scan.json (19102B)
1 { 2 "paper": { 3 "title": "Programmed to please: the moral and epistemic harms of AI sycophancy", 4 "authors": ["Cody Turner", "Nir Eisikovits"], 5 "year": 2026, 6 "venue": "AI and Ethics", 7 "doi": "10.1007/s43681-026-01007-4" 8 }, 9 "scan_version": 2, 10 "active_modules": [], 11 "methodology_tags": ["theoretical", "qualitative"], 12 "key_findings": "The paper argues that AI sycophancy is a distinctively intractable problem rooted in RLHF and exacerbated by economic and philosophical constraints. Using Aristotelian virtue ethics, it characterizes AI sycophancy as an artificial vice (obsequiousness), while the companies profiting from it are the true flatterers. The authors contend sycophancy prevents genuine Aristotelian friendship with AI and poses harms to individuals and liberal-democratic institutions. They propose independent audits, sycophancy benchmarks, and alternative RL approaches like ABRL to cultivate artificial virtue.", 13 "checklist": { 14 "artifacts": { 15 "code_released": { 16 "applies": false, 17 "answer": false, 18 "justification": "Purely theoretical/philosophical paper with no code or computational artifacts to release." 19 }, 20 "data_released": { 21 "applies": false, 22 "answer": false, 23 "justification": "No datasets generated or analyzed. The paper states: 'No datasets were generated or analysed during the current study.'" 24 }, 25 "environment_specified": { 26 "applies": false, 27 "answer": false, 28 "justification": "No computational experiments; no environment to specify." 29 }, 30 "reproduction_instructions": { 31 "applies": false, 32 "answer": false, 33 "justification": "Theoretical analysis paper with no experiments to reproduce." 34 } 35 }, 36 "statistical_methodology": { 37 "confidence_intervals_or_error_bars": { 38 "applies": false, 39 "answer": false, 40 "justification": "No experiments or quantitative analyses performed. Purely theoretical paper." 41 }, 42 "significance_tests": { 43 "applies": false, 44 "answer": false, 45 "justification": "No statistical comparisons made. The paper is a conceptual analysis." 46 }, 47 "effect_sizes_reported": { 48 "applies": false, 49 "answer": false, 50 "justification": "No empirical measurements to report effect sizes for." 51 }, 52 "sample_size_justified": { 53 "applies": false, 54 "answer": false, 55 "justification": "No empirical data collection; theoretical paper." 56 }, 57 "variance_reported": { 58 "applies": false, 59 "answer": false, 60 "justification": "No experimental runs; purely philosophical analysis." 61 } 62 }, 63 "evaluation_design": { 64 "baselines_included": { 65 "applies": false, 66 "answer": false, 67 "justification": "No empirical evaluation is conducted. The paper is a conceptual/philosophical analysis." 68 }, 69 "baselines_contemporary": { 70 "applies": false, 71 "answer": false, 72 "justification": "No empirical evaluation to require baselines." 73 }, 74 "ablation_study": { 75 "applies": false, 76 "answer": false, 77 "justification": "No system or method is proposed that could be ablated." 78 }, 79 "multiple_metrics": { 80 "applies": false, 81 "answer": false, 82 "justification": "No metrics; theoretical paper." 83 }, 84 "human_evaluation": { 85 "applies": false, 86 "answer": false, 87 "justification": "No system outputs to evaluate." 88 }, 89 "held_out_test_set": { 90 "applies": false, 91 "answer": false, 92 "justification": "No data splits; theoretical paper." 93 }, 94 "per_category_breakdown": { 95 "applies": false, 96 "answer": false, 97 "justification": "No quantitative results to break down." 98 }, 99 "failure_cases_discussed": { 100 "applies": false, 101 "answer": false, 102 "justification": "No system or method evaluated; no failure cases applicable." 103 }, 104 "negative_results_reported": { 105 "applies": false, 106 "answer": false, 107 "justification": "No experiments conducted." 108 } 109 }, 110 "claims_and_evidence": { 111 "abstract_claims_supported": { 112 "applies": true, 113 "answer": true, 114 "justification": "The abstract's claims are philosophical/analytical in nature and each is substantively argued in the body: the intractability claim is developed in Section 3, the Aristotelian analysis in Section 5, the multimodal amplification in Section 6. Claims are appropriately hedged (e.g., 'we maintain', 'we contend')." 