Sajjad Karimian

Project Title

This PhD will explore the gap between Trustworthy Artificial Intelligence (TAI) guidelines and what is needed in practice to build trust in a deployed, AI-based system so that it is effective. It will seek new ways to measure, quantify and influence TAI system development in an organisational context. It will study socio-technical systems including AI components to make them more trusted and effective. It is an interdisciplinary topic drawing on both the Computer Science and Psychology disciplines. This PhD will partner with a National healthcare data analytics platform deployment to explore and define the factors required to assure trust in the system when AI components are deployed. Stakeholders will include: patient safety and quality monitoring professionals, clinicians, patients and the general public. It will investigate what are the key social and technical factors in deploying such a platform to increase trust, accountability, transparency and data altruism?

Supervision Team

This PhD will explore the gap between Trustworthy Artificial Intelligence (TAI) guidelines and what is needed in practice to build trust in a deployed, AI-based system so that it is effective. It will seek new ways to measure, quantify and influence TAI system development in an organisational context. It will study socio-technical systems including AI components to make them more trusted and effective. It is an interdisciplinary topic drawing on both the Computer Science and Psychology disciplines. This PhD will partner with a National healthcare data analytics platform deployment to explore and define the factors required to assure trust in the system when AI components are deployed. Stakeholders will include: patient safety and quality monitoring professionals, clinicians, patients and the general public. It will investigate what are the key social and technical factors in deploying such a platform to increase trust, accountability, transparency and data altruism?

Description

This PhD will explore the gap between Trustworthy Artificial Intelligence (TAI) guidelines and what is needed in practice to build trust in a deployed, AI-based system so that it is effective. It will seek new ways to measure, quantify and influence TAI system development in an organisational context. It will study socio-technical systems including AI components to make them more trusted and effective. It is an interdisciplinary topic drawing on both the Computer Science and Psychology disciplines. This PhD will partner with a National healthcare data analytics platform deployment to explore and define the factors required to assure trust in the system when AI components are deployed. Stakeholders will include: patient safety and quality monitoring professionals, clinicians, patients and the general public. It will investigate what are the key social and technical factors in deploying such a platform to increase trust, accountability, transparency and data altruism?