Unified Multi-Criteria Evaluation Framework

• Unified multi-criteria evaluation framework is an integrated method that systematically aggregates quantitative, qualitative, and experiential signals to appraise models or artifacts.
• The framework defines precise criteria and hierarchical dependencies, enabling normalized scoring and weighted aggregation across metrics such as faithfulness, stability, and intelligibility.
• It dynamically adapts to different contexts by prioritizing relevant evaluation dimensions, thereby enhancing transparency and real-world performance.

A unified multi-criteria evaluation framework is an integrated methodological structure for systematically appraising models, decisions, explanations, or artifacts across a set of independent, sometimes hierarchically structured criteria. Such frameworks enable rigorous aggregation of heterogeneous evidentiary signals—whether quantitative, qualitative, functional, or experiential—while maintaining transparency regarding trade-offs and prerequisite relationships. Recent advances have operationalized this notion for domains such as explainable AI (XAI), model selection, human preference learning, safety benchmarking, and other complex evaluation contexts (Pinto et al., 2024, &&&1&&&, Gong et al., 28 Aug 2025, Xiong et al., 26 Nov 2025).

1. Conceptual Foundations and Motivation

Multi-criteria evaluation arises from the need to judge “usefulness” or “optimality” not on a single axis (e.g., accuracy), but with respect to plural objectives—such as faithfulness, intelligibility, robustness, fairness, or operational fitness. In explanation evaluation, for instance, the dual perspectives of ML engineering and HCI design led to semantic misalignments in what constitutes a satisfactory explanation. The unified framework mediates between model output and stakeholder expectation by positing that usefulness requires both faithfulness (alignment with internal model logic) and intelligibility (user comprehension), underpinned by stability and plausibility as prerequisites (Pinto et al., 2024). Analogous principles structure frameworks for multi-task image editing, multi-model comparison, and multimodal judge benchmarking (Gong et al., 28 Aug 2025, Ohi et al., 2024, Xiong et al., 26 Nov 2025).

2. Formal Criterion Definitions, Hierarchy, and Metricization

A framework’s foundation is its criterion set $\mathcal{C} = \{ c_1, ..., c_K \}$, with each criterion $c_k$ precisely defined and equipped with a scoring function or measurement metric. For XAI:

• Faithfulness: $$\mathrm{Faith}(E,M) = \mathrm{Corr}(g(E(x)), f(M(x)))$$
• Stability: $$\mathrm{Stab}(E,x,\delta) = 1 - \|E(x)-E(x+\delta)\|/\|\delta\|$$
• Plausibility: $\mathrm{Plaus}(E,x,u) = \mathrm{Sim}(E(x), H_u(x))$
• Intelligibility: $$\mathrm{Intell}(E,x,u) = \Pr[\text{user }u\text{ predicts }M(x) \mid E(x)]$$

Logical dependencies are encoded as a directed acyclic graph: stability enables faithfulness; plausibility enables intelligibility; both faithfulness and intelligibility must pass thresholds for the explanation to be deemed “useful” (Pinto et al., 2024).

In model comparison or multi-task contexts, criteria may be:

• Task-specific metrics: e.g., image fill quality, text alignment, structure, removal quality (Gong et al., 28 Aug 2025)
• General dimensions: correctness, completeness, clarity, efficiency, novelty
• Stakeholder-defined axes (e.g., fairness, adverse impact, generalizability) (Harman et al., 2024)

Multi-modal judge evaluation further complicates this by requiring pluralistic, per-criterion judgments (e.g., “Visual Grounding” vs. “Logic Coherence”), and introducing formal metrics for aggregate adherence and conflict sensitivity: PAcc (pluralistic accuracy), TOS (trade-off sensitivity), and CMR (conflict matching rate) (Xiong et al., 26 Nov 2025).

