Language and Cultural Diversity in ADM: Australia in the Asia Pacific

PROJECT SUMMARY

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Language and Cultural Diversity in ADM: Australia in the Asia Pacific

Focus Areas: News & Media, Mobilities, Social Services
Status: Active

This project investigates the challenges and opportunities for cultural and linguistic diversity in automated decision making (ADM) across Australia and the Asia-Pacific region. Focusing upon language and cultural diversity as the central concern, the project aims to better understand the ways in which AI and ADM may be utilised to promote diversity and social cohesion across our region, in addition to identifying the roles of bias and manipulation in ADM.

Moving beyond the dominant paradigms and voices that inform debates about contemporary technologies (e.g. Anglo-centric and superpowers-focused), the Diversity in ADM project focuses on: (1) culturally and linguistically diverse (CALD) communities in Australia, and (2) communities across the Asia-Pacific. It has significance in highlighting both the comparative and connecting perspectives, viewpoints and life experiences, and dialogues in relation to ADM and in increasing creativity and problem-solving capabilities in our multicultural society and workplace.

PROJECT OBJECTIVES

  • Develop a better understand the landscapes of ADM across the region, including the role of NGOs, industry, government and other stakeholders; 
  • Empower community members to participate in dialogues concerning diversity in ADM; and
  • Build capacity for community organisations in collective bargaining with public policymakers for inclusive and equitable ADM policies.

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RESEARCHERS

Haiqing Yu

Prof Haiqing Yu

Project Co-Leader and Chief Investigator,
RMIT University

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ADM+S Chief Investigator Heather Horst

Prof Heather Horst

Project Co-Leader and Chief Investigator,
University of Western Sydney

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Deborah Lupton

Prof Deborah Lupton

Project Co-Leader and Chief Investigator,
UNSW

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ADM+S Chief Investigator Anthony McCosker

Prof Anthony McCosker

Chief Investigator,
Swinburne University
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Sarah Pink

Prof Sarah Pink

Chief Investigator,
Monash University

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Distinguished Professor Julian Thomas

Prof Julian Thomas

Chief Investigator,
RMIT University

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Daniel Featherstone

Dr Daniel Featherstone

Research Fellow,
RMIT University
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Gerard Goggin

Prof Gerard Goggin

Associate Investigator,
University of Western Sydney
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ADM+S Associate Investigator Jenny Kennedy

Assoc Prof Jenny Kennedy

Associate Investigator,
RMIT University
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Dang Nguyen

Dr Dang Nguyen

Research Fellow,
RMIT University

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Dr Damiano Spina

Dr Damiano Spina

Associate Investigator,
RMIT University

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Yong-Bin Kang

Dr Yong-Bin Kang

Research Fellow,
Swinburne University

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Thao Phan

Dr Thao Phan

Research Fellow,
Monash University
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Xiaofang Yao

Dr Xiaofang Yao

Affiliate,
Federation University

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Prof Jason G. Karlin

Research Partner,
Interfaculty Initiative in Information Studies,
University of Tokyo, Japan

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Lee Kwang-Suk

Prof Kwang-Suk Lee

Research Partner,
Seoul National University of Science & Technology,
South Korea

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Cheryll soriano

Prof Cheryll Ruth Soriano

Research Partner,
La Salle Institute of Governance and Social Development Research Center,
De La Salle University, The Philippines

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Prof-Jack-Linchuan-Qiu 4x5small

Prof Jack Qiu

Research Partner,
Asian Communication Research Centre,
Nanyang Technological University, Singapore

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PARTNERS

Digital Asia Hub logo

Digital Asia Hub
(Hong Kong)

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Telstra

Telstra

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COLLABORATORS

Centre for Trusted
Internet and Community

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The Regulatory Project

PROJECT SUMMARY

The Regulatory Project

Focus Areas:  News & Media, Mobilities, Social Services, Health
Status: Active

ADM systems (including AI, foundation models and generative AI) pose ongoing regulatory challenges for Australian governments at every level, and across multiple domains. 

2024-2027 is a critical period for the development of regulation of AI. Worldwide, governments are taking concrete steps to adapt existing laws to technological, social and other changes brought about by expanding uses of AI, and to develop new, risk-based regulatory frameworks.

At the same time others are moving to provide more governance for AI: for example via the development of technical standards and other frameworks. 

The Regulatory Project will contribute to this process, examining fundamental questions that these technologies pose for our regulatory techniques, and engaging research and researchers from across the Centre and the Centre’s partners to inform and respond to regulatory initiatives and quandaries.

