Confidentiality]

(This chapter is core to the policy and needs extremely detailed and specific provisions based on the organization’s actual situation and national standards (e.g., PIPL, GDPR, CCPA, cybersecurity grade protection requirements). Below are example points; specifics need elaboration by professionals.)

(1) Data Classification: Must strictly classify data intended for AI processing (e.g., public info, internal general, sensitive business, client confidential, personal info, sensitive personal info), and apply corresponding security protection and usage restriction measures based on level.

(2) Principle of Least Privilege/Data Minimization: Strictly follow the principle of minimum necessity when providing any information to AI tools, only providing the least amount necessary for the specific auxiliary task. Use anonymized or pseudonymized data whenever possible.

(3) Access Control: Access permissions to AI tools (especially those handling sensitive data) must be strictly controlled, granted only to personnel with a clear work need who have received adequate training. Strong identity authentication (e.g., MFA) is required.

(4) Encryption Requirements: All data containing confidential information or personal data must be protected using strong encryption technologies compliant with national or industry standards during network transmission (including uploads to cloud platforms) and when stored at rest (locally or in the cloud). Key management must be secure.

(5) Secure Storage & Timely Deletion: Sensitive data processed by AI tools should be promptly and securely deleted from the AI platform or local temporary storage according to established rules (e.g., project completion, retention period expiry), avoiding unnecessary long-term storage. Ensure deletion is thorough.

(6) Third-Party Vendor Security Management: For third-party AI tools/services, conduct strict security due diligence (see risk assessment checklist), and sign legally binding DPAs or NDAs containing adequate data protection clauses, security responsibilities, audit rights, and liability provisions. Periodically assess their security practices.

(7) Cross-Border Data Transfer Restrictions: Strictly prohibit transferring any information containing personal data or important data outside the [relevant jurisdiction, e.g., mainland China] via AI tools without undergoing strict compliance assessment and obtaining necessary approvals (e.g., security assessment by authorities, separate consent from data subjects).

(8) Special Requirements for Privileged or Specially Protected Information: For information potentially subject to attorney-client privilege, state secrets, or other special legal protections, avoid using external AI tools in principle. If absolutely necessary, must undergo highest-level risk assessment and approval, employing strictest technical and managerial controls.

Chapter 4 Supervision, Responsibility & Training

Article 9 [Human Oversight, Review & Final Responsibility]

(1) Reiterate Core Principle: Any AI tool, regardless of its claimed capabilities, can always only serve as an auxiliary means. Its output results (research leads, data analysis, risk alerts, document drafts, summaries, or any other form) can never automatically become the final work product or decision basis.

(22) User Review Duty: Relevant Personnel using AI tools must conduct rigorous, professional-standard, responsible review, verification, and necessary revision of all AI-generated outputs before formally adopting or using them. Review must cover accuracy, completeness, logical coherence, legal compliance, ethical appropriateness, and alignment with organizational quality standards.

(3) Supervisory Responsibility: Supervising attorneys, team leaders, department managers bear final review and quality assurance responsibility for work products completed by their subordinates with AI assistance. Must ensure subordinates fully understand and comply with these regulations and effectively supervise their work.

(4) Ultimate Responsibility Attribution: Regardless of the extent of AI involvement, the full legal, professional, and ethical responsibility for the final work product or decisions made based on AI assistance rests solely with the human professional (and their organization) who signs off, approves, or ultimately uses that product/decision.

Article 10 [Intellectual Property Considerations]

(1) Input Risk Reminder: Remind personnel that inputting third-party copyrighted materials (articles, reports) into AI tools (esp. LLMs) might pose infringement risks; ensure legality of input.

(2) Output Ownership & Use: Clearly state that any potentially original content generated by personnel using organizational resources (including approved AI tools) in the course of performing job duties, its intellectual property (unless otherwise agreed) belongs to [Organization Name]. Employees only have reasonable usage rights based on work needs.

(3) Prohibition of Infringing Generation: Strictly prohibit using AI tools to intentionally or negligently generate content that clearly plagiarizes, copies, or otherwise infringes upon any third party’s (client, competitor, own org) intellectual property rights (copyright, trademark, patent, trade secret).

(4) Commercial Use License (External Tools): When using third-party AI tools, pay attention to terms of service regarding commercial use licenses for generated content, ensuring the organization has sufficient rights for providing services to clients.

