Related papers: A Scoresheet for Explainable AI

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In Terms of Explainability: Refining Requirements for Self-Explainable Systems [0.0]
We propose unified definitions in explainability based on an analysis and combination of existing definitions.<n>We present structured explainability requirements that are necessary to build (self-)explainable systems.
arXiv Detail & Related papers (2026-08-06T13:58:14Z)
ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation [54.788849448970154]
We introduce ClarifyCodeBench, a novel interactive benchmark for evaluating Large Language Models' capability in resolving requirement ambiguity.<n>We conduct a systematic evaluation of six state-of-the-art LLMs using ClarifyCodeBench.<n>Our empirical results yield three critical insights: 1) Capability Decoupling: Strong code generation performance does not inherently translate to effective requirement clarification; 2) The Reasoning Paradox: While increased computational thinking enhances code correctness, it yields marginal gains in identifying ambiguities.
arXiv Detail & Related papers (2026-07-01T09:58:59Z)
From Quality Properties to Practice: A Guideline and Workflow for Explainability Requirements [1.9218231341036756]
Explainability is increasingly required in AI-enabled software systems.<n>Unguided large language model support can yield vague, inconsistent, or incomplete statements.<n>This paper presents a sequential, guideline-driven workflow for explainability requirements.
arXiv Detail & Related papers (2026-06-09T13:56:31Z)
Position: agentic AI orchestration should be Bayes-consistent [92.04373485710285]
This paper argues that coherent decision-making requires Bayesian principles at the orchestration level of the agentic system.<n>It provides examples and design patterns to illustrate how beliefs and utility-aware policies can improve agentic AI orchestration.
arXiv Detail & Related papers (2026-05-01T15:43:43Z)
The Necessity of a Unified Framework for LLM-Based Agent Evaluation [46.631678638677386]
General-purpose agents have seen fundamental advancements.<n> evaluating these agents presents unique challenges that distinguish them from static QA benchmarks.<n>We propose that a unified evaluation framework is essential for the rigorous advancement of agent evaluation.
arXiv Detail & Related papers (2026-02-03T08:18:37Z)
Towards Systematic Specification and Verification of Fairness Requirements: A Position Paper [2.8091685003204905]
We argue that discrimination is often the result of a lack of well-specified fairness requirements and their verification.<n>The fact that experts' knowledge about fairness is often implicit makes the task of specifying precise and verifiable fairness requirements difficult.<n>We propose the development of a knowledge graph-based framework for fairness.
arXiv Detail & Related papers (2025-09-22T19:37:18Z)
Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned [45.44933002008943]
This white paper presents the T"UV AUSTRIA Trusted AI framework.<n>It is an end-to-end audit catalog and methodology for assessing and certifying machine learning systems.<n>Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - it translates the high-level obligations of the EU AI Act into specific, testable criteria.
arXiv Detail & Related papers (2025-09-08T17:52:08Z)
Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs [17.373348983049176]
We propose RAGcceptance M2RE, a novel approach to generate acceptance criteria from multi-modal requirements data.<n>We show that our approach effectively reduces manual effort, captures nuanced stakeholder intent, and provides valuable criteria.<n>This research underscores the potential of multi-modal RAG techniques in streamlining software validation processes and improving development efficiency.
arXiv Detail & Related papers (2025-08-09T08:35:40Z)
Automatic Generation of Explainability Requirements and Software Explanations From User Reviews [2.2392379251177696]
This work contributes to the advancement of explainability requirements in software systems by introducing an automated approach to derive requirements from user reviews and generate corresponding explanations.<n>We created a dataset of 58 user reviews, each annotated with manually crafted explainability requirements and explanations.<n>Our evaluation shows that while AI-generated requirements often lack relevance and correctness compared to human-created ones, the AI-generated explanations are frequently preferred for their clarity and style.
arXiv Detail & Related papers (2025-07-10T00:03:36Z)
General Scales Unlock AI Evaluation with Explanatory and Predictive Power [57.7995945974989]
benchmarking has guided progress in AI, but it has offered limited explanatory and predictive power for general-purpose AI systems. We introduce general scales for AI evaluation that can explain what common AI benchmarks really measure. Our fully-automated methodology builds on 18 newly-crafted rubrics that place instance demands on general scales that do not saturate.