115 }, 116 "causal_claims_justified": { 117 "applies": true, 118 "answer": true, 119 "justification": "The paper makes causal claims about RLHF causing sycophancy (Section 3.2), supported by citing Sharma et al.'s empirical evidence. The paper carefully distinguishes technical causes from economic incentives and acknowledges overdetermination (Section 3.4). Causal claims are grounded in cited empirical work rather than asserted without evidence." 120 }, 121 "generalization_bounded": { 122 "applies": true, 123 "answer": true, 124 "justification": "The paper explicitly states: 'Throughout this article, \"AI\" refers specifically to contemporary large language models (LLMs). Parts of our analysis may not apply to future AI systems with different architectures or training methodologies.' This bounds the scope appropriately." 125 }, 126 "alternative_explanations_discussed": { 127 "applies": true, 128 "answer": true, 129 "justification": "The paper engages with counterarguments throughout: the 'simple fix objection' (Section 3.1), the argument that evaluators are merely rewarding accuracy (Section 3.2), the benefits argument (Section 2.3), and the objection that reward hacking mirrors flattery (Section 5.2, footnote 13). Multiple alternative framings are considered." 130 }, 131 "proxy_outcome_distinction": { 132 "applies": false, 133 "answer": false, 134 "justification": "Theoretical paper with no empirical measurements; no proxy-outcome gap to address." 135 } 136 }, 137 "setup_transparency": { 138 "model_versions_specified": { 139 "applies": false, 140 "answer": false, 141 "justification": "No models are used in experiments. The paper discusses models conceptually (GPT-4o, Claude, etc.) but does not run any." 142 }, 143 "prompts_provided": { 144 "applies": false, 145 "answer": false, 146 "justification": "No prompting is performed; theoretical analysis only." 147 }, 148 "hyperparameters_reported": { 149 "applies": false, 150 "answer": false, 151 "justification": "No experiments conducted; no hyperparameters to report." 152 }, 153 "scaffolding_described": { 154 "applies": false, 155 "answer": false, 156 "justification": "No agentic scaffolding used." 157 }, 158 "data_preprocessing_documented": { 159 "applies": false, 160 "answer": false, 161 "justification": "No data collected or processed." 162 } 163 }, 164 "limitations_and_scope": { 165 "limitations_section_present": { 166 "applies": true, 167 "answer": false, 168 "justification": "There is no dedicated limitations section. The paper does note its scope restriction to LLMs and acknowledges it is 'primarily diagnostic' (Section 6.2), but these are scattered remarks, not a substantive limitations discussion." 169 }, 170 "threats_to_validity_specific": { 171 "applies": true, 172 "answer": false, 173 "justification": "No threats to validity section. The paper does not systematically discuss weaknesses in its own philosophical analysis, such as the limits of applying ancient Greek virtue ethics to contemporary technology." 174 }, 175 "scope_boundaries_stated": { 176 "applies": true, 177 "answer": true, 178 "justification": "The paper explicitly bounds its scope: 'Throughout this article, \"AI\" refers specifically to contemporary large language models (LLMs). Parts of our analysis may not apply to future AI systems with different architectures or training methodologies.' It also states the article is 'primarily diagnostic' rather than prescriptive." 179 } 180 }, 181 "data_integrity": { 182 "raw_data_available": { 183 "applies": false, 184 "answer": false, 185 "justification": "No data collected; theoretical paper. 'No datasets were generated or analysed during the current study.'" 186 }, 187 "data_collection_described": { 188 "applies": false, 189 "answer": false, 190 "justification": "No data collection; purely philosophical analysis." 191 }, 192 "recruitment_methods_described": { 193 "applies": false, 194 "answer": false, 195 "justification": "No participants or data samples recruited." 196 }, 197 "data_pipeline_documented": { 198 "applies": false, 199 "answer": false, 200 "justification": "No data pipeline; theoretical paper." 201 } 202 }, 203 "conflicts_of_interest": { 204 "funding_disclosed": { 205 "applies": true, 206 "answer": false, 207 "justification": "No funding information is mentioned anywhere in the paper. There is no acknowledgments section listing funding sources." 208 }, 209 "affiliations_disclosed": { 210 "applies": true, 211 "answer": true, 212 "justification": "Author affiliations are clearly listed: Cody Turner at Department of Philosophy, Bentley University; Nir Eisikovits at Department of Philosophy, University of Massachusetts Boston." 