Article 11 [Transparency Requirements & Internal/External Communication]

(1) Duty to Inform Clients: Specify under what circumstances (e.g., when AI analysis significantly informs advice/reports, or AI use notably affects service method/billing), and how, to appropriately and clearly inform clients about the use of AI, its role, and limitations, ensuring their understanding (obtain written consent if necessary, see Template Three).

(2) Disclosure to Court/Tribunal/Regulators: In litigation, arbitration, or regulatory interactions, if submitted evidence, analysis reports, or legal opinions substantively rely on AI analysis or generation, follow relevant legal rules, court orders, or best practices for necessary and honest disclosure.

(3) Internal Communication & Knowledge Sharing: Encourage personnel to openly and constructively communicate and share within the organization regarding successful AI usage experiences, practical techniques, encountered risks, effective prompt templates, etc. (via internal training, knowledge base, case discussions), to promote overall application level and risk awareness.

Article 12 [Mandatory Training Requirements]

(1) All Relevant Personnel within the scope must attend and pass foundational training organized by the organization (or third party) covering these regulations, AI ethics, data security requirements, risk identification/prevention, proper use of approved tools, and updates on relevant laws.

(2) The organization will periodically conduct update training based on technology and regulatory developments, which personnel are obligated to attend.

(3) Training completion will be recorded and may be considered in employee performance reviews, compliance assessments, or project eligibility.

Chapter 5 Violation Handling & Miscellaneous

Article 13 [Handling of Violations]

(1) Any violation of these regulations, especially breaches of Article 6 [Strictly Prohibited Actions] and Article 8 [Core Data Security & Confidentiality Requirements], will be considered a serious violation.

(2) Upon confirmation, the organization will, based on the nature, severity, consequences of the violation, and the individual’s fault, impose corresponding internal disciplinary actions according to internal rules, ranging from warning, public criticism, deduction of bonuses, suspension/revocation of AI tool access, demotion, up to termination of employment or engagement.

(3) If the violation causes losses to the organization or clients, the organization reserves the right to pursue liability for damages against the individual.

(4) If the violation also contravenes national laws and constitutes a crime, the organization will report to judicial authorities according to law.

(5) The organization establishes [Designated Reporting Channel, e.g., compliance email/hotline] to encourage employees to report observed violations, promising confidentiality for whistleblowers (unless otherwise required by law).

Article 14 [Policy Management & Effectiveness]

(1) The final interpretation right of these regulations rests with [Designated Department, e.g., AI Governance Committee / Risk & Compliance Dept / Management Committee].

(2) [Designated Dept] is responsible for periodically reviewing (at least annually, or upon significant changes in law/technology) these regulations and revising/updating them as needed.

(3) Any revisions must be approved following internal management procedures and promptly communicated to all relevant personnel.

(4) These regulations take effect from [YYYY-MM-DD]. Any prior internal documents conflicting with these regulations shall be superseded by these regulations.

Appendices:

• Appendix A: List of Approved AI Tools and Services (Dynamically maintained by [Designated Dept])
• Appendix B: AI Application Risk Assessment Process and Checklist Template
• Appendix C: Information Table of Relevant Responsible Departments, Contacts & Reporting Channels

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Template Two: AI Application Risk Assessment Checklist (Detailed Guidance Example)

(This checklist provides a comprehensive framework; organizations must tailor, refine, and supplement it based on their own situation and the specifics of the AI application. The assessment process should be documented and archived.)

Project/Tool Basic Information

• Project/Tool Name: [e.g., Introduction of “XX Brand Intelligent Contract Preliminary Risk Screening System V2.0” SaaS Service]
• Vendor Name: [Full Legal Name]
• Requesting Department: [Dept Name] Lead: [Name]
• Assessment Start Date: [YYYY-MM-DD]
• Assessment Participants & Departments: [Must include representatives from Legal, Compliance, Risk Management, IT, Cybersecurity, Data Protection, and core Business Units involved]

