Domain Co-Leaders: Dr Gentry White and Professor Kevin Desouza
ECR Co-lead: Dr Aiden Price
Partnering with government agencies to improve people’s lives.
Developing cutting-edge, data-driven solutions to drive innovation in public agencies, public policy processes, and governance systems. Our research:
- Addresses the urgent challenges facing public agencies trying to make sense of their vast data reservoirs to create and implement evidence-driven public policy.
- Designs and evaluates computational solutions driving innovation in the business of government, and
- Solves challenges faced by public agencies designing, developing, and deploying data-driven systems
Topics in Modern Data Science Series
“Using ML OPs to Drive Real World Impact” – Dr Anton Lord, Leap In!
Abstract: In recent years there has been a massive uptake of artificial intelligence in industry. Impacts of this are seen in many aspects of our lives, from security measures implemented by banks to assisting with decision making in healthcare as well as more traditional recommender systems. However around 90% of models developed for industry never make it to production. In this talk I will discuss the current best practice of machine learning operations and provide recommendations on how to implement AI into business critical tasks while minimising the risk of adverse outcomes. Watch the video of the seminar:
“Generative AI: Hype or Reality” – Prof Richi Nayak, Centre Deputy Director
Abstract: The emergence of ChatGPT has sparked both excitement and scepticism regarding the potential impact of Generative AI on various industries and society at large. Generative AI encompasses a range of techniques based on deep learning and neural networks to create original content such as text, images, music, and even human-like conversations. In this talk, I will delve into the workings of Generative AI, specifically focusing on highlighting the unique capabilities of Large Language Models. Additionally, I will address the realistic risks associated with the use of Generative AI. This seminar aims to equip attendees with the knowledge and critical thinking necessary to effectively engage with Generative AI in an ever-evolving landscape. Watch the video of the seminar:
“Responsible ML: Expections of NSOs” – Claire Clarke, Acting director of the Methodology Futures section of the Methodology and Data Science Division of the ABS and the co-chair of the QUT Centre for Data Science Domain for Government Systems.
Abstract: There has been considerable debate and discussion about the principles of responsible AI and machine learning for both public and private sector organisations. Fairness, human oversight, privacy, and explainability are all universally agreed upon as important. Is it sufficient for National Statistical Offices to abide by these generally decided principles? Do our unique roles require more rigour on some aspects of ML responsibility? Are there some aspects we can be more relaxed about? And how do we communicate this in an atmosphere of growing concern about AI and ML? Watch the video of the seminar:
Responsible Data Science Symposium – December 2021
ACEMS and the QUT Centre for Data Science ABS Co-Chair of Government Statistics hosted the inaugural Responsible Data Science Symposium on 3 December 2021.
1. A/Professor Gentry White (ACEMS, Associate Investigator; and QUT Centre for Data Science, ABS Co-Chair Data Science in Government Systems Domain) – Welcome & Exploratory Overview of “Responsible Data Science”
2. Professor Rachel Thomas (Fast.ai, Co-founder; QUT Centre for Data Science Data Scientist in Residence) – Ethical Artificial Intelligence
3. Professor Timothy Miller (Centre of AI and Digital Ethics, Co-Director) – Explainable Artificial Intelligence (XAI)
4. Professor Kevin Desouza (QUT Business School/Centre for Data Science, Professor of Business, Technology and Strategy) – AI & Public Agencies: A Maturity Model
5. Dr. S. Kate Devitt (Trusted Autonomous Systems (TAS-D) CRC, Chief Scientist) – Australia’s AI Governance Frameworks & Pragmatic Tools Manage Ethical Risk
6. Professor Dan Hunter (ARC Centre of Excellence for Automated Decision-Making + Society (ADM+), Chief Investigator; QUT, Executive Dean of the Faculty of Law) – Fairness, Accountability and Transparency (FATML) in Machine Learning and Mr. Aaron Snoswell (ACEMS UQ, PhD to be conferred; Research Fellow in Computational Law at ARC ADM+S) – Overcoming AI Opacity in Civil Litigation
7. Dr. Tristan Perez (Boeing Research Australia, Principal Systems Engineer – Mission Systems & Autonomy) – Data-driven Verification of AI – Fundamental Limitations & Feasible Solutions
- Distinguished Professor Kerrie Mengersen
- Professor David Lovell
- Associate Professor Helen Thompson
- Dr Gentry White
- Professor Kevin Desouza
- Jamie Hogg
- Shadi Jaradat
- Dr Robert Andrews
- Stephen Vu
- Deepak Uniyal
- Nayomi Dulanjala Sewwandi