12 Days of Christmas - Day 6: Knowledge
Dive into Open AI
Spend some time reading key issues in open AI, transparency, AI in use and for science.
Research: The UK Algorithmic Transparency Standard: A Qualitative Analysis of Police Perspectives
The UK Government’s draft ‘Algorithmic Transparency Standard’ is intended to provide a standardised way for public bodies and government departments to provide information about how algorithmic tools are being used to support decisions. The research discussed in this report was conducted in parallel to the piloting of the Standard by the Cabinet Office and the Centre for Data Ethics and Innovation.
The researchers conducted semi-structured interviews with respondents from across UK policing and commercial bodies involved in policing technologies. The aim was to explore the implications for police forces of participation in the Standard, to identify rewards, risks, challenges for the police, and areas where the Standard could be improved, and therefore to contribute to the exploration of policy options for expansion of participation in the Standard.
Research: User Journeys through 3 Open AI Collaboratives
Summary: "Open Artificial Intelligence (Open source AI) collaboratives offer alternative pathways for how AI can be developed beyond well-resourced technology companies and who can be a part of the process. To understand how and why they work and what additionality they bring to the landscape, we focus on three such communities, each focused on a different kind of activity around AI: building models (BigScience workshop), tools/ways of working (The Turing Way), and ecosystems (Mozilla Festival’s Building Trustworthy AI Working Group).
First, we document the community structures that facilitate these distributed, volunteer-led teams, comparing the collaboration styles that drive each group towards their specific goals. Through interviews with community leaders, we map user journeys for how members discover, join, contribute, and participate. Ultimately, this paper aims to highlight the diversity of AI work and workers that have come forth through these collaborations and how they offer a broader practice of openness to the AI space."
Update: Application of AI in science
Understanding the potential of machine learning in the next 5 – 10 years, and the actions required to build an environment of careful stewardship that can help realise its potential.
By processing the large amounts of data now being generated in fields such as the life sciences, particle physics, astronomy, the social sciences, and more, machine learning could be a key enabler for a range of scientific fields, pushing forward the boundaries of science.