Part I: Application Scenario & Objective Definition

1. Detailed Description of AI Use Case:
   • Describe the specific work task or business problem the AI tool is intended to solve. (e.g., Perform first-round automated risk scanning on all [Type, e.g., software procurement] contracts under [Amount, e.g., $100,000] handled by this department, identifying missing key clauses (IP, confidentiality, liability limits), major deviations from our standard template, and presence of predefined “high-risk” terms.)
   • What are the core functions of this AI? (e.g., NLP, text comparison, rule matching, risk scoring.)
2. Expected Objectives & Value Measurement:
   • What specific goals are expected? (e.g., Reduce initial review time for these contracts from avg X hours to Y hours; Increase detection rate of high-risk contracts from Z% to W%; Reduce junior legal staff time spent on this task by XX%.)
   • How will goal achievement be measured? (Set quantifiable KPIs or qualitative criteria).
   • Any other expected value besides efficiency? (e.g., Improve consistency of risk identification; Standardize review process; Enhance compliance level; Improve employee work experience.)
3. Data Types & Sources Involved:
   • What types of data will the AI tool directly process or access? (e.g., Contract text (Word, PDF), potentially containing party names, addresses, contacts, transaction amounts, subject matter details, technical specs, commercial terms.)
   • What are the data sources? (e.g., Submitted by business units via internal contract management system.)
   • Does the data contain Personal Information? Sensitive Personal Information? (Judge based on PIPL/GDPR/CCPA definitions and detail).
   • Does it contain trade secrets of clients or third parties? Our organization’s trade secrets?
   • Does it contain information subject to special legal protections (state secrets, financial regulation, healthcare)?
4. Primary User Group & Interaction Method:
   • Who are the primary users? (e.g., Contract review team in Legal Dept (mainly junior staff with 1-3 yrs experience).)
   • How will users interact with the tool? (e.g., Uploading contract files via web interface, viewing flagged results and reports; Or via API integration with existing contract system.)
5. Output Results & Their Usage:
   • What form will the output take? (e.g., A contract text PDF with potential risk clauses highlighted; A structured risk summary report (with risk description, clause excerpt, preliminary risk level suggestion, rule/model basis).)
   • How will these outputs be used? By whom? (e.g., Used by mid-level legal staff as starting point and reference for further detailed manual review, helping identify clauses needing focus, assisting in drafting review comments.)
   • Key Point: Will the output be directly used for any automated decision-making? (Answer should be “No” in this example, only aids human decision).

Part II: Data Security & Privacy Risk Assessment

(This part needs collaboration with InfoSec and Data Protection professionals)

6. Data Residency & Cross-Border Transfer:
   • Is data processing done locally (on-premise) or in the cloud?
   • If cloud, in which country/region are the servers physically located? Does it involve cross-border data transfer?
   • If cross-border transfer is involved, have requirements of relevant laws (e.g., China’s Measures for Security Assessment, Standard Contract Measures; GDPR adequacy/SCCs) been met? (Need compliance proof/plan).
7. Vendor’s Data Processing Policies & Commitments:
   • Have the vendor’s Terms of Service, Privacy Policy, and Data Processing Agreement (DPA) been carefully reviewed and understood?
   • Does the vendor explicitly commit (preferably in a legally binding DPA) never to use customer (our organization’s) input data for training their general AI models or any purpose beyond providing the agreed service?
   • Is a clear, easy Opt-out mechanism provided (if vendor defaults to potential data use)?
   • Are the clauses in the DPA regarding data ownership, confidentiality, security liability, breach responsibility, audit rights clear, sufficient, and meet our requirements?
8. Technical Security Measures:
   • Is data in transit (e.g., upload from browser to cloud) protected end-to-end with strong encryption (TLS 1.2+)?
   • Is data encrypted at rest in the cloud storage? What algorithms and key management are used?
   • Does the platform implement strict identity authentication and access controls? Support MFA? Is permission management fine-grained enough?
   • Does the platform have comprehensive security audit logging capabilities?
   • Does the vendor conduct regular security vulnerability scanning and penetration testing? Is their infrastructure sufficiently resilient against attacks?
   • Does the vendor hold authoritative third-party security & privacy compliance certifications (ISO 27001, SOC 2 Type II, CSA STAR, etc.)? Verify certificate validity.
9. Specific Personal Information Protection Requirements (if applicable):
   • If processing personal information, is the legal basis ensured (consent, contract necessity, etc.)?
   • Is the principle of minimization followed? Are de-identification/anonymization measures taken?
   • Are data subjects informed and their rights (access, correction, deletion, etc.) guaranteed?
10. Security Risk Identification & Mitigation:
    • Main Risk Points: [List identified risks. E.g., Risk 1: Cloud server outside jurisdiction poses data export compliance risk. Risk 2: Vendor DPA commitment on not using data for training is ambiguous. Risk 3: Users might inadvertently upload attachments with sensitive PI.]
    • Planned Mitigation Measures: [List specific, actionable measures for each risk. E.g., Measure 1: Require vendor to provide local service or sign standard contract; prohibit processing PI data until approved. Measure 2: Negotiate DPA revision or seek higher security isolation environment from vendor. Measure 3: Implement sensitive info detection/warning at upload; enhance user training on pre-upload anonymization.]
    • Residual Risk Assessment: [After mitigation, is the residual risk level High/Medium/Low? Is it acceptable?] (If unacceptable high risk remains, reject or demand further remediation)