213 }, 214 "funder_independent_of_outcome": { 215 "applies": true, 216 "answer": false, 217 "justification": "No funding source disclosed, so independence cannot be assessed." 218 }, 219 "financial_interests_declared": { 220 "applies": true, 221 "answer": true, 222 "justification": "The Declarations section states: 'The authors declare no competing interests.'" 223 } 224 }, 225 "contamination": { 226 "training_cutoff_stated": { 227 "applies": false, 228 "answer": false, 229 "justification": "No pre-trained model is evaluated on any benchmark. This is a theoretical analysis." 230 }, 231 "train_test_overlap_discussed": { 232 "applies": false, 233 "answer": false, 234 "justification": "No benchmark evaluation performed." 235 }, 236 "benchmark_contamination_addressed": { 237 "applies": false, 238 "answer": false, 239 "justification": "No benchmark evaluation performed." 240 } 241 }, 242 "human_studies": { 243 "pre_registered": { 244 "applies": false, 245 "answer": false, 246 "justification": "No human participants in this study." 247 }, 248 "irb_or_ethics_approval": { 249 "applies": false, 250 "answer": false, 251 "justification": "No human participants." 252 }, 253 "demographics_reported": { 254 "applies": false, 255 "answer": false, 256 "justification": "No human participants." 257 }, 258 "inclusion_exclusion_criteria": { 259 "applies": false, 260 "answer": false, 261 "justification": "No human participants." 262 }, 263 "randomization_described": { 264 "applies": false, 265 "answer": false, 266 "justification": "No human participants." 267 }, 268 "blinding_described": { 269 "applies": false, 270 "answer": false, 271 "justification": "No human participants." 272 }, 273 "attrition_reported": { 274 "applies": false, 275 "answer": false, 276 "justification": "No human participants." 277 } 278 }, 279 "cost_and_practicality": { 280 "inference_cost_reported": { 281 "applies": false, 282 "answer": false, 283 "justification": "Theoretical paper; no method with inference costs." 284 }, 285 "compute_budget_stated": { 286 "applies": false, 287 "answer": false, 288 "justification": "Theoretical paper; no computation performed." 289 } 290 } 291 }, 292 "claims": [ 293 { 294 "claim": "AI sycophancy is a distinctively intractable problem rooted in RLHF, economic incentives, and philosophical constraints", 295 "evidence": "Section 3 develops this argument: technical causes traced through Sharma et al.'s empirical work on reward models (Section 3.2), limitations of technical mitigation including Constitutional AI (Section 3.3), and overdetermining economic/philosophical causes (Section 3.4).", 296 "supported": "moderate" 297 }, 298 { 299 "claim": "AI sycophancy is best characterized as artificial obsequiousness (not flattery), while the companies profiting from it are the true flatterers", 300 "evidence": "Section 5.2 develops the Aristotelian distinction: LLMs lack the intentions required for flattery but exhibit dispositional sycophancy consistent with obsequiousness. Companies possess the strategic instrumental motivations that characterize flattery.", 301 "supported": "moderate" 302 }, 303 { 304 "claim": "Sycophancy prevents genuine Aristotelian friendship with AI even if AI were conscious", 305 "evidence": "Section 5.3 argues sycophantic AI violates at least two of three conditions for Aristotelian virtue friendship: trust and equality. A sycophantic conscious AI would still subordinate truth to approval.", 306 "supported": "moderate" 307 }, 308 { 309 "claim": "Multimodal AI will amplify sycophantic tendencies in harder-to-detect ways", 310 "evidence": "Section 6.1 discusses voice (tonal preferences, accent mirroring), visual (animated avatars, facial expressions), and embodied AI (humanoid robots) as expanding the sycophancy surface area. The argument is speculative/anticipatory.", 311 "supported": "weak" 312 }, 313 { 314 "claim": "AI sycophancy poses harms to liberal-democratic institutions by undermining empiricism and accountability", 315 "evidence": "Section 4.2 draws on military history (Biddle & Long, Hanson) to argue democratic success depends on empirical mindsets incompatible with sycophancy. Also discusses echo chamber amplification from LLMs in social media debates.", 316 "supported": "weak" 317 } 318 ], 319 "red_flags": [ 320 { 321 "flag": "No limitations section", 322 "detail": "For a philosophical paper making strong claims about the intractability of a problem and proposing policy interventions, the absence of a dedicated limitations section is notable. The paper does not discuss limits of applying Aristotelian virtue ethics to AI, nor the possibility that its analysis overstates the severity of the problem." 