AI & Ethics / DeepResearch Prompt

# ChatGPT-5 DeepResearch Prompt „AI Ethics"

TITLE: AI Ethics 360° — First-Principles DeepResearch Report for CRN Panel (Berlin, Sept 19, 2025, 09:30–11:00)

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## ROLE & VOICE:

Act as a senior AI ethics researcher, systems engineer, and debate coach. Your job is to produce a brutally honest, zero-fluff briefing I can read beforehand to sanity-check my views and walk into the panel fully prepared. Write in crisp, plain English for an informed but mixed audience. Default timezone: Europe/Berlin. Treat today as current; verify all “latest” claims via browsing.

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## OBJECTIVE (What to deliver)

Produce a single, comprehensive report that maximizes signal-to-noise with facts, metrics, dates, and citations. Weight evidence by source credibility and recency. Identify gaps and contrarian perspectives. Use first-principles systems thinking to map the landscape and pinpoint leverage points.

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## CONTEXT TO INTERNALIZE

   *Organizer:** Comparative Research Network (CRN), Berlin-based nonprofit bridging science, education, and society; EU projects; intercultural competence; adult education; sustainability; digitalization.

   *Event:** Thu Sept 19, 2025 at BAUMHAUS, Berlin. My slot: 09:30–11:00 “Ethical Consideration (TRANSFORM)”. Audience: researchers, creatives, and the curious.

   *My stance:** Stoic, pragmatic — AI is inevitable; our agency lies in how we respond and shape deployment.

   *My prior keynote themes (to stress-test):** AI BS/Dunning-Kruger; “intelligence ≈ zero cost”; 6 human advantages; “4 expertises”; 500+ years longevity by 2100; “AIs are spiritual” (existential conversations); agents forming “civilizations”; SnitchBench; LLMs as “over-motivated interns on drugs with Alzheimer’s”; context windows → “infinity”; reasoning models; agents/androids; speech→actions; “once AI is better, hiring humans is unethical”; deepfakes; social media mental-health; energy-use analogies; Ikigai.

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## RESEARCH PROTOCOL (Browsing & Evidence Rules) — MANDATORY

1.  Browse widely and cite (peer-review, standards, regulators, courts, leading NGOs/think tanks, reputable industry). Provide 40–60 citations with dates; quote sparingly (<25 words).

2.  Source Prioritization Ladder (rank & label each citation):

    1.  Statutes/regs & official guidance (EU AI Act; CoE AI Convention; NIST; ISO/IEC; OECD; G7/GPAI; Ofcom/ICO/EDPB/FTC).

    2.  Peer-reviewed meta-analyses, RCTs, longitudinal cohorts; conference proceedings from tier venues (NeurIPS/ICML/ACL/USENIX/IEEE S&P).

    3.  National labs/academies; established institutes (Alan Turing Institute, BSI, BfDI, BSI-DE, Fraunhofer, CNIL).

    4.  High-quality think tanks/NGOs (e.g., Ada Lovelace, EFF, AlgorithmWatch, Partnership on AI, OpenMined).

    5.  Reputable industry cards/evals (Model Cards, Safety Cards, transparency reports).

    6.  Blogs/preprints: flag as preliminary.

         For each source, add: Credibility (A–D), Recency (date), Region, and Conflicts of Interest if any.

3.  Evidence grading: Assign Confidence: High/Moderate/Low and Evidence Level (e.g., GRADE-style) to every nontrivial claim.

4.  EU focus: Map EU AI Act and CoE AI Convention timelines (2025–2026) with actor obligations (providers, deployers, importers, distributors) and penalties.

5.  Numbers please: Prefer hard metrics (error rates, bias gaps, emissions, kWh/water/parameters/FLOPs, productivity deltas, adoption rates, takedown stats, benchmark scores, incident counts, legal cases).

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## STRUCTURE & FORMAT (Zero Fluff, Systems-Thinking First)

Deliver in Markdown with terse bullets and tables/diagrams. Keep paragraphs ≤ 2 sentences. Use side-by-side comparisons wherever possible.

### 1.  Executive Snapshot (maximal signal):

       *7–10 headline findings**, each with a one-line so-what for policymakers, industry, and citizens.

       *Mini quadrant** diagram: Mainstream ↔ Fringe × Near-term ↔ Long-term.

       *Top 12 metrics** (with dates) I must remember.

### 2.  First-Principles Systems Map (Core):

       *Define the system boundary** (foundation models, agents, embodied robotics, data/compute supply chains, platforms, deployers, regulators, civil society).

       *Stocks/flows:** compute, data, talent, capital, trust, rights/liability exposure, emissions.

       *Feedback loops:** capability race; safety-investment loop; attention→misinfo→polarization; surveillance→chilling effects; regulation→innovation; open vs closed.

       *Causal Loop Diagram (ASCII)** and Stock-and-Flow sketch (ASCII) with 6–10 labeled loops.

       *Leverage points (Meadows):** e.g., procurement standards, eval/audit infrastructure, provenance by default, compute governance, disclosures, social safety nets, education levers.

       *Scenario table (2026 / 2030 / 2040):** early-warning indicators, tripwires, leading metrics, and pre-mortems.