Part III: Accuracy, Reliability & Hallucination Risk Assessment

11. Core Function Performance Validation (Based on Internal Testing):
    • Has the AI tool been sufficiently tested using our organization’s real, representative, anonymized contract samples? (Specify test sample size, types, coverage).
    • Test results show, regarding identifying specific risk clauses/elements of interest to us:
      • What is the Precision? (% of AI-flagged risks that are true risks)
      • What is the Recall? (% of all true risks successfully flagged by AI)
      • What is the False Positive Rate? (% of safe clauses wrongly flagged as risky)
      • What is the False Negative Rate? (% of true risks missed by AI)
      • (Provide specific test methodology, data, results)
    • Is there significant performance variation across different contract types or clause complexities?
12. “Hallucination” & Incorrect Output Risk:
    • During testing, were instances observed where the tool generated completely wrong or fabricated risk alerts (“hallucinations”)? How frequent and in what typical scenarios?
    • Is it possible for it to misinterpret or misclassify the nature of clauses?
    • Are the suggested risk level ratings reasonable and consistent?
13. Ability to Handle Complex & Non-Standard Situations:
    • How well does the tool handle clauses with unconventional wording, complex structures, or involving special industry/commercial arrangements? Is it prone to errors, failure, or meaningless alerts?
    • Can it effectively process contracts containing tables, handwritten notes, or messy formatting?
14. Understandability & Explainability of Output:
    • Are the AI-flagged risks and generated reports clear and easy for human reviewers to understand?
    • Does it provide sufficient context or basis to explain why a clause was flagged as risky? (e.g., which internal rule violated? difference from which standard clause? matched which risk keyword?)
    • If explainability is poor, does it increase the difficulty and time needed for manual review?
15. Accuracy Risk Identification & Mitigation:
    • Main Risk Points: [e.g., Risk 1: High false negative rate for certain non-standard clauses. Risk 2: Suggested risk levels sometimes inaccurate. Risk 3: Lack of clear explanation for flags increases review burden.]
    • Planned Mitigation Measures: [e.g., Measure 1: Workflow mandates lawyers review not just AI flags but also must review key sections AI didn’t flag (liability limits, IP, dispute resolution); periodically “stress test” AI with difficult cases & provide vendor feedback. Measure 2: Treat AI risk level as preliminary reference only, final rating by human; train users specifically on AI limits & importance of human judgment. Measure 3: Request vendor improve report explainability; build internal knowledge base documenting common false positives/negatives.]
    • Residual Risk Assessment: [After mitigation, is residual risk for accuracy/reliability High/Medium/Low? Is it acceptable?]

Part IV: Bias & Fairness Risk Assessment

16. Potential Training Data Bias:
    • Has the vendor provided any information about the source, composition, and potential biases of their model’s training data? (Usually limited, but try to understand).
    • Is there reason to believe the training data might be overly concentrated on certain industries, regions, contract types, or company sizes, potentially leading to poor performance or systemic bias when handling other types of contracts?
17. Potential Algorithmic Design Bias:
    • Could the design of the risk assessment rules or ML models unintentionally discriminate against or disfavor certain legitimate but non-mainstream or non-standard commercial arrangements, contract clauses, or counterparty types? (e.g., overly relying on certain keywords potentially correlated with certain groups as risk indicators?)
18. Bias Risk Identification & Mitigation:
    • Main Risk Points: [e.g., Risk 1: Training data might lack coverage of non-standard contracts common for SMEs, leading to inaccurate risk assessment for them. Risk 2: Risk rules might be overly “sensitive” to clauses reflecting innovative business models.]
    • Planned Mitigation Measures: [e.g., Measure 1: During internal testing/pilot, consciously include diverse contract types/sources for evaluation, monitor performance differences; explicitly remind reviewers in guidelines to be alert to potential AI bias and use independent judgment. Measure 2: Periodically review and update risk rule library, removing potentially biased rules; provide feedback to vendor on observed bias issues, urge model improvement.]
    • Residual Risk Assessment: [After mitigation, is residual risk for bias/fairness High/Medium/Low? Is it acceptable?]

