Failure Modes Taxonomy

• Taxonomy of Failure Modes is a structured classification that defines how systems, algorithms, components, or sociotechnical processes fail based on architecture, empirical observation, and legal frameworks.
• It categorizes failures into distinct groups such as hardware, software, human, and interaction errors, with adaptations for domains like machine learning and complex systems.
• Hierarchical, ontological, and quantitative criteria enable systematic risk analysis, transparent legal attribution, and the design of robust, auditable platforms.

A taxonomy of failure modes provides a structured classification of ways in which systems, algorithms, components, or complex sociotechnical processes can fail to achieve their intended function. In technical research, the taxonomy is shaped by factors such as system architecture, computational models, empirical observations, and legal or ethical frameworks. The resulting taxonomies support risk analysis, comparative evaluation, legal attribution, and systematic remediation.

1. Foundational Formalisms for Failure Mode Taxonomy

Contemporary approaches to failure mode taxonomy often begin with mathematically rigorous abstractions. For human-augmented AI, the oracle-machine model formalizes “human-in-the-loop” (HITL) architectures as computational reductions where a finite-control machine T◦ interacts with an oracle function $f$ at designated states (Chiodo et al., 15 May 2025). This yields three canonical regimes for HITL, each corresponding to a distinct reduction:

• Trivial monitoring (total functions): $T^\mathrm{f}$ never invokes the oracle; human can only abort the process, not affect outcomes.
• Endpoint action (many-one reduction): $T^\mathrm{f}$ calls the oracle once at a critical juncture.
• Involved interaction (Turing reduction): $T^\mathrm{f}$ may make unbounded queries, maximizing human influence at the expense of traceability and explainability.

This computational lens distinguishes failure classes according to intervention capability, influence boundaries, and theoretical limits on system correction or override.

2. Universal High-Level Classes

Failure mode taxonomies invariably recognize several universal top-level categories, adapted to domain-specific context:

Domain	Taxonomy Root Categories
Complex Systems	Hardware, Software, Human, and Interaction Failures
Machine Learning	Intentional (adversarial/CIA) vs. Unintentional (safety/misspecification) failures
Field Software	Field-intrinsic faults (IEC, UAC, UEC, CE) vs. Bad-testing faults
AI Incident Analysis	Failure Cause (dataset, spec, model, deployment), System Goals, Methods/Tech

Hardware Failures encompass mechanical, thermal, chemical, electronic, and radiation-induced degradation mechanisms (Parhizkar et al., 2021). Software Failures include functional, value-related, timing, and interaction failures. Human Failures entail both unintentional error (slips, lapses, mistakes) and intentional violations. Interaction Failures arise at the interfaces and can cut across all component boundaries, often leading to fault cascades.

In ML-centric contexts, the Intentional/Unintentional dichotomy formalizes the adversarial versus accidental origins of failure—central to incident response, legal analysis, and risk modeling (Kumar et al., 2019).

3. Hierarchical and Ontological Structures

Recent taxonomic innovations introduce explicitly hierarchical or ontological relationships:

• Partition-based formalism for system safety: For Safety Instrumented Systems, failure modes partition the $(\text{measured}, \text{actual})$ state space into classes (e.g. “false negative”, “false positive”, “too high”, “too low”), with propagation relations computed compositionally along the program dataflow (Jahanian et al., 2020).
• GMF Ontology in open AI incidents: Classification proceeds as a triple $(g, m, F)$, where $g$ is the AI system goal, $m$ the method/technology, and $F$ a set of technical failure causes drawn from a defined category vocabulary with cross-taxonomy relations $R_{GM}$ and $R_{MF}$ (Pittaras et al., 2022).

In software field failures, the hierarchy distinguishes between field-intrinsic and bad-testing faults, with the former subclassified as:

• Irreproducible Execution Condition (IEC): Non-reproducible in test/lab
• Unknown Application Condition (UAC): Triggered by undocumented app behaviors
• Unknown Environment Condition (UEC): Arising from unmodeled runtime environments
• Combinatorial Explosion (CE): Unmanageable configuration spaces (Gazzola et al., 2017)

4. Domain- and Task-Specific Failure Modes

Sophisticated taxonomies target niche failure dynamics in particular classes of systems:

• Load-sharing complex systems: Modes determined by the topology of load redistribution. Six principal modes include “resilient operation”, “progressive cascade”, “catastrophic avalanche”, “capacity-limited loss”, “multi-modal congestion”, and “global congestion collapse”, each defined by scaling laws and phase transitions (Siddique et al., 2013).
• Disordered solids: Failure transitions in the random fiber bundle model (RFBM) exhibit six modes depending on disorder parameter β and load-sharing range R; critical boundaries are located via cluster-area integration, avalanche-size scaling, and abruptness criteria (Roy et al., 2016).
• Automotive safety systems: Five regimes—operational, fail-operational, fail-degraded, fail-safe, fail-unsafe—arise from four orthogonal criteria: fault presence, safe-state viability, functionality provision, and performance threshold (Stolte et al., 2021).

