Arthit Suriyawongkul
AI Governance Researcher, ADAPT Centre, Trinity College Dublin
นักวิจัยการอภิบาลปัญญาประดิษฐ์ ศูนย์ ADAPT วิทยาลัยทรินิตี ดับลิน
Dublin, Ireland
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I'm a PhD candidate in topic of AI governance at Trinity College Dublin, funded by Research Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real). I also volunteer for Thai Netizen Network, a digital civil rights group based in Bangkok, and help maintain SPDX system bill of materials projects.
My interest is in software engineering standards and how it can facilitate interactions between the technical layer and the legal layer for an accountable AI. Part of my PhD work is around system trustworthiness and accountability vocabulary.
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Accountability Taxonomy for AI Software Bill of Materials
Software Bill of Materials (SBOM) are increasingly a requirement in many sectors, especially in critical infrastructure and public sector. This session will see how SBOM concepts can be use for the purpose of AI accountability.
A taxonomy for system trustworthiness and accountability will be presented. It is based largely on the European Union Artificial Intelligence Act (draft) requirements on technical documentation of a high-risk AI system and also inspired by related bills like US Algorithmic Accountability Bill (S.2892) and US Artificial Intelligence Research, Innovation, and Accountability Bill (S.3312).
A focus will be on how a software developer can capture required informational artifacts in a consistent way that allowed a (partially) automated auditing. A crosswalk with Software Package Data Exchange (SPDX) 3.0 AI Profile will also be discussed.
For a demonstration purpose in MLOps setting, a small open source Python package is created to show how standardized terms in the taxonomy can facilitate the documentation and record keeping from the source code level. The same terms can be use for querying/reporting. An example will be given in the an MLflow environment.
Open Source Summit North America 2024 Sessionize Event
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