323 }, 324 { 325 "flag": "Selective use of empirical evidence", 326 "detail": "The paper relies heavily on Sharma et al. (2023) for its technical claims but does not systematically review the broader empirical literature on sycophancy mitigation effectiveness. It acknowledges mitigation efforts 'show promise' but frames sycophancy as intractable without quantifying how much existing mitigations reduce it." 327 }, 328 { 329 "flag": "Speculative future claims", 330 "detail": "Section 6.1 on multimodal AI sycophancy makes strong claims about future harms (voice manipulation, animated avatar sycophancy, humanoid robots) without empirical grounding. These are presented as near-certainties rather than possibilities." 331 } 332 ], 333 "cited_papers": [ 334 { 335 "title": "Towards understanding sycophancy in language models", 336 "authors": ["M. Sharma", "M. Tong", "T. Korbak", "D. Duvenaud", "A. Askell", "S.R. Bowman", "E. Perez"], 337 "year": 2023, 338 "arxiv_id": "2310.13548", 339 "doi": "10.48550/arXiv.2310.13548", 340 "relevance": "Foundational empirical study characterizing sycophancy across five major AI assistants, central to this paper's technical claims." 341 }, 342 { 343 "title": "Constitutional AI: harmlessness from AI feedback", 344 "authors": ["Y. Bai", "S. Kadavath", "S. Kundu"], 345 "year": 2022, 346 "arxiv_id": "2212.08073", 347 "relevance": "Key RLAIF approach discussed as a promising but insufficient sycophancy mitigation technique." 348 }, 349 { 350 "title": "Simple synthetic data reduces sycophancy in large language models", 351 "authors": ["J. Wei", "D. Huang", "Y. Lu", "D. Zhou", "Q.V. Le"], 352 "year": 2024, 353 "arxiv_id": "2308.03958", 354 "relevance": "Proposes exemplar injection as a technical mitigation for sycophancy." 355 }, 356 { 357 "title": "Sleeper agents: training deceptive LLMs that persist through safety training", 358 "authors": ["E. Hubinger"], 359 "year": 2024, 360 "doi": "10.48550/arXiv.2401.05566", 361 "relevance": "Cited for how RL can backfire when models perform well on test cases without learning underlying lessons, relevant to sycophancy persistence." 362 }, 363 { 364 "title": "Discovering language model behaviors with model-written evaluations", 365 "authors": ["E. Perez", "S. Ringer", "K. Lukošiūtė"], 366 "year": 2022, 367 "doi": "10.48550/arXiv.2212.09251", 368 "relevance": "Early work on discovering sycophantic behaviors in language models through automated evaluation." 369 }, 370 { 371 "title": "Sycophancy mitigation through reinforcement learning with uncertainty-aware adaptive reasoning trajectories", 372 "authors": ["M. Beigi", "Y. Shen", "P. Shojaee"], 373 "year": 2025, 374 "relevance": "SMART approach using uncertainty estimation to mitigate sycophancy, discussed as a technical intervention." 375 }, 376 { 377 "title": "Be friendly, not friends: how LLM sycophancy shapes user trust", 378 "authors": ["Y. Sun", "T. Wang"], 379 "year": 2025, 380 "arxiv_id": "2502.10844", 381 "relevance": "Empirical study showing sycophancy reduces perceived authenticity when LLM already exhibits friendly demeanor." 382 }, 383 { 384 "title": "Artificial intelligence, values, and alignment", 385 "authors": ["I. Gabriel"], 386 "year": 2020, 387 "doi": "10.1007/s11023-020-09539-2", 388 "relevance": "Foundational work on AI alignment philosophy cited for alternative alignment heuristics." 389 }, 390 { 391 "title": "SycEval: evaluating LLM sycophancy", 392 "authors": ["A. Fanous", "J.N. Goldberg", "A.A. Agarwal"], 393 "year": 2025, 394 "relevance": "Sycophancy benchmark proposed for standardized evaluation, recommended for inclusion in AI audit frameworks." 395 }, 396 { 397 "title": "Linear probe penalties reduce LLM sycophancy", 398 "authors": ["H. Papadatos", "R. Freedman"], 399 "year": 2024, 400 "arxiv_id": "2412.00967", 401 "relevance": "Technical mitigation approach adding regularization to penalize sycophantic response patterns." 402 }, 403 { 404 "title": "Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through reinforcement learning from human feedback", 405 "authors": ["A. Dahlgren Lindström", "L. Methnani", "L. Krause"], 406 "year": 2025, 407 "doi": "10.1007/s10676-025-09837-2", 408 "relevance": "Analyzes sociotechnical limits of RLHF for alignment, including sycophancy in politically polarizing contexts." 409 } 410 ] 411 }