3. Unified Evaluation and Aggregation Workflows

Unified frameworks prescribe stepwise workflows suited to both intrinsically interpretable and black-box models. Typical procedure includes:

• Stakeholder and context specification
• Criterion scoring: using human surveys, experimental tasks, automated proxies, or similarity metrics
• Normalization and aggregation: applying mathematical normalization (min-max, z-score, etc.) to handle disparate scales
• Weighted summation or logical combination: e.g., composite suitability $S_j = \sum_i w_i x_{ij}^{\text{norm}}$ (Abdussami et al., 24 Jun 2025), or logical dominance tests (Agrawal, 2015)
• Iterative refinement: explanations or solutions are retried until minimum criterion thresholds are achieved

For multi-task RL evaluation, this is operationalized as averaging multi-criteria losses and training a single reward model to generalize across domains by adapting to both task identity and evaluation metric via prompt injection (Gong et al., 28 Aug 2025).

In benchmarking, unified frameworks compare candidate models by reducing raw scores to ordinal ranks, then applying decision-theoretic voting rules (Condorcet majority, Borda aggregation). This yields holistic, Pareto-informed rankings and transparently exposes trade-offs or dominance cycles (Harman et al., 2024).

4. Scenario and Task Adaptivity

Unified frameworks increasingly feature scenario-adaptive logic: the set and weighting of criteria are dynamically chosen according to the context (e.g., jailbreak detection with explicit separation of detection dimensions and harm scores per scenario) (Jiang et al., 8 Aug 2025). Adaptation mechanisms involve automated scenario classification, rule-based dimension selection, and expert weighting via methods such as Delphi consensus or AHP eigenvector normalization.

The benefit is more rigorous precision—criteria irrelevant to a scenario are omitted, weights reflect true contextual priorities, and evaluation scales (e.g., binary, ordinal, continuous) can be flexibly adjusted. Extensibility enables rapid integration of new scenario definitions, detection/harm dimensions, or regulatory requirements without recoding core logic (Jiang et al., 8 Aug 2025).

5. Empirical Validation and Case Studies

Empirical practices include functionally grounded (no human), human-grounded (simulated user tasks), and application-grounded (real-world deployment) evaluation phases (Pinto et al., 2024). Representative studies:

• Interpretable neural networks for gaming-the-system detection: explanations evaluated by forward and counterfactual user simulation (Pinto et al., 2024)
• Mask-guided image generation via RL: human annotators produce pairwise winner/loser labels for multi-dimensional evaluation, and a single reward model generalizes over text alignment, structure, removal, aesthetics, and usability dimensions (Gong et al., 28 Aug 2025)
• Multi-model judge benchmarks: assessment of more than two dozen judges on pluralistic evaluation metrics reveals both current model limitations and the impact of fine-tuned preference signals (Xiong et al., 26 Nov 2025)
• Fusion facility siting: expert-driven fuzzy weighting and geospatial data integration over 22 criteria, producing transparent, sensitivity-safe site rankings (Abdussami et al., 24 Jun 2025)

Ablation studies consistently show that both multi-criterion adaptivity and explicit weighting increase both alignment with expert judgments and benchmark performance.

6. Limitations, Extensions, and Best-Practice Guidance

Framework limitations include potential bias in expert weighting procedures, instability under omitted criteria, and challenges in score normalization with highly heterogenous data (Jiang et al., 8 Aug 2025, Harman et al., 2024). Practitioners are advised to:

• Define user and task context before selecting criteria
• Begin with stability or scenario classification to ensure downstream reliability
• Employ mixed evaluation modes to triangulate explanation or model utility
• Iterate explanation/model design based on criterion evaluation feedback

Advanced extensions include Bayesian multi-criteria aggregation for uncertainty and subgroup discovery (Mohammadi, 2022), automated multi-modal evaluation with vision-LLMs and harmonically weighted scores (Ohi et al., 2024), and non-numeric consensus formation using approximate reasoning (Yager, 2013).

7. Impact and Future Directions

The unified multi-criteria evaluation framework, spanning logical, functional, statistical, and reinforcement learning instantiations, underpins the current state-of-the-art in robust model assessment, interpretability, and multi-task adaptation. It is applicable across AI/ML, optimization, safety, engineering, multi-modal perception, and decision-support systems. Future work targets modular hierarchies, dynamic criterion generation, context-aware weighting, and data-driven learning of aggregation schemata to further expand flexibility, adaptability, and real-world fidelity (Pinto et al., 2024, Gong et al., 28 Aug 2025, Ohi et al., 2024, Xiong et al., 26 Nov 2025).