AI product cards: a framework for code-bound formal documentation cards in the public administration - Albana Celepija; Alessio Palmero Aprosio; Bruno Lepri; Raman Kazhamiakin (Data & Policy)
Currently, artificial intelligence (AI) is integrated across various segments of the public sector, in a scattered and fragmented manner, aiming to enhance the quality of people’s lives. While AI adoption has proven to have a great impact, there are several aspects that hamper its utilization in public administration. Therefore, a large set of initiatives is designed to play a pivotal role in promoting the adoption of reliable AI, including documentation as a key driver. The AI community has been proactively recommending a variety of initiatives aimed at promoting the adoption of documentation practices. While currently proposed AI documentation artifacts play a crucial role in increasing the transparency and accountability of various facts about AI systems, we propose a code-bound declarative documentation framework that aims to support the responsible deployment of AI-based solutions. Our proposed framework aims to address the need to shift the focus from data and models being considered in isolation to the reuse of AI solutions as a whole. By introducing a formalized approach to describing adaptation and optimization techniques, we aim to enhance existing documentation alternatives. Furthermore, its utilization in the public administration aims to foster the rapid adoption of AI-based applications due to the open access to common use cases in the public sector. We further showcase our proposal with a public sector-specific use case, such as a legal text classification task, and demonstrate how the AI Product Card enables its reuse through the interactions of the formal documentation specifications with the modular code references.