PROJECT OBJECTIVES

  • Examine and understand the deployment of ADM systems (including AI), by public and private sector actors and across supply chains, and the effect on fundamental legal concepts, such as natural justice (procedural fairness) as it applies to ADM use by government and firms; responsibility, and accountability, delivering critical new knowledge regarding the changing nature of law and regulation in the AI/ADM space; 
  • Examine and analyse emerging regulatory and governance mechanisms for the development and deployment of AI, including their interaction with socio-technical context, in order to understand what mechanisms are emerging, whether they work, and (if so) how;
  • Translate these understandings across other projects and themes in the centre by collaborating on emerging regulatory implications of research and projects across ADM+S; and
  • Provide a hub for ongoing government and policy engagement and to bring legal and regulatory perspectives to research across the Centre.

PUBLIC RESOURCES

GenAI Concepts

Target audience: Government agencies, industry, researchers, general public

This resource offers technical, operational and regulatory terms and concepts for generative artificial intelligence (GenAI), developed in collaboration with the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S) and the Office of the Victorian Information Commissioner (OVIC).

View website
View PDF guide

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RESEARCHERS

Kim Weatherall

Prof Kimberlee Weatherall

Project Co-Leader and Chief Investigator,
University of Sydney

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ADM+S Investigator Christine Parker

Prof Christine Parker

Project Co-Leader and Chief Investigator,
University of Melbourne

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Jake Goldenfein

Dr Jake Goldenfein

Project Co-Leader and Chief Investigator,
University of Melbourne

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Michael Richardson

Assoc Prof Michael Richardson

Project Co-Leader and Associate Investigator,
UNSW

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ADM+S Chief Investigator Nic Suzor

Prof Nicolas Suzor

Chief Investigator,
QUT

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Zofia Bednarz

Dr Zofia Bednarz

Associate Investigator,
University of Sydney

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Emeritus Professor Terry Carney

Prof Terry Carney

Associate Investigator,
University of Sydney

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Kylie Pappalardo profile picture

Prof Kylie Pappalardo

Associate Investigator,
QUT

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Scarlet Wilcock

Dr Scarlet Wilcock

Associate Investigator,
University of Sydney

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José-Miguel Bello y Villarino

Dr Jose-Miguel Bello y Villarino

Research Fellow,
University of Sydney

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Henry Fraser

Dr Henry Fraser

Research Fellow,
QUT

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Fan Yang

Dr Fan Yang

Research Fellow,
University of Melbourne

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Tegan Cohen

Dr Tegan Cohen

Affiliate,
QUT

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PARTNERS

ABC logo

Australian
Broadcasting
Corporation

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AlgorithmWatch logo

Algorithm Watch
(Germany)

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Consumer Policy Research Centre Logo

Consumer Policy R
esearch Centre

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Cornell Tech logo

Cornell Tech

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OVIC Logo

Victorian Information
Commissioner

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COLLABORATORS

University of Melbourne logo

Centre for Artificial Intelligence
and Digital Ethics (CAIDE)

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CHOICE

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Gradient Institute logo

Gradient Institute

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Generative Authenticity

PROJECT SUMMARY

Generative Authenticity

Focus Areas: News & Media, Mobilities, Social Services, Health
Status: Active

Authenticity is a key problem for understanding and managing the impacts of generative AI and synthetic media in society, and a central target for automated decision-making systems in the information and media environment. From trustworthy news reporting to identity verification for social services and the everyday risk of scams, generative AI and synthetic media present significant real-world implications for practitioners, institutions, and publics in Australia and elsewhere. 

A wide range of technical solutions collectively understood as authenticity infrastructure promise to address these issues; but if adopted and embedded at scale, some of these solutions could have potentially significant downstream effects on stakeholders and implications for society.

This project will critically examine the assumptions underpinning these developments and debates, assess the technical and legal challenges associated with them, and explore novel technical responses that contribute to more responsible, ethical and inclusive ADM systems.

In doing so, the project will draw on the multidisciplinary  expertise of the Centre and our partners explore authenticity as both a socio-technical challenge and as a contested cultural idea. We address these challenges in practical and experimental ways within the innovative and Generative AI Test Range environment. It will also examine what happens after any determination of authenticity, including mechanisms for explaining and communicating determinations and increasing trust in such measures.

PROJECT OBJECTIVES

  • Produce a cross-disciplinary understanding of the problem of authenticity in the context of Generative AI;
  • Study and map the field of Authenticity-as-a-Service (AaaS), providing a detailed account of its infrastructure, operations, and political economy; 
  • Analyse how the integration of authenticity infrastructure is already playing out in practice in specific sectors, and impacting or likely to impact specific communities;
  • Within the Generative AI Test Range environment, simulate and evaluate competing ADM techniques for addressing the problem of authenticity in a range of real-world scenarios; and
  • Build on our findings to develop improved tools and techniques, and produce and share guidelines for explanation and communication for a range of stakeholders and audiences.