arXiv Detail & Related papers (2025-03-09T01:13:56Z)
Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions. Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes. We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
arXiv Detail & Related papers (2025-02-18T17:12:26Z)
Explainability-Driven Quality Assessment for Rule-Based Systems [0.7303392100830282]
This paper introduces an explanation framework designed to enhance the quality of rules in knowledge-based reasoning systems.<n>It generates explanations of rule inferences and leverages human interpretation to refine rules.<n>Its practicality is demonstrated through a use case in finance.
arXiv Detail & Related papers (2025-02-03T11:26:09Z)
An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms [62.878616839799776]
We propose SynthRAG, an innovative framework designed to enhance Question Answering (QA) performance. SynthRAG improves on conventional models by employing adaptive outlines for dynamic content structuring. An online deployment on the Zhihu platform revealed that SynthRAG's answers achieved notable user engagement.
arXiv Detail & Related papers (2024-10-23T09:14:57Z)
Specification Overfitting in Artificial Intelligence [1.8292622558116727]
Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency. High-level requirements such as fairness and robustness need to be formalized into concrete specification metrics. This paper defines specification overfitting, a scenario where systems focus excessively on specified metrics to the detriment of high-level requirements and task performance.
arXiv Detail & Related papers (2024-03-13T11:20:34Z)
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales [54.78115855552886]
We show how to construct over-complete invariants with a Convolutional Neural Networks (CNN)-like hierarchical architecture. With the over-completeness, discriminative features w.r.t. the task can be adaptively formed in a Neural Architecture Search (NAS)-like manner. For robust and interpretable vision tasks at larger scales, hierarchical invariant representation can be considered as an effective alternative to traditional CNN and invariants.
arXiv Detail & Related papers (2024-02-23T16:50:07Z)
Building Interpretable and Reliable Open Information Retriever for New Domains Overnight [67.03842581848299]
Information retrieval is a critical component for many down-stream tasks such as open-domain question answering (QA) We propose an information retrieval pipeline that uses entity/event linking model and query decomposition model to focus more accurately on different information units of the query. We show that, while being more interpretable and reliable, our proposed pipeline significantly improves passage coverages and denotation accuracies across five IR and QA benchmarks.
arXiv Detail & Related papers (2023-08-09T07:47:17Z)
How to Evaluate Explainability? -- A Case for Three Criteria [0.0]
We will provide a multidisciplinary motivation for three quality criteria concerning the information that systems should provide. Our aim is to fuel the discussion regarding these criteria, such as adequate evaluation methods for them will be conceived.
arXiv Detail & Related papers (2022-09-01T11:22:50Z)
A Methodology and Software Architecture to Support Explainability-by-Design [0.0]
This paper describes Explainability-by-Design, a holistic methodology characterised by proactive measures to include explanation capability in the design of decision-making systems. The methodology consists of three phases: (A) Explanation Requirement Analysis, (B) Explanation Technical Design, and (C) Explanation Validation. It was shown that the approach is tractable in terms of development time, which can be as low as two hours per sentence.
arXiv Detail & Related papers (2022-06-13T15:34:29Z)
A New Score for Adaptive Tests in Bayesian and Credal Networks [64.80185026979883]
A test is adaptive when its sequence and number of questions is dynamically tuned on the basis of the estimated skills of the taker. We present an alternative family of scores, based on the mode of the posterior probabilities, and hence easier to explain.
arXiv Detail & Related papers (2021-05-25T20:35:42Z)
Multisource AI Scorecard Table for System Evaluation [3.74397577716445]
The paper describes a Multisource AI Scorecard Table (MAST) that provides the developer and user of an artificial intelligence (AI)/machine learning (ML) system with a standard checklist. The paper explores how the analytic tradecraft standards outlined in Intelligence Community Directive (ICD) 203 can provide a framework for assessing the performance of an AI system.
arXiv Detail & Related papers (2021-02-08T03:37:40Z)
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge [59.87823082513752]
This paper investigates the injection of knowledge from general-purpose knowledge bases (KBs) into vision-and-language transformers. We empirically study the relevance of various KBs to multiple tasks and benchmarks. The technique is model-agnostic and can expand the applicability of any vision-and-language transformer with minimal computational overhead.
arXiv Detail & Related papers (2021-01-15T08:37:55Z)
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies [1.2762298148425795]
Lack of transparency is identified as one of the main barriers to implementation of AI systems in health care. We review the recent literature to provide guidance to researchers and practitioners on the design of explainable AI systems. We conclude that explainable modelling can contribute to trustworthy AI, but the benefits of explainability still need to be proven in practice.
arXiv Detail & Related papers (2020-07-31T09:08:27Z)

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