failure family

ai model and training failure

44 claims across 14 works.

• overfitting-in-repeated-dataset-use: Repeated use of the same dataset by data scientists creates an overfitting risk: the training model fits the test set so closely that its performance
• Overfitting from repeated reuse of the same data set: Repeated use of the same data set by data scientists creates an overfitting risk: the training model overfits the test set, which limits the performan
• tragedy of the commons in microdemocracy: Microdemocracies must confront the tragedy of the commons, because voters acting on self-interest independent of the totality of voters may, without c
• regression failure on novel situations: Suggestions that artificial intelligence will solve the rigidity of centralized reputation systems are misguided and will fail for the same reason, be
• absent reliability guarantee: Federated learning lacks theoretical guarantees of reliability and robustness, which makes its behavior unpredictable in practical applications.
• training data bias transmission: Bias in AI systems arises when algorithms incorporate discriminatory practices carried in their training data, and the resulting outputs reveal a prof
• engagement driven data distortion: AI learning is degraded by Web2 platforms because their engagement driven algorithms amplify extreme viewpoints and negativity, so the human sentiment
• systemic bias invisible to validation: Broad community governance of AI training identifies and mitigates bias more effectively than data validation alone, because validation focused approa
• unexplainable prediction: Concrete cases show the cost of AI opacity: Nvidia self driving cars that learn from human behavior might confuse the moon for a traffic light, and th
• privacy driven data starvation: Strict data privacy regulation such as the GDPR imposes stringent conditions on data sharing that limit the amount and variety of data available to AI
• training data bias propagation: A critical unsolved challenge for AI governance is bias mitigation, because biases enter inadvertently when algorithms incorporate discriminatory prac
• Scale Without Quality: Transformer neural network architecture removes the scale constraint on training data but not the quality constraint, so data quality continues to be
• Small Dataset Overfitting: The move by AI developers toward smaller training datasets raises the risk of overfitting, especially with complex models, which forces LLM developers
• Training data bias amplification: Deep learning models inadvertently learn and amplify whatever biases exist in their training data, so the composition of the training corpus, not the
• Distribution shift brittleness: Deep learning models adapt to changes in data distribution far less readily than human learning does, which limits their reliability once the operatin
• Restricted legal dataset access: In the legal domain the adoption of transformer based language models is blocked less by capability than by resources and access: training and deploym
• GNN adversarial vulnerability: GNNs are vulnerable to adversarial attacks that target both node features and graph structure, and their lack of interpretability remains a major obst
• Homogeneous graph assumption: Most GNN architectures assume homogeneous graph structures, so adapting them to heterogeneous graphs with diverse node and edge types remains an unsol
• Sparse reward learning failure: Deep reinforcement learning demands large amounts of training data, which suggests its algorithms differ fundamentally from human learning, and learni
• quality-filtered supply shortfall: The apparent abundance of internet text overstates the usable supply, because much of it fails quality thresholds for model training due to redundancy
• representation skew: When a training dataset disproportionately represents one region or demographic group, the resulting model produces skewed and sometimes inappropriate
• recursive synthetic dilution: As AI-generated content proliferates online it dilutes the diversity and originality of the text pool available for later training, producing performa
• clinical bias amplification: In healthcare, biased or stale training data produces algorithms that misdiagnose underrepresented populations and thereby reinforce existing health d
• annotator bias propagation: Biases held by human annotators or embedded in automated annotation systems are propagated into the models trained on their output, producing AI that
• expert system brittleness: Early legal expert systems failed because they were brittle: they could not handle unforeseen complexity and could not generalize beyond the domains t
• residual hallucination under RAG: Retrieval-augmented generation reduces but does not eliminate hallucination: leading legal research tools including Lexis+AI and Westlaw AI-Assisted R
• emotive-cognitive gap in NLP: Natural language processing lets predictive systems handle specialized legal terminology and competing interpretations, but it still cannot capture th
• fairness through unawareness failure: Bias in judicial AI arises because models are trained on historical data that reflect past inequities, and the standard remedy of fairness through una
• Western training data bias: Because AI systems are predominantly developed in the West and trained mostly on Western data, their outputs are liable to carry cultural biases that
• centralized discernment deficit: Centralized AI systems excel at scaling defined work but struggle to cultivate genuine discernment without consequential feedback, and they face escal
• Plausibility Truth Orthogonality: Probabilistic language models hallucinate because they are trained to predict statistically likely token sequences rather than to verify propositional
• No Policy Trained: No policy is trained anywhere in the paper.
• Reward Model Overoptimization: Reward-model overoptimization can raise proxy reward while degrading true reward as optimized policies move farther from the reference policy.
• AI Feedback Bias Propagation: RLAIF addresses scalability by substituting AI-generated preferences, but it propagates the AI labeler's own biases through the pipeline.
• Format D Training Untested: Format D would train on arguments accompanying a staked vote change, but that use is untested.
• Training Flywheel Unimplemented: The proposed four-format flywheel is not measured or implemented and is listed as an open problem.
• Signal Invariance Does Not Establish Policy Invariance: Remark 1 states only signal invariance and supports no policy-level consequence without a separability model and empirical training evidence.
• Training Pipeline Remains a Proposal: The three-stage RLRF training pipeline is a proposal, and the paper does not establish a signal-quality advantage before specifying it.
• Reward Model May Relearn Style Proxy: A fitted reputation reward model can learn style correlations absent from an externally checked outcome, and no such reward model is trained or tested
• Tail Lost First Under Recursive Training: When intelligence models repeatedly learn from data that earlier intelligence models produced, the rare cases disappear first: the unusual facts, mino
• Imperfect Verifier Centers The Model: Yi et al. (2026) report that an imperfect external verifier drives the model toward the verifier's own knowledge center.
• Curation Amplifies Curator Bias: Ferbach et al. (2024) report that curation acts as implicit preference optimization that amplifies the curator's biases.
• Fragmented Reference Accelerates Tail Loss: Qiao et al. (2026) report that verifiers which see only fragmented slices of the reference can accelerate tail loss.
• All Filters Worse Than No Filter: The design names a failure pattern in advance: if the likelihood filter, the equal-weight pool, and the reputation pool each carry the negative label