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RESEARCHERS

ADM+S Associate Director Jean Burgess

Prof Jean Burgess

Project Co-Leader and Chief Investigator,
QUT

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ADM+S Chief Investigator Christopher Leckie

Prof Christopher Leckie

Project Co-Leader and Chief Investigator,
University of Melbourne

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ADM+S Chief Investigator Anthony McCosker

Prof Anthony McCosker

Project Co-Leader and Chief Investigator,
Swinburne University

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Michael Richardson

Assoc Prof Michael Richardson

Project Co-Leader and Associate Investigator,
UNSW

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Jake Goldenfein

Dr Jake Goldenfein

Chief Investigator,
University of Melbourne

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Prof Flora Salim

Prof Flora Salim

Chief Investigator,
UNSW

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Jeffrey Chan

Assoc Prof Jeffrey Chan

Associate Investigator,
RMIT University

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ADM+S Investigator Sarah Erfani

Dr Sarah Erfani

Associate Investigator,
University of Melbourne

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ADM+S Investigator Timothy Graham

Assoc Prof Timothy Graham

Associate Investigator,
QUT

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Ariadna Matamoros Fernandez profile picture

Dr Ariadna Matamoros-Fernández

Associate Investigator,
QUT

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Dr Aaron Snoswell

Dr Aaron Snoswell

Associate Investigator,
QUT

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ADM+S partner investigator Wiebke Loosen

Prof Wiebke Loosen

Partner Investigator
Hans Bredow Institut

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ADM+S Investigator Julia Stoyanovich

Assoc Prof Julia Stoyanovich

Partner Investigator,
New York University

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Ned Watt

Ned Watt

PhD Student,
QUT

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Daniel Binns

Dr Daniel Binns

Affiliate,
RMIT University

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William He

Will He

Affiliate,
QUT

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Dr Silvia Montaña-Niño profile picture

Dr Silvia Montaña-Niño

Affiliate,
University of Melbourne

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Luke Munn

Dr Luke Munn

Affiliate,
The University of Queensland

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Michelle Riedlinger

Dr Michelle Riedlinger

Affiliate,
QUT

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TJ Thomson

Dr TJ Thomson

Affiliate,
RMIT University

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Kevin Witzenberger

Dr Kevin Witzenberger

Affiliate,
QUT

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PARTNERS

ABC logo

Australian
Broadcasting
Corporation

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Hans Bredow Institut Logo

Hans Bredow
Institut

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GenAISim: Simulation in the Loop for Multi-Stakeholder Interactions with Generative Agents

PROJECT SUMMARY

GenAISim: Simulation in the Loop for Multi-Stakeholder Interactions with Generative Agents

Focus Areas: News & Media, Mobilities, Social Services, Health
Status: Active

Traditional decision-making processes often struggle to adapt to the dynamic and multifaceted nature of the modern world. This research addresses a higher-level profound need for advanced automated decision-making tools that can address complex, context-rich challenges in society.

This project will investigate a hybrid decision-making system, leveraging cooperative knowledge from multiple stakeholders through socio-technical observations, and knowledge priors in Large Language Models (LLMs) and open datasets.

It will develop GenAISim, a novel suite of generative and data driven simulations, useful for depicting current and future urban scenarios, including in mobility, urban policymaking, and health domains. Through a multidisciplinary sociotechnical framework of investigation, this project will establish a new simulation in the loop paradigm.

PROJECT OBJECTIVES

  • Explore LLM agent-based synthetic data generation techniques to simulate and augment human behaviours in diverse contexts;
  • Develop a robust framework for hypothesis testing of real-world observations and relationships, while avoiding spurious correlations;
  • Investigate diverse stakeholder settings, often with nonoverlapping and potentially conflicting objectives, priorities, constraints, incentives and pain points; and
  • Explore questions around hybrid decision making – if an LLM agent is substituting for a decision maker in contexts.

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RESEARCHERS

Prof Flora Salim

Prof Flora Salim

Project Co-Leader and Chief Investigator,
UNSW

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Hao Xue

Dr Hao Xue

Project Co-Leader and Associate Investigator,
UNSW

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Kim Weatherall

Prof Kimberlee Weatherall

Project Co-Leader and Chief Investigator,
University of Sydney

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Sarah Pink

Prof Sarah Pink

Project Co-Leader and Chief Investigator,
Monash University

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Daniel Angus

Prof Daniel Angus

Project Co-Leader and Chief Investigator,
QUT

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Jeffrey Chan

Assoc Prof Jeffrey Chan

Project Co-Leader and Chief Investigator,
RMIT University

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Jake Goldenfein

Dr Jake Goldenfein

Chief Investigator,
University of Melbourne

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ADM+S Chief Investigator Christopher Leckie

Prof Christopher Leckie

Chief Investigator,
University of Melbourne

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ADM+S Investigator Sarah Erfani