Part V: Legal, Compliance & Ethical Risk Assessment

19. Compliance with Professional Ethics:
    • Could using this AI tool impair our lawyers’ ability to fulfill their duties of confidentiality (see Part II), competence (e.g., over-reliance leading to skill degradation), diligence (e.g., AI error causes failure to spot key risk)? Assess comprehensively considering mitigations.
    • Could it pose risks of Unauthorized Practice of Law? (Low risk for this internal use case, but high concern for client-facing tools).
20. Intellectual Property Risks:
    • Could the tool’s output (e.g., recommended clause wording, report structure) directly or indirectly infringe third-party copyrights? (Does vendor offer indemnity or warranty?)
    • Does inputting our organization’s contract templates or clause library into the AI (if needed for customization/training) infringe our own IP or violate client agreements?
21. Compliance with AI-Specific Regulations:
    • Do the application scenario, technology principle, data handling practices need to comply with specific AI regulations like China’s Deep Synthesis Provisions (if involves substantive content modification/generation) or Generative AI Measures (if involves providing generated content services externally)? (Likely not directly triggering filing etc. for internal assistance, but monitor regulatory dynamics).
    • If business involves EU or US, do EU AI Act or relevant US state law requirements need consideration?
22. Impact on Legal Privilege:
    • Does the contract or related material being processed potentially contain communications or information protected by attorney-client privilege or other legal privileges?
    • Does using a third-party cloud AI tool increase the risk of privilege being deemed waived? (Analyze based on specific jurisdiction rules/cases; risk generally lower for contract text itself, but higher for attachments containing attorney notes/communications).
23. Legal Compliance & Ethical Risk Identification & Mitigation:
    • Main Risk Points: [e.g., Risk 1: Users might over-trust AI results, failing duty of independent review (diligence violation). Risk 2: Vendor’s commitment regarding IP liability for output is vague.]
    • Planned Mitigation Measures: [e.g., Measure 1: Repeatedly emphasize final human responsibility in policy/training; include AI usage & review records in work quality spot checks. Measure 2: Exercise independent judgment when using AI-suggested clauses, assume responsibility; prioritize internal standard clauses; negotiate clear IP liability terms with vendor.]
    • Residual Risk Assessment: [After mitigation, is residual risk for legal/compliance/ethics High/Medium/Low? Is it acceptable?]

Part VI: Operational, Business & Organizational Risk Assessment

24. Usability & User Acceptance:
    • Does the tool’s interface and logic fit the working habits of target users (junior legal staff)? Is it easy to learn?
    • How much time and resources are estimated for user training? Are users generally willing to learn and use this new tool? Is there resistance?
25. Integration with Existing Workflows & Systems:
    • Can this tool be smoothly and efficiently integrated with our existing contract management system, document management system (DMS), or approval workflows?
    • What is the integration method? (API? Plugin? Manual import/export?) What are the integration costs and development timeline?
    • Will integration disrupt or complicate existing workflows? Require users to switch frequently between systems?
26. Risk of Over-reliance & Skill Degradation:
    • Is there a risk that users (esp. junior staff) will over-rely on AI’s preliminary screening results, neglecting to develop their own independent, in-depth contract review and risk judgment skills?
    • Could this lead to degradation of core professional skills in the team long-term?
27. Vendor Reliability & Business Continuity:
    • Is the vendor financially stable, reputable, with long-term service capability? (Assess carefully, especially for startups).
    • Is the vendor’s service stable and reliable? Does their SLA commitment meet business needs? Is technical support timely and professional?
    • Is there a risk of the vendor being acquired, discontinuing service, or significantly raising prices? If so, are there contingency plans and data migration strategies? Is there significant vendor lock-in risk?
28. Cost-Benefit Re-evaluation:
    • Considering the full estimated costs of the tool (software fees, implementation, integration, training, maintenance, potential hardware upgrades) versus its more realistic expected benefits (quantified efficiency gains, risk reduction effects) after testing and assessment, is the Return on Investment (ROI) still attractive? Does it align with organizational budget and strategic priorities?
29. Operational/Business/Organizational Risk Identification & Mitigation:
    • Main Risk Points: [e.g., Risk 1: Integration with existing contract system requires heavy custom development, high cost/long timeline. Risk 2: Junior users might over-rely on AI, hindering skill development. Risk 3: Vendor is a startup, long-term stability uncertain.]
    • Planned Mitigation Measures: [e.g., Measure 1: Pilot with standalone use & manual uploads first, assess deep integration later based on value validation; or choose alternative with better integration capabilities. Measure 2: Make contract review skill development a core KPI for junior staff; organize regular case sharing & complex contract reading sessions emphasizing AI as aid only; implement work product spot checks. Measure 3: Negotiate more flexible contract terms; develop backup plan & data export strategy; focus assessment on their core tech & team strength.]
    • Residual Risk Assessment: [After mitigation, is residual risk for operational/business/organizational aspects High/Medium/Low? Is it acceptable?]

Part VII: Overall Risk Assessment Conclusion & Decision Recommendation

30. Overall Risk Rating:
    • Synthesizing assessment results across all dimensions (esp. residual risks) and considering weights of risk factors, the overall risk level for this AI application is assessed as: [High / Medium / Low].
    • (Briefly explain the core rationale for this overall rating)
31. Decision Recommendation:
    • Based on the overall risk assessment and expected business value, provide a clear recommendation:
      • Approve Introduction/Application: Residual risks are within acceptable limits, and expected benefits are significant. Recommend proceeding as planned.
      • Approve with Conditions: Recommend approval, but only after the following critical mitigation measures are completed and verified or preconditions are met: [Clearly list mandatory actions].
      • Defer Decision / Further Assessment Needed: Significant risks or uncertainties remain, preventing a final decision. Recommend conducting [specify additional testing, information gathering, or risk assessment activities] before revisiting.
      • Reject / Do Not Approve: Identified risks are too high and cannot be effectively mitigated, or cost-benefit is clearly unfavorable. Recommend not introducing/applying this AI tool.
32. Subsequent Monitoring Requirements:
    • If decision is Approve or Conditional Approve, list the key risk points, core performance metrics, or user feedback areas that need continuous monitoring during tool deployment and usage. Specify responsible department/personnel and monitoring frequency.

Approval Signatures Section

• Assessment Team Recommendation: [Briefly state team’s main conclusion and rationale]
  • Assessment Team Lead Signature: _______________ Date: _______________
• [Designated Approval Body/Committee] Decision:
  • Concur with Assessment Team Recommendation
  • Do Not Concur, Reason: _________________________
  • Final Decision: [Approve / Approve with Conditions / Defer / Reject]
  • (If conditional approval, specify conditions and deadlines)
  • Approver / Committee Chair Signature: _______________ Date: _______________

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Template Three: Client Informed Consent Form Regarding AI Use (Example Elements - Lawyer Customization Required)

(Important Notice: This template only illustrates core elements and communication ideas. It must NOT be used directly as a final legal document. It must be specifically and meticulously drafted, reviewed, and customized by a qualified lawyer according to the latest legal requirements of the relevant jurisdiction (e.g., China, US, EU), the specific services provided, potential risks, and the client’s particular circumstances! Language must be clear, accurate, unambiguous, fully protecting the client’s right to know and choose.)

Notice and Consent Form Regarding the Use of Artificial Intelligence (AI) Assisted Technology in Legal Services Provided to You

To: [Full Name of Client / Client Entity Name]

Thank you for engaging [Name of Your Law Firm / Corporate Legal Department] (“the Firm,” “we,” “us”) to provide legal services regarding [Briefly describe the matter, e.g., “Your contract dispute with Company XX regarding contract Y”] (“this Matter”).

To enhance service efficiency, process complex information, and strive for the best possible outcome for you, while adhering to the highest professional standards and confidentiality requirements, we may prudently use certain internally assessed, approved, secure, and compliant artificial intelligence (AI) assisted technology tools (“AI Tools”) in the course of providing legal services for this Matter.

I. How Might AI Tools Assist Our Work?

We want you to understand that AI Tools play an auxiliary role in this Matter. Their purpose is to help our professional legal team (lawyers, assistants, etc.) perform certain specific, often information-intensive or repetitive tasks more efficiently. We may utilize AI Tools to assist in one or more of the following areas (specific use and tool selection will be based on actual needs and professional judgment):

• Legal Research & Information Retrieval: Using AI to more quickly and accurately find relevant public laws, regulations, judicial precedents, administrative rules, or academic literature related to your case, as a basis for our legal analysis.
• Document Review & Preliminary Analysis: For large volumes of contracts, agreements, evidentiary documents, or other materials you provide, we might use AI tools for initial automated processing, such as:
  • Content Summarization: Quickly generating summaries of long documents to help us grasp key points faster.
  • Information Extraction: Automatically extracting key information like party names, key dates, amounts, specific clauses (e.g., dispute resolution) from contracts for organization and verification.
  • Preliminary Risk Flagging: Comparing documents against standard templates or risk rule libraries to preliminarily flag potential risk points or anomalies requiring our focused attention (Note: This is by no means a final risk judgment).
• Standardized Document Drafting Assistance: For relatively standard documents (e.g., simple notice letters, parts of procedural applications), AI tools might be used to generate initial drafts, but all drafts will undergo substantial revision, review, and finalization by our lawyers.
• Meeting Recording Transcription or File Translation Assistance: If relevant meeting recordings or foreign language documents need processing, AI tools might be used to quickly convert them into text transcripts or draft translations. All such drafts must be rigorously proofread and confirmed by our professional staff.
• (Please describe potential uses more specifically and accurately based on your institution’s actual planned usage, avoiding overly broad or misleading statements)

II. Our Core Commitments & Safeguards to You

We deeply understand the trust you place in us and the high sensitivity of your information. Therefore, when using any AI tool to process information related to this Matter, we will always prioritize protecting your core interests and information security, and strictly adhere to the following core commitments:

• (1) Human Professional Judgment Leads, Final Responsibility Ours:

  • We solemnly promise: AI tools are always only auxiliary means; they will never replace the independent thinking, professional judgment, experience application, and final decision-making of our human lawyers.
  • All final legal opinions, litigation/arbitration strategy recommendations, risk assessment conclusions, and legal documents or work products submitted on your behalf to any third party will be entirely reviewed, analyzed, revised, and finally confirmed comprehensively, prudently, and responsibly by our qualified and experienced professional legal team.
  • We will bear full legal and professional responsibility for the final service outcomes and legal opinions we provide.

• (2) Upholding Highest Standard of Client Confidentiality:

  • Protecting all your confidential client information (including but not limited to your identity, case details, trade secrets, personal privacy, our communications, and our work product) is our highest code of conduct and non-negotiable legal duty.
  • When using AI tools, we will take extremely strict technical and managerial measures to ensure the security and confidentiality of your information, including but not limited to:
    • Prioritizing and using enterprise-grade AI solutions offering the highest level of data security guarantees, promising data isolation, and contractually committing never to use client data for model training, or deploying tools within our controllable, secure internal environment.
    • Using effective, necessary anonymization or redaction techniques to the greatest extent possible before inputting any potentially sensitive information into AI tools, to minimize exposure risk.
    • Ensuring all AI tools and their vendors we use have passed our rigorous security and compliance assessments and are bound by strict confidentiality agreements.
    • Strictly complying with all provisions regarding client confidentiality in the [Applicable Law, e.g., Lawyers Law of the PRC, PIPL, GDPR] and professional ethics rules.

• (3) Ensuring Work Quality, Maintaining Professional Standards:

  • We will subject any intermediate results generated with AI assistance (research leads, analysis reports, draft documents) to rigorous review, verification, and necessary correction according to professional legal standards.
  • We ensure the goal of using AI assistance is to enhance service quality and efficiency, and we will never lower our due professional standards or service quality because of AI use.

III. Understanding Technical Limitations & Potential Risks

In the spirit of honesty and transparency, we also wish for you to understand that current AI technology (especially generative AI like LLMs we might use), while powerful, is still rapidly evolving and has inherent technical limitations and potential risks that cannot be entirely eliminated, such as:

• Potential for Inaccurate or Fabricated Information: AI can sometimes confidently generate factually incorrect information (“hallucinations”).
• Potential for Bias: AI analysis and outputs might be influenced by societal biases present in its training data (e.g., stereotypes based on region, industry).
• Lack of True Understanding: AI works based on pattern matching and statistics; it lacks human common sense, emotion, values, and deep understanding of complex business contexts, legal principles, or social situations.
• Potentially Outdated Information: AI’s knowledge base might not be real-time; legal information could be outdated (we strive to mitigate this with real-time databases).

We will use rigorous human review processes, cross-validation mechanisms, and the professional judgment of our legal team to identify, control, and correct these potential risks and errors to the maximum extent possible. However, we hope you understand that no technology or process can guarantee 100% perfection.

IV. Your Consent and Related Rights

Our work always relies on full communication and mutual trust with you. Therefore, we seek your input:

• Consent: Please carefully read and understand the above explanations regarding our potential use of AI-assisted technology, our core commitments and safeguards, and the related technical limitations. If you agree to allow us, while providing services for this Matter and strictly adhering to the aforementioned commitments and safeguards, to prudently use AI-assisted technology based on professional judgment to potentially provide you with higher quality and more efficient services, please signify your consent by signing below.

• Right to Choose & Ask Questions: You have the full right to choose not to consent to our use of AI-assisted technology in handling your matter. This will not affect our commitment to providing services to you, although it might impact the efficiency of certain stages (especially involving large volume information processing). If you have any questions, concerns, or wish to discuss further details, please do not hesitate to raise them with your lead attorney. We are happy to provide detailed explanations.

• Right to Withdraw Consent: Even if you consent now, you have the right to withdraw this consent at any time by notifying us in writing. Upon receiving your notice, we will cease using AI-assisted technology to process information related to you in subsequent work (excluding processing already completed).

Client Acknowledgment and Consent

I/We (as the client(s) of [Your Law Firm/Company Name]) have carefully read and fully understand the entire content of this “Notice and Consent Form Regarding the Use of Artificial Intelligence (AI) Assisted Technology in Legal Services Provided to You.”

I/We understand the potential uses of AI-assisted technology, the Firm’s core commitments and safeguards, and the related technical limitations and potential risks.

I/We hereby [ ] AGREE / [ ] DISAGREE (Please check one box) for the Firm to prudently use AI-assisted technology in providing legal services for this Matter, in accordance with the principles and commitments stated in this notice.

(If disagreeing, no need to sign below)

Client (or Authorized Representative) Signature: _________________________ Signatory Name (Please print clearly): _________________________ Signatory Title (If signing for an entity): _________________________ Date Signed: ______ [Month] ____ [Day], ______ [Year]

(Copy for Firm/Organization Records)

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Suggestions and Reminders for Using These Templates & Checklists:

• Customization by Legal Professionals is Essential: Reiterating, the above templates are only illustrative frameworks. They must be customized by qualified lawyers based on specific legal requirements of the relevant jurisdiction (e.g., China, US, EU).
• Internal Discussion & Consensus First: Before formally issuing AI policies or introducing significant AI tools, ensure sufficient discussion and consensus are reached internally (among management, senior practitioners, IT, compliance) regarding goals, principles, risks, and management measures.
• Training is Key: Policy and process effectiveness ultimately depends on every employee’s understanding and execution. Invest adequately in comprehensive, ongoing training and communication.
• Maintain Dynamism & Adaptability: AI technology and the regulatory landscape change rapidly. Establish regular review and update mechanisms to ensure governance frameworks, policies, checklists, and templates remain current, effective, and compliant.
• Seek Expert Support When Needed: For complex AI governance strategies, high-risk application assessments, or related legal compliance issues, do not hesitate to seek help from external lawyers or consultants with deep expertise in AI law, data protection, information security, etc.

By systematically constructing and implementing these practical governance tools, abstract principles and requirements can be translated into concrete action guidelines and control measures. This provides a solid institutional guarantee and operational compass for legal service organizations to both ride the wave of AI transformation and navigate it safely and steadily.