FRAIM: Framing Responsible AI Implementation and Management

Seventeen multicoloured post-it notes are roughly positioned in a square shape on a white board. Each one of them has a hand drawn sketch in pen on them, answering the prompt on one of the post-it notes "AI is...." The sketches are all very different, some are patterns representing data, some are cartoons, some show drawings of things like data centres, or stick figure drawings of the people involved.
Rick Payne and team / Better Images of AI / AI is… / CC-BY 4.0
  • Led by Dr Denis Newman-Griffis, University of Sheffield

This project will work with four partner organisations across public, private, and third sectors to build shared learning, values and principles for responsible AI. This will enable best practice development, help organise information and support decision making.


Increasing applications of AI technologies have necessitated rapid evolution in organisational policy and practice. However, these rapid changes have often been isolated in individual organisations and sectors, with a lack of shared cross-sectoral learning and accompanying shared values for responsible and ethical AI (RAI). Meanwhile, RAI resources have proliferated, but few effectively address the challenges in implementing shared principles, further hindering best practice development. As policymakers and industry worldwide grapple to guide, regulate, and design RAI, there is a clear need for establishing shared values and knowledge of the factors involved in implementing and managing RAI in practice.


The Framing Responsible AI Implementation and Management (FRAIM) project will bring together cross-sector perspectives on organisational RAI policy and process to scope key stakeholders, shared values, and actionable research needs for building the evidence base on implementing and managing RAI. We have partnered with organisations representing example key areas in which AI use will significantly impact people’s lives, including local policy, information access, and cultural enrichment. Our scoping work with these partners will provide a strong foundation for future development of practices and interventions to enable an ecosystem approach to RAI, and creatively and critically examine organisational implementation and management of RAI.

Aims and Objectives

The project will focus on organisational policies and processes around using AI technologies, including pre-trained foundation models as well as context-specific machine learning, to help organise information and support decision-making. With this focus and the specific expertise of our partners, the project will address two key aims:


Aim 1: Map the network of key stakeholders and values in organisational policy and process towards RAI implementation and management.

Aim 2: Scope actionable needs for building a stronger evidence base around RAI implementation and management across sectors.

We will achieve these aims by working with our partners to complete three objectives:
Objective 1: To query and collate the values, questions, and implementation and management challenges being described in the current RAI discourse by performing a meta-analysis of RAI resources and literature.
Objective 2: To identify key stakeholders, practices, and values involved in implementing and managing RAI at organisational levels by conducting exploratory interviews with staff from partner organisations.
Objective 3: To scope specific plans for expanding the RAI evidence base to inform RAI implementation, management, and policy via a scoping workshop held in collaboration with project partners.

Potential applications & benefits

The scoping work in this project will establish clear directions and next steps for ecosystem-focused research and shared values to guide RAI policy and process within organisations. By drawing on multi-stakeholder perspectives and creatively engaging with the complex questions of RAI in practice, the project will benefit:
  • Partner organisations, through shared insights from other organisations’ approaches to RAI policy and process.
  • The RAI research community, through empirically-grounded mapping of values, policies, and processes for RAI use across organisational contexts.
  • The public, through insights into AI implementation and management and creative reflection on RAI policy and process in the world around them.
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