The UK Research and Innovation (UKRI)'s Accountable, Responsible and Transparent Artificial Intelligence (ART-AI) recently appointed Husna M Siddiqi Lawson, a Bangladeshi, as chair of its independent advisory board.
ART-AI, funded as a centre for doctoral training (CDT) by the UKRI, produces interdisciplinary graduates who can act as leaders and innovators with the knowledge to make the right decisions on what is possible, what is desirable, and how AI can be ethically, safely and effectively deployed.
An information protection practitioner, she is currently leading Cloud Assurance Management at KPMG International Services Ltd.
She has over fifteen years of experience in information security, data protection, IT risk and transformation program management across a number of sectors including oil and gas, energy and utilities, consumer business, manufacturing and professional services, according to ART-AI's official disclosure.
She succeeds Lord Clive Hollick, who is a former member of both the House of Lords Select Committee on Science and Technology (2019-21) and the Lords’ Select Committee on Artificial Intelligence (2017-18.)
Last month he was elected chair of the House of Lords Select Committee on Industry and Regulators, and had to retire from chairing the ART-AI board.
"Very proud and honoured to be appointed Chair of the Independent Advisory Board, UKRI Accountable, Responsible and Transparent (ART) Artificial Intelligence at Bath University," she posted on Facebook.
Prior to becoming a consultant, Ms. Siddiqi was a lecturer in computer science and a software engineer.
She is also a World Economic Forum (WEF) contributor with publications in a number of reputed forums, including IAPP.
Ms Siddiqi aims to increase security and privacy awareness among diverse audiences using relatable examples, and is actively involved in global initiatives to promote diversity and inclusion and address unconscious bias.
ART-AI draws together a wide range of topics: from algorithms to ethics; robotics safety to computational and public policy; probabilistic machine learning to symbolic AI; provenance, transparency and uncertainty quantification to intelligibility and trust in heterogeneous intelligent systems; reinforcement learning to emotion in human-machine interaction; and many others.