In ML and AI, taxonomies similarly capture both technical and socio-technical sources:

• ML: Adversarial perturbations (integrity), data poisoning (training-time integrity), inference/model stealing (confidentiality), backdoors/supply chain (integrity/confidentiality), system reprogramming (integrity/availability), distributional shift, reward hacking, common corruptions, and insufficient testing (Kumar et al., 2019).
• Multi-agent systems: Accidental steering, coordination failure, adversarial misalignment, input spoofing/filtering, and goal co-option define essential multiparty complexities (Manheim, 2018).
• Medical multi-agent LLMs: Thirteen collaborative failure modes including incorrect knowledge, flawed evidence extraction, flawed consensus, minority suppression, information loss, role assignment failures, and collaboration stasis (Gu et al., 11 Oct 2025).
• LLM applications: System-level failures such as hallucinations, logical inconsistency, planning collapse, prompt injection, context loss, tool invocation errors, business-rule violation, and cost-driven trade-offs (Vinay, 25 Nov 2025).
• Text-to-image: Attribute binding errors, object count, spatial relation, text rendering, perspective, physics violations, negation, anatomical inaccuracies, and style blending (Hayes et al., 1 Dec 2025).

5. Mapping Failure Modes to Computational and Legal Consequences

Taxonomic classification has critical consequences for both technical design and legal/moral assignment of responsibility:

• Computational reduction strength directly bounds the scope for human correction or override: Trivial monitoring (total function) ensures maximal explainability and simplest blame assignment, but provides almost no safety remedy. Endpoint action (many-one) permits one correction but is vulnerable to interface timing and decision bottlenecks. Involved interaction (Turing reduction) enables maximal human intervention, but responsibility and explainability become inherently diffuse (Chiodo et al., 15 May 2025).
• Field-intrinsic software failures—comprising 70% of production bugs—cannot be entirely eliminated by any in-house process, no matter how rigorous (Gazzola et al., 2017).
• System-level taxonomies reveal that failure does not localize only at the AI component: Human, process, interface, exogenous legal or organizational factors all generate distinct, often dominant, failure classes (Chiodo et al., 15 May 2025, Vinay, 25 Nov 2025).
• Legal regulation must recognize the limits inherent to each reduction regime: Over-emphasizing human presence (as in “trivial monitoring”) can lead to scapegoating without delivering safety; deeper HITL setups can guarantee substantive oversight but defy simple attribution (Chiodo et al., 15 May 2025).

6. Quantitative, Phase, and Empirical Characterizations

A mature taxonomy is operationalized via quantitative or formal criteria:

• Scaling laws, thresholds, and phase diagrams: Load-sharing and disordered systems are mapped by analytic or empirical boundaries between failure classes (e.g., critical values of load L, disorder β, redistribution range R) (Siddique et al., 2013, Roy et al., 2016).
• Statistical auditing and metric-based boundaries: LLM/collaborative diagnoses use explicit retention rates ($R_\mathrm{KEU}$), consensus thresholds, and argument-quality metrics to distinguish modes (Gu et al., 11 Oct 2025, Vinay, 25 Nov 2025).
• Hierarchical error partitioning: Fine-grained vision-language taxonomies assign errors by multi-level structure, each with directly measurable false positive, false negative, and precision/recall rates (Hayes et al., 1 Dec 2025).

These enable automated failure tracking, give actionable boundaries for system design, and facilitate cross-system benchmark comparisons.

7. Synthesis and Emerging Principles

Cross-cutting insights unify the field:

• No taxonomy can be flat; cross-cutting relationships, causal dependencies, and phase transitions are the norm—necessitating ontological, hierarchical, or compositional designs (Pittaras et al., 2022, Jahanian et al., 2020).
• System reliability emerges as a property of the entire socio-technical configuration: interaction boundaries, process and workflow architectures, and regulatory context all shape failure distributions (Chiodo et al., 15 May 2025, Parhizkar et al., 2021).
• Quantitative and empirical criteria, when feasible, are essential for nontrivial, actionable taxonomy: rates, thresholds, formal logic, and audit outcomes must ground each failure class.

Taxonomies of failure modes are thus indispensable not only for root-cause analysis, but for guiding the architecture of robust, auditable, and legally defensible technology platforms across domains and modalities.