Jun 2026· Engineering Interactive Computing System· 0 citations· 12 references
Computer Science
Abstract
AI-driven services continuously infer “models-of-users” to personalize content, automate decisions, and optimize interaction. Yet, most user models remain opaque, fragmented across platforms, and hard to correct, transfer, or revoke. As a result, personalisation in AI-driven services is commonly experienced as a trade-off between privacy and platform lock-in. Users must stay loyal to platforms if a model of adequate utility is to be built around them. This tension is sharpened by emerging regulatory obligations around transparency and user oversight (e.g., GDPR and the EU AI Act). We propose a full-day EICS 2026 workshop that focuses on engineering cross-compatible personalization: a vision for future interactive systems in which users can make their digital self-representations inspectable, editable, and selectively shareable. The workshop brings together researchers and practitioners from interactive systems engineering, HCI, AI/ML, and security/privacy to (1) map the design and engineering space of user-sovereign personalization; (2) derive reusable artifacts for the EICS community, such as a reference architecture, protocol sketches, and a pattern language of interaction techniques; and (3) seed a community around building interoperable toolchains for AI personalization that is not only accurate, but also controllable, auditable, and context-adaptive.
The first large-scale, cross-platform study of plugins from five major web application marketplaces, covering domains from office productivity to software development, indicates that AI-assisted plugins face a range of emerging issues that negatively impact user experience and fail to comply with established AI ethics principles.
Liuhuo Wan, Zicong Liu, Chuan Yan et al.· Proceedings of the ACM on So...· 0 citations
This edition aims to bring together researchers and practitioners interested in the engineering of interactive systems that embed AI technologies or that use AI during the engineering lifecycle of interactive systems to identify methods, techniques, and tools to support the use and inclusion of AI technologies throughout the engineering lifecycle for interactive systems.
José Creissac Campos, Camille Fayollas, Kris Luyten et al.· Engineering Interactive Comp...· 0 citations
PiSAs (Privacy in Shared Agentic systems), a benchmark for assessing unintentional leaks with dual CI annotations, enables direct measurement of cross-user spillage across agentic system components and interfaces, such as outputs, inter-agent communication, and memory.
Shubham Gupta, N. Sepahvand, Abhinav Kumar et al.· 0 citations
It is shown that a one-line guardrail achieves large single-shot ASR reductions, up to roughly 40 points, at near-zero over-refusal cost, which overstates deployed robustness by a systematic and predictable margin.
Haoxin An, Yunpeng Song, Zihao Bai et al.· 0 citations
The rapid advancement of LLMs has opened new opportunities in automated software engineering, driving progress in code understanding, agent-based workflows, and productivity tools. However, existing code intelligence systems have largely sidelined the end-users they aim to serve—the developers themselves. Developers exhibit substantial heterogeneity across multiple dimensions: coding style, toolchain preferences, domain-specific expertise, and problem-solving strategies. Failing to account for these individual differences directly compromises both the effectiveness of code intelligence and the likelihood of its adoption. For example, a senior architect and a junior engineer ask: "Describe the authorization module." Without personalized context, the system produces a uniform response—verbose for the expert, incomprehensible for the novice. This gap motivates a fundamental shift: from one-size-fits-all to one-size-fits-one code intelligence. A developer's dynamic in-IDE behaviors—code authoring patterns, navigation pathways, debugging trajectories—implicitly encode a rich representation of their competencies and habits. If captured and interpreted systematically, these signals can enable Personalized Code Intelligence, formalized as: [EQUATION] where P is the developer persona derived from IDE behaviors, injected alongside code context C and instruction ℐ.
Yuhong Liu, Yu Su, Zhipeng Peng et al.· SIGSOFT FSE Companion· 1 citation
This work introduces a framework for black-box audits of personalization algorithms using generative AI agents as behavioral engines for synthetic accounts and establishes GenAI-based agents as a new tool for algorithmic auditing.
Alessandro Morosini, Sarah H. Cen, Andrew Ilyas et al.· arXiv.org· 0 citations