惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园_首页
博客园 - 【当耐特】
IT之家
IT之家
M
MIT News - Artificial intelligence
酷 壳 – CoolShell
酷 壳 – CoolShell
Martin Fowler
Martin Fowler
V
Visual Studio Blog
F
Fortinet All Blogs
The Cloudflare Blog
Last Week in AI
Last Week in AI
博客园 - 司徒正美
G
Google Developers Blog
Vercel News
Vercel News
爱范儿
爱范儿
小众软件
小众软件
WordPress大学
WordPress大学
I
InfoQ
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
MongoDB | Blog
MongoDB | Blog
A
About on SuperTechFans
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
C
Check Point Blog
Apple Machine Learning Research
Apple Machine Learning Research
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知

cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Exploring the Assessment List for Trustworthy AI in the C...
Markus Borg, Joshua Bronson, Linus Christensson, Fredrik Olsson, · 2021-03-05 · via cs.SE updates on arXiv.org

Artificial Intelligence (AI) is increasingly used in critical applications. Thus, the need for dependable AI systems is rapidly growing. In 2018, the European Commission appointed experts to a High-Level Expert Group on AI (AI-HLEG). AI-HLEG defined Trustworthy AI as 1) lawful, 2) ethical, and 3) robust and specified seven corresponding key requirements. To help development organizations, AI-HLEG recently published the Assessment List for Trustworthy AI (ALTAI). We present an illustrative case study from applying ALTAI to an ongoing development project of an Advanced Driver-Assistance System (ADAS) that relies on Machine Learning (ML). Our experience shows that ALTAI is largely applicable to ADAS development, but specific parts related to human agency and transparency can be disregarded. Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation. We present how we plan to develop the ADAS to ensure ALTAI-compliance. Finally, we provide three recommendations for the next revision of ALTAI, i.e., life-cycle variants, domain-specific adaptations, and removed redundancy.