2023-08-17 –, HS 119 - Maintainer track
The use of AI documentation such as repository cards (model and dataset cards), as a means of transparently discussing ethical and inclusive problems that could be found within the outputs and/or during the creation of AI artefacts, with the aim of inclusivity, fairness and accountability, has increasingly become part of the ML discourse. As limitations and risks centred documentation approaches have become more standard and anticipated with launches of new development e.g Chatgpt/GPT-4 system card and other LLM model cards.
This talk highlights the inclusive approaches that the broader open source community could explore when thinking about their aims when creating documentation.
In this talk we will first cover some of the current literature and standard approaches of documentation found within the open source community and within the ethical/ AI space. Building on this overview, we will then detail how to build on the strengths of the open source community and its ability to bridge the gap between academia and research. From which we will map to more inclusive and ethics focused components found in AI model documentation practises, and how they could be incorporated within open source documentation methods.
By the end of this talk we would have not only identified the limitations within current open source documentation practises, we will also explore how centering fairness, ethics and inclusivity can create richer documentation that is more wholly inclusive of the open source community it represents.
Rethinking open source documentation: bridging academia & industry, while creating richer documentation that is wholly inclusive of the open source community.
Category [Community, Education, and Outreach] –Learning and Teaching Scientific Python
Expected audience expertise: Domain –none
Expected audience expertise: Python –some
Research engineer excited and working on applied AI research and quantum ML and the intersections of ethical and inclusive practises.