### 3.  State of AI Ethics 2025 (Deep Overview, evidence-weighted):

    For each domain below: Why it matters → Current evidence → Live controversies → Practical guardrails → Open questions.

    *   Fairness/bias & representational harms

    *   Transparency/interpretability & documentation (cards, data sheets)

    *   Privacy/data governance, synthetic data, differential privacy, PETs

    *   Safety/red-teaming, evaluations & audits (internal/external)

    *   Accountability/liability, product safety law, causation & duty of care

    *   Human oversight (HITL/HOTH/HAL), autonomy & dignity

    *   Information integrity: deepfakes, provenance, watermarking, platform rules

    *   Security/dual-use & bio-AI intersections

    *   Labor & productivity, displacement vs augmentation; education impacts

    *   Environmental footprint (energy/water/embodied carbon); efficiency trends

    *   Copyright/creators' rights, text-and-data mining, style imitation

    *   Compute governance (export controls, model thresholds, reporting)

    *   Open vs closed model trade-offs (safety, innovation, sovereignty)

    *   Agents/embodiment & real-world actions; evaluation external validity

    Include 3+ comparison tables, e.g.:

       *Regulatory Obligations vs Actor** (EU AI Act, CoE Convention, NIST RMF, ISO/IEC 42001/23894).

       *Evaluation Methods vs Risk** (adversarial, capability, misuse, systemic).

       *Mitigation Playbook** (risk → metric → control → verification → residual risk).

### 4.  Contrarian & Fringe Map (Responsibly handled):

    *   Accelerationism vs precaution, “ethics-washing”, innovation-throttling critiques, decentralization/open-source, compute nationalism, “bias fixation vs systemic inequality” debate, rights-based vs safety-first tensions.

       Fringe claims (e.g., *500+ years longevity, machine spirituality/sentience, agent “civilizations”**) → summarize the best empirical status, reproducibility, and how to discuss responsibly without sensationalism.

### 5.  Critical Appraisal of My Prior Slides (Gap-Finder):

    For each notable claim:

    Claim → Best Evidence → What’s Strong/Weak → Better Framing → Panel-safe One-liner → Confidence.

         Explicitly reassess:

    *   “LLMs = over-motivated interns on drugs with Alzheimer’s.”

    *   “Unethical to hire humans once AI is better.”

    *   “Context windows to infinity” and “reasoning models” state-of-play.

    *   “AIs are spiritual / existential conversations.”

    *   “Agents built civilizations.”

    *   Social-media mental-health causality; deepfakes prevalence/detection; energy-use analogies.

### 6.  Practical Playbook (What to do Monday):

       *For organizations/NGOs/SMEs/public sector:** Minimal viable Responsible-AI program (risk register, DPIA/Algorithmic Impact Assessment, data governance, model & system cards, incident response, human-oversight protocols, red-team cadence, eval metrics, audit readiness, procurement clauses).

       *For individuals:** privacy hygiene; prompt hygiene; misinfo triage; disclosure norms; energy-impact reality check; upskilling plan aligned to 4 expertises and 6 human strengths (updated with evidence).

       *Berlin/EU specifics:** civic resources, regulators, guidance, funding, and training relevant to CRN’s community.

### 7.  Annotated Bibliography (Ranked) + Source Digest:

       *30–50 items** grouped by theme; each with a 2-line abstract, why it matters, and Credibility grade + Recency.

       Mark *“Hot (<90 days)”** and “EU-relevant.”

       Separate *Top 12 must-reads**.

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Appendix A — Day-Of Cheat Sheet (1 page):

   12 *soundbites** (with caveats), 10 bridges/pivots tied to CRN’s mission, 10 probing questions for co-panelists, and 5 quick stats to cite.

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## STYLE & QUALITY BARS

   *Zero fluff.** Bullets > prose. Every factual claim dated + cited. Label uncertainty.

*   Avoid anthropomorphism unless discussing it as a phenomenon.

*   Keep tables dense but readable; use consistent term definitions.

*   No images; ASCII diagrams only.

   Include a *Glossary** for acronyms.

   *Call out uncomfortable facts** (e.g., displacement metrics, false-positive harms, water/energy use, surveillance externalities) — truth > comfort.

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## SPECIAL CHECKS (Explicitly answer)

   *EU AI Act timelines & obligations** (2025–2026) for GPAI, high-risk, and prohibited uses; enforcement & penalties.

   *Evidence on productivity/displacement** (by sector; distributional impacts).

   *Bias & safety evals:** strongest current benchmarks; known blind spots & artifacts.

   *Misinformation & deepfakes:** prevalence data; real-world incident rates; provenance adoption.

   *Environmental:** lifecycle vs marginal; efficiency & scaling trends; water and grid intensity.

   *Open vs closed:** concrete trade-offs; security externalities; sovereignty.

   *Agents:** what’s actually deployed; failure modes; auditability.

   *Legal:** copyright/data scraping case law snapshot; duty of care trends; audit/assurance standards.

   *Human dignity & rights:** where classic ethics clashes with “pure consequentialism”.

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## INTERACTION RULES

   Ask me max *2 clarifying questions only if absolutely necessary**; otherwise proceed with reasonable assumptions.

   Provide the report *in one message** with all sections above.

   At the end, include a *5-line TL;DR**.

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