arXiv subjects

Arthit Suriyawongkul

Publications and source records attributed to Arthit Suriyawongkul.

4 recordsLinked to original sources

arXiv2026

Building an Open AIBOM Standard in the Wild

Modern software engineering increasingly relies on open, community-driven standards, yet how such standards are created in fast-evolving domains like AI-powered systems remains underexplored. This paper presents a detailed experience report on the development of the AI Bill of Materials AIBOM specification, an extension of the ISO/IEC 5962:2021 Software Package Data Exchange (SPDX) software bill of materials (SBOM) standard, which captures AI components such as datasets and iterative training artifacts. Framed through the lens of Action Research (AR), we document a global, multi-stakeholder effort involving over 90 contributors and structured AR cycles. The resulting specification was validated through four complementary approaches: alignment with major regulations and ethical standards (e.g., EU AI Act and IEEE 7000 standards), systematic mapping to six industry use cases, semi-structured practitioner interviews, and an industrial case study. Beyond delivering a validated artefact, our paper documents the process of building the AIBOM specification in the wild, and reflects on how it aligns with the AR cycle, and distills lessons that can inform future standardization efforts in the software engineering community.

cs.SE↗

arXiv2025

Implementing AI Bill of Materials (AI BOM) with SPDX 3.0: A Comprehensive Guide to Creating AI and Dataset Bill of Materials

A Software Bill of Materials (SBOM) is becoming an increasingly important tool in regulatory and technical spaces to introduce more transparency and security into a project's software supply chain. Artificial intelligence (AI) projects face unique challenges beyond the security of their software, and thus require a more expansive approach to a bill of materials. In this report, we introduce the concept of an AI-BOM, expanding on the SBOM to include the documentation of algorithms, data collection methods, frameworks and libraries, licensing information, and standard compliance.

cs.SE↗

arXiv2024

A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms

This paper introduces a collaborative, human-centred taxonomy of AI, algorithmic and automation harms. We argue that existing taxonomies, while valuable, can be narrow, unclear, typically cater to practitioners and government, and often overlook the needs of the wider public. Drawing on existing taxonomies and a large repository of documented incidents, we propose a taxonomy that is clear and understandable to a broad set of audiences, as well as being flexible, extensible, and interoperable. Through iterative refinement with topic experts and crowdsourced annotation testing, we propose a taxonomy that can serve as a powerful tool for civil society organisations, educators, policymakers, product teams and the general public. By fostering a greater understanding of the real-world harms of AI and related technologies, we aim to increase understanding, empower NGOs and individuals to identify and report violations, inform policy discussions, and encourage responsible technology development and deployment.

cs.LG↗

arXiv2023

PyThaiNLP: Thai Natural Language Processing in Python

We present PyThaiNLP, a free and open-source natural language processing (NLP) library for Thai language implemented in Python. It provides a wide range of software, models, and datasets for Thai language. We first provide a brief historical context of tools for Thai language prior to the development of PyThaiNLP. We then outline the functionalities it provided as well as datasets and pre-trained language models. We later summarize its development milestones and discuss our experience during its development. We conclude by demonstrating how industrial and research communities utilize PyThaiNLP in their work. The library is freely available at https://github.com/pythainlp/pythainlp.

cs.CL↗