Dr Sarah Erfani

Associate Investigator,
University of Melbourne

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Danula Hettiachchi

Dr Danula Hettiachchi

Associate Investigator,
RMIT University

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Debora Lanzeni

Dr Debora Lanzeni

Associate Investigator,
Monash University

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ADM+S Chief Investigator Falk Scholer

Prof Falk Scholer

Associate Investigator,
RMIT University

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Dr Aaron Snoswell

Dr Aaron Snoswell

Associate Investigator,
QUT

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Dr Damiano Spina

Dr Daminao Spina

Associate Investigator,
RMIT University

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Maarten de Rijke

Prof Maarten de Rijke

Partner Investigator,
University of Amsterdam

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ADM+S Partner Investigator Ouri Wolfson

Prof Ouri Wolfson

Partner Investigator,
University of Illinois

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Shohreh Deldari

Dr Shohreh Deldari

Research Fellow,
UNSW

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Tiberio Caetoano

Prof Tiberio Caetano

Affiliate,
Gradient Institute

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PARTNERS

Bendigo Health logo

Bendigo
Hospital

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Halmstad University logo

Halmstad
University

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University of Amsterdam logo

University of
Amsterdam

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COLLABORATORS

Gradient Institute logo

Gradient Institute

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University of Illinois

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Critical Capabilities for Inclusive AI

PROJECT SUMMARY

Critical Capabilities for Inclusive AI

Focus Areas: News & Media, Mobilities, Social Services, Health
Status: Active

Inclusive AI is related to, but distinct from Responsible AI and the ethical principles and governance frameworks currently in development. At base it involves ensuring that all members of society benefit from AI tools and ADM systems and can participate in their design or respond to their deployment. We see capabilities – machine and human – as central to how inclusive AI might be achieved.

While much of the research focus is currently targeting the features, functions and ‘use cases’ of LLMs and other AI model types, not enough emphasis is placed on the ‘human factors’ or the co-learning and socialisation taking place in real-world settings and among different groups using these tools and systems.

This project addresses the knowledge, skills and literacies – the critical capabilities – needed to achieve inclusive AI in Australia. It will work with research partners, consumers and communities to better understand the capabilities and resources needed to access and use AI tools including Generative AI. Central to the project is the AI Capabilities Lab, a platform and space to experiment, observe and evaluate the use of new AI tools with our industry partners and members of the public.

Through the AI capabilities Lab and participatory research methods, the project will build an evidence base about the shifting lines of expertise, knowledge and decision making in organisational and everyday life settings as people begin to use AI tools.

PROJECT OBJECTIVES

  • Develop a model of AI capability and literacy for AI inclusion, tested with key domain areas and target populations;
  • Generate empirical evidence about the way people and organisations are using AI tools, and their potential for alleviating or deepening digital inequalities;
  • Co-design resources with partner organisations and their communities and consumers to enhance inclusive AI literacy and capability and foster responsible forms of ‘social governance’ for AI use and ADM processes; and
  • Develop and test AI capability and usage metrics, test and evaluate AI tools and systems through user studies and explore meta-evaluation approaches for targeted AI tools and applications.

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RESEARCHERS

ADM+S Chief Investigator Anthony McCosker

Prof Anthony McCosker

Project Co-Leader and Chief Investigator,
Swinburne University

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Distinguished Professor Julian Thomas

Prof Julian Thomas

Project Co-Leader and Chief Investigator,
RMIT University

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Kath Albury

Prof Kath Albury

Project Co-Leader and Associate Investigator,
Swinburne University

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ADM+S Associate Director Jean Burgess

Prof Jean Burgess

Chief Investigator,
QUT

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Paul Henman headshot

Prof Paul Henman

Chief Investigator,
The University of Queensland

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Prof Jackie Leach Scully

Chief Investigator,
UNSW

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Deborah Lupton

Prof Deborah Lupton

Chief Investigator,
QUT

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Haiqing Yu

Prof Haiqing Yu

Chief Investigator,
RMIT University

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Danula Hettiachchi

Dr Danula Hettiachchi

Associate Investigator,
RMIT University

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James Meese

Assoc Prof James Meese

Associate Investigator,
RMIT University

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ADM+S Associate Investigator Jenny Kennedy

Assoc Prof Jenny Kennedy

Associate Investigator,
RMIT University

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Sharon Parkinson

Assoc Prof Sharon Parkinson

Associate Investigator,
Swinburne University

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Dr Damiano Spina

Dr Daminao Spina

Associate Investigator,
RMIT University

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Rowan Wilken

Assoc Prof Rowan Wilken

Associate Investigator,
RMIT University

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ADM+S Investigator Julia Stoyanovich

Assoc Prof Julia Stoyanovich

Partner Investigator,
New York University

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Daