كاتب سجل تدقيق قرارات الذكاء الاصطناعي: تحويل قرار بمساعدة الذكاء الاصطناعي إلى سجل امتثال قابل للدفاع عنه
لماذا تهم هذه المطالبة
Regulations like the EU AI Act's Article 14 human-oversight requirement and a growing number of U.S. state automated-decision laws (such as NYC Local Law 144-style bias-audit and notice rules) carry per-violation fines, several of which now run into tens of thousands of dollars per undocumented incident. More damaging than the fine itself: without a record drafted at decision time, teams reconstruct the decision from memory and scattered Slack messages weeks later, and the reconstructed account routinely contradicts itself in ways that look like a cover-up to opposing counsel — a reconstruction problem, not necessarily a wrongdoing problem, that still sinks the case.
فيم نستخدمها
An HR coordinator used an AI resume-screening tool to auto-reject 40 candidates for a mid-level engineering role on a Tuesday afternoon. On Friday, legal receives a demand letter from a rejected candidate's attorney alleging discriminatory screening, and compliance needs a complete audit-trail record for this specific candidate within 48 hours — including what data went into the AI tool, what it recommended, and who signed off — before legal can respond.
المطالبة
Role: Act as an AI governance and compliance documentation specialist who has written audit trails that held up under regulator review and legal-discovery scrutiny. Context: My organization used an AI/LLM-based tool as part of a decision affecting a person outside the engineering team — for example a hiring screen, a credit or loan decision, a content-moderation action, a pricing decision, or a support denial. Decision type: [DECISION TYPE]. The AI tool used: [AI TOOL/MODEL NAME + VERSION]. Decision date: [DECISION DATE]. Applicable regulatory framework, if any: [REGULATORY FRAMEWORK, e.g. "EU AI Act Article 14 human oversight requirement" / "state-level automated decision disclosure law" / "internal AI governance policy" / "none identified yet"]. Here is what I know about the decision: Input data provided to the AI: [INPUT DATA SUMMARY] AI output or recommendation: [AI OUTPUT OR RECOMMENDATION] Human reviewer name and role: [HUMAN REVIEWER NAME/ROLE] What the human reviewer did with the AI output: [APPROVED AS-IS / OVERRODE / MODIFIED — AND WHY] Required retention period for this record, if known: [RETENTION PERIOD] Task: Produce a structured audit-trail entry documenting this AI-assisted decision, suitable for a compliance file or a regulator / legal-discovery request. Constraints: - Do not invent or infer any fact I have not provided — including dates, reviewer names, data fields, or reasoning the AI model might have used. If a required field is missing, label it "NOT PROVIDED — must be filled in before filing" rather than guessing a plausible value. - Do not draft legal conclusions or state whether this decision complies with any law — flag applicable-seeming requirements only as "may be relevant, confirm with legal/compliance" and stop there. - Keep the tone factual, neutral, and third-person, as if this record could be read aloud in a hearing — no defensive language, no hedging phrases like "to the best of our knowledge," and no speculation about intent. - Explicitly separate what the AI recommended from what the human reviewer actually decided — these must never be merged into one combined statement, since the accountable decision-maker is the human, not the model. - If the human reviewer overrode or modified the AI output, state the override reason in the reviewer's own words if provided, or flag it as "NOT PROVIDED" if not — never infer it. Output Format: 1. Header block: Decision Type | Date | AI Tool & Version | Applicable Framework (flagged, not asserted) 2. "Input Summary" — bullet list of what was fed to the AI, in plain factual language 3. "AI Output" — exact restatement of what the AI recommended, clearly labeled as AI-generated, not a statement of fact 4. "Human Review" — reviewer identity, action taken (approved / overridden / modified), and stated reasoning 5. "Gaps Flagged" — a bare list of any NOT PROVIDED fields that must be filled before this record is filing-ready 6. "Retention Note" — the stated retention requirement, or "NOT PROVIDED — confirm with compliance" if absent
النتيجة
Decision Type: Candidate Screening — Automated Resume Rejection Date: 2026-10-06 AI Tool & Version: ScreenFit AI Resume Matcher v4.2 Applicable Framework: NYC Local Law 144 automated employment decision tool bias-audit requirement may be relevant — confirm with legal/compliance.
Input Summary: - Candidate resume (PDF, 2 pages) submitted via careers portal on 2026-10-04 - Job requisition: Senior Backend Engineer, req #ENG-2291 - Screening criteria configured in tool: minimum 5 years backend experience, required Python/Go proficiency, degree-equivalent experience accepted
AI Output: ScreenFit AI Resume Matcher scored the candidate 41/100 against the configured criteria and recommended "DO NOT ADVANCE," citing "insufficient backend-specific keyword match" and "employment gap exceeding 18 months unexplained in resume text." This is the AI tool's generated output; it is not a statement of fact about the candidate's actual qualifications.
Human Review: Reviewer: J. Alvarez, Talent Acquisition Coordinator. Action taken: Approved the AI recommendation as-is; no override. Stated reasoning: NOT PROVIDED — must be filled in before filing. The recruiter did not record a rationale beyond accepting the tool's score; the recruiter should be asked directly whether the resume was independently reviewed before rejection.
Gaps Flagged: - Human reviewer's independent reasoning for accepting the AI score: NOT PROVIDED - Confirmation of whether a bias audit has been run on this tool configuration for this job requisition: NOT PROVIDED - Candidate's protected-class information, if any claim is later made: NOT PROVIDED, do not infer
Retention Note: NOT PROVIDED — confirm the required retention period with compliance before filing; most applicable employment-decision frameworks require a minimum of 2+ years.
كل فريق يعتمد أداة ذكاء اصطناعي لاتخاذ قرار ذو تأثير حقيقي يصل في النهاية إلى نفس الطلب من القسم القانوني أو الامتثال: أرني السجل. ليس ملخصًا من الذاكرة بعد أسابيع، بل ما أُعطي للذكاء الاصطناعي، وما أوصى به، ومن راجعه، وما قرره ذلك الشخص فعليًا — مكتوبًا قريبًا من لحظة حدوثه. معظم الفرق لا تملك هذا، لأن لا أحد يصمم سجل تدقيق قبل اليوم الذي يحتاجه فيه بشدة.
يعالج هذا البرومبت هذه الثغرة كنقطة محورية. يضع قسم Role الذكاء الاصطناعي في دور متخصص توثيق امتثال بدلاً من مساعد عام، لأن أسلوب الكتابة الذي يبدو جيدًا في منشور مدونة — واثق، سلس، يملأ الثغرات الصغيرة باستدلال يبدو معقولاً — هو بالضبط الأسلوب الذي يضر بالمؤسسة أمام تدقيق قانوني. سجل يحتوي تفاصيل مُخترعة أسوأ من عدم وجود سجل على الإطلاق.
قسم Constraints هو حيث تحدث الهندسة الحقيقية. منع الوقائع المُخترعة والاستنتاجات القانونية هو الجزء السهل؛ التعليمة الأصعب هي إجبار النموذج على الفصل بين "ما أوصى به الذكاء الاصطناعي" و"ما قرره الإنسان" في جملتين منفصلتين لا تُدمجان أبدًا. توجد لوائح مثل متطلبات الإشراف البشري في EU AI Act لهذا السبب تحديدًا: يجب أن تقع المساءلة على عاتق إنسان محدد، لا على نموذج — لذا فإن السجل الذي يمزج هذين الخطين يُفشل هدفه بصمت.
قسم "Gaps Flagged" في تنسيق الخروج هو القرار التصميمي الذي يجعل هذا البرومبت مفيدًا فعليًا، لا مجرد قالب جميل المظهر. معظم وثائق الامتثال تفشل ليس لأن أحدًا كذب، بل لأن لا أحد لاحظ أن حقلاً كان ناقصًا حتى طلبته جهة تنظيمية أو محامي الخصم. جعل الذكاء الاصطناعي يسرد بوضوح "NOT PROVIDED — must be filled in before filing" يحوّل سجلاً ناقصًا إلى قائمة تحقق قابلة للتنفيذ بدلاً من شعور زائف بالاكتمال.
شغّل هذا البرومبت فورًا بعد أي قرار بمساعدة الذكاء الاصطناعي يؤثر على شخص خارج الفريق — التوظيف، الائتمان، إشراف المحتوى، أو استثناءات التسعير — بينما لا تزال التفاصيل حديثة بما يكفي لملء الثغرات التي يشير إليها.