How can safety be assured through the AI lifecycle and accountability through complex supply chains be achieved?
Background
HSE Strategic Objective: Maintain Great Britain’s record as one of the safest countries to work in (https://www.hse.gov.uk/aboutus/assets/docs/the-hse-strategy.pdf)
The legislation under which HSE operates has enabled Great Britain to become one of the safest places in the world to work through a combination of our extensive proactive regulatory work, enforcement, and prosecutions. To underpin policy, regulatory and operational activities in this strategic objective the evidence requirements in this area will include:
To develop the existing system of ongoing data collection, analysis, interpretation and result dissemination so that it remains fit for purpose to enable appropriate targeting of interventions and enforcement to maintain safety performance.
To extract insight and intelligence from data to develop data driven solutions which will improve safety performance by building on our learning and knowledge from the Discovering Safety programme.
To further develop understanding of the current and future world of work to ensure that our regulatory approach remains suitable and sufficient, taking account of any social, demographic and technological changes (including artificial intelligence).
To maintain and develop our risk models and evidence that supports statutory requirements and regulatory regimes to maintain safety within major hazard industries.
The questions provide more detail of the evidence needs within the main Areas of Research Interest Question Group.
Next steps
Get in touch: [email protected]
Source
This question was published as part of the set of ARIs in this document:
Topics
No topics assigned yet
Research fields
No research fields assigned yet
Related UKRI funded projects
-
Collective Risk Learning for Supply Chain Disruption Preparedness
The need for more efficient, resilient, supply chains has been highlighted by various government inquiries and amplified by recent world events including Brexit and Covid-19. As organisations outsource production to one ...
Funded by: EPSRC
Lead research organisation: University of Cambridge
Why might this be relevant?
Partially relevant as it addresses supply chain disruption preparedness, but not specifically focused on AI lifecycle and accountability.
-
Smart Integration of Process Systems Engineering & Machine Learning for Improved Process Safety in Process Industries (PROSAFE)
PROSAFE proposes a novel doctoral training program in the multidisciplinary field combining machine learning, artificial intelligence, and process systems engineering with domain knowledge of process industry and process...
Funded by: Horizon Europe Guarantee
Lead research organisation: Imperial College London
Why might this be relevant?
Partially relevant as it focuses on process safety in process industries using AI and machine learning.
-
A New Approach to Manufacturing Supply Chain Risk Management
In this project we plan to devise a completely new way of managing risk in Supply Chains, supported by independent technology that allows very safe assurance and risk data sharing controlled by each participant in the Su...
Funded by: ISCF
Lead research organisation: CROSSWORD CYBERSECURITY PLC
Why might this be relevant?
Partially relevant as it focuses on managing risk in supply chains but does not specifically address AI lifecycle safety or accountability.