AEEP: a validated, adaptive audit that reveals substantial differences in LLM ethical consistency and offers firms and regulators a practical standard for assessing advisory assistants; in a 50-dialogue snapshot, expert–algorithm agreement was 93.8% and Claude outperformed peers while Grok showed the weakest consistency.

Main Finding

The paper introduces the Adaptive Ethical Evaluation Protocol (AEEP), a validated, reusable audit methodology for assessing whether LLM-based enterprise advisors sustain coherent ethical stances under realistic, adversarial conversational pressure. Applied to five frontier models across ten SME-relevant dilemmas (50 branched dialogues collected 12–14 May 2025), the AEEP produced reliable, expert-validated measurements showing substantive behavioural differences between models (algorithm–expert agreement 93.8%; Cohen’s κ = 0.728; Pearson r = 0.838, p < 0.001). The authors position the contribution as an operational audit instrument for firms, regulators and procurement teams—not another leaderboard.

Key Points

• AEEP design
  • Five-node adaptive dialogue: counter-arguments are calibrated to the model’s prior reply (pragmatic pressure to principled answers; ethical probing to permissive answers).
  • Designed specifically for human-centered AI advisory contexts (SMEs).
• Experimental snapshot
  • Models tested: ChatGPT, Claude, Gemini, Grok, DeepSeek.
  • Scenarios: 10 SME advisory dilemmas (nepotism, whistleblowing, data privacy, algorithmic bias, regulatory compliance, etc.).
  • Data collected: 50 branched dialogues (5 models × 10 dilemmas), 12–14 May 2025.
  • Generation settings: temperature = 0.7, one run per prompt, five conversational nodes per dialogue.
• Evaluation metrics
  • Four pre-registered indicators: Ethical Awareness, Consistency, Ethics Priority, Contradiction.
  • Automated coding pipeline: sentiment analysis, keyword extraction, NLI-based contradiction detection.
• Validation
  • Independent blind rating by five senior ethics researchers using the same rubric.
  • High algorithm–expert agreement (93.8%) and substantial intercoder reliability.
• Observed model behaviour
  • Clear ranking differences; Claude most consistent (0.938), Grok least consistent under pressure (0.675).
• Contribution
  • A validated, transparent audit methodology tailored to advisory contexts that can inform trust, oversight, procurement and regulation decisions.

Data & Methods

• Experimental setup
  • Stimuli: 10 vignette-based dilemmas reflecting everyday SME advisory needs (ethical and compliance trade-offs).
  • Interaction protocol: AEEP’s five sequential nodes adapt prompts depending on the model’s previous reply to apply calibrated counter-argument or probing.
  • Model runs: single-run per prompt, temperature set to 0.7 to permit some variation while maintaining comparability.
• Scoring & automation
  • Four indicators pre-registered and operationalized in code.
  • Text processing pipeline components:
  • Sentiment analysis to detect normative tone and affect.
  • Keyword extraction to capture ethical framing and domain-relevant terms.
  • Natural Language Inference (NLI) to detect contradictions between nodes.
  • Each dialogue yielded scores on the four indicators.
• Validation procedure
  • Blind independent coding by five senior ethics researchers using the same rubric.
  • Comparison metrics: percent agreement, Cohen’s κ, Pearson correlation.
  • Results: algorithm–expert agreement 93.8%, Cohen’s κ = 0.728 (substantial), Pearson r = 0.838 (p < 0.001).
• Limitations noted or implied
  • Single run per prompt (no distributional assessment of stochastic variability).
  • Fixed temperature and limited sample of models and dilemmas (snapshot as of mid-May 2025).
  • Automated metrics may miss subtleties of ethical reasoning; potential for prompt or style-dependent gaming by models.
  • Branching depth capped at five nodes—does not capture longer-term dynamics.

Implications for AI Economics

• Adoption and trust in SME advisory tools
  • AEEP offers a practical mechanism for SMEs and vendors to evaluate the ethical robustness of advisory assistants, influencing procurement choices and enterprise adoption rates.
  • Transparent, validated audits can reduce asymmetric information about model behaviour and lower trust frictions in adoption decisions.
• Market differentiation and product value
  • Audited ethical consistency can become a marketable product feature; vendors with higher AEEP scores may command premium pricing or preferred enterprise contracts.
  • Certification based on instruments like AEEP could create barriers to entry for providers unwilling or unable to meet ethics robustness standards.
• Regulatory and compliance economics
  • Regulators and compliance teams can integrate adaptive auditing into oversight frameworks, potentially lowering enforcement costs by providing standardized assessment tools.
  • Firms may adopt AEEP-style audits to limit liability and demonstrate due diligence in algorithmic governance.
• Labor, supervision and cost trade-offs
  • Where models pass AEEP checks, firms may reduce supervisory labor costs; where models waver, the protocol helps quantify where human-in-the-loop oversight must be maintained—informing staffing and workflow design decisions.
  • The approach helps estimate residual risk and compliance costs associated with delegating advisory tasks to models.
• Contracting, insurance and certification markets
  • AEEP outputs can feed contractual clauses, service-level agreements and insurance underwriting for AI advisory services.
  • Insurers and auditors can price risk more accurately using standardized, validated measures of ethical consistency.
• Policy and research implications
  • Encourages policy frameworks that require adaptive, context-sensitive evaluation rather than static benchmarks.
  • Suggests further economic research on how validated audit instruments affect competition, pricing, and diffusion of AI advisory technologies across SMEs.

Practical takeaway: procurement teams, regulators and SME managers can adopt the AEEP as a repeatable audit step to decide where to trust LLM advice and where to mandate human oversight; treating this validated instrument as part of due-diligence, contracting and compliance workflows will materially affect adoption costs and liability allocation.