AI for Computational Design Science: A Responsible Human-AI Framework and Case Study on Short-Form Video Safety Surveillance

• URL: http://arxiv.org/abs/2609.05270v1
• Date: Fri, 04 Sep 2026 15:26:41 GMT
• Title: AI for Computational Design Science: A Responsible Human-AI Framework and Case Study on Short-Form Video Safety Surveillance
• Abstract summary: We develop AI for Computational Design Science (AI4CDS)<n>ChildRiskGuard is an interpretable artifact for detecting short-form videos inappropriate for children.<n>ChildRiskGuard achieves an F1 score of 0.769, substantially outperforming direct application of a general-purpose content-safety model.
• Score: 4.110053032708927
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artificial intelligence (AI) is transforming not only what information systems researchers design, but also how design research is conducted. Yet existing literature offers limited guidance for computational design science (CDS) when AI actively participates in problem formulation, resource construction, design search, evaluation, and knowledge abstraction. We develop AI for Computational Design Science (AI4CDS), a five-phase methodological framework in which AI expands problem and design search while researchers retain responsibility for domain grounding, admissibility, verification, and scientific judgment. Collaboration is governed by graduated trust, reversibility, auditability, and differentiated reproducibility. We instantiate AI4CDS through ChildRiskGuard, an interpretable artifact for detecting short-form videos inappropriate for children, while documenting AI interactions, rejected alternatives, corrections, and audit trails. The case translates audience-dependent safety and explanation faithfulness into three technical challenges and develops an artifact that separates generic from child-specific risk, represents distinct developmental-risk mechanisms, and makes concept-level explanations part of the predictive computation. ChildRiskGuard achieves an F1 score of 0.769, substantially outperforming direct application of a general-purpose content-safety model while remaining competitive with strong benchmarks. The primary contribution is AI4CDS as a responsible framework for AI-enabled CDS; ChildRiskGuard provides process and artifact evidence of how AI-expanded, researcher-governed design can generate and evaluate novel computational design knowledge.

Related papers

• Vibe Compiler: A Research-Logic Synthesis Tool That Runs without Prompt Engineering -Toward Enhancing Metacognition for Sustaining Agency in the Age of Generative AI- [0.0]
  We present the Vibe Compiler, a research-logic compiler that helps researchers transform vague ideas into coherent research logic.<n>The framework characterizes structural gaps along two dimensions: cognitive function (Synthesis vs. Analysis) and executing agent (human vs. AI)
  arXiv  Detail & Related papers  (2026-08-06T02:51:03Z)
• FLARE-AI: Flaw Reporting for AI [51.64618567247884]
  Flaw reporting for deployed AI systems is fundamental to identifying system failures and improving AI safety.<n>Yet the AI reporting ecosystem is fragmented: researchers who identify flaws often do not know what to report, and groups who receive reports rarely share them with other relevant stakeholders.<n>We audit 12 reporting systems published by AI developers, cybersecurity groups, and AI flaw aggregators.<n>We introduce FLARE-AI, an open-source AI flaw reporting system designed for interoperability with existing systems.
  arXiv  Detail & Related papers  (2026-06-30T12:27:09Z)
• PAIRED: A Process-Anchored Framework for Transparent Reporting of AI Contributions in Scientific Research [0.7614628596146601]
  Existing frameworks for reporting AI contributions are uniformly output-oriented.<n>No current framework can distinguish between a researcher who originated a research direction and one who adopted a direction proposed by AI.
  arXiv  Detail & Related papers  (2026-05-23T01:10:56Z)
• Governing AI-Assisted Security Operations: A Design Science Framework for Operational Decision Support [0.0]
  This study uses Kusto Query Language (KQL) and Microsoft Azure security capabilities as a bounded technical instantiation of an engineering management problem.<n>The study develops a governed AI query-broker artifact that separates AI planning from operational execution.
  arXiv  Detail & Related papers  (2026-05-10T13:35:17Z)
• Towards AI-Supported Research: a Vision of the TIB AIssistant [6.36260975777314]
  We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery.<n>We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.
  arXiv  Detail & Related papers  (2025-12-18T12:08:46Z)
• AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
  This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
  arXiv  Detail & Related papers  (2025-11-27T16:56:04Z)
• The next question after Turing's question: Introducing the Grow-AI test [51.56484100374058]
  This study aims to extend the framework for assessing artificial intelligence, called GROW-AI.<n>GROW-AI is designed to answer the question "Can machines grow up?" -- a natural successor to the Turing Test.<n>The originality of the work lies in the conceptual transposition of the process of "growing" from the human world to that of artificial intelligence.
  arXiv  Detail & Related papers  (2025-08-22T10:19:42Z)
• Computational Safety for Generative AI: A Signal Processing Perspective [65.268245109828]
  computational safety is a mathematical framework that enables the quantitative assessment, formulation, and study of safety challenges in GenAI.<n>We show how sensitivity analysis and loss landscape analysis can be used to detect malicious prompts with jailbreak attempts.<n>We discuss key open research challenges, opportunities, and the essential role of signal processing in computational AI safety.
  arXiv  Detail & Related papers  (2025-02-18T02:26:50Z)
• OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI [73.75520820608232]
  We introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities.<n>These challenges encompass a wide range of disciplines spanning seven fields and 62 international Olympic competitions, rigorously examined for data leakage.<n>Our evaluations reveal that even advanced models like GPT-4o only achieve a 39.97% overall accuracy, illustrating current AI limitations in complex reasoning and multimodal integration.
  arXiv  Detail & Related papers  (2024-06-18T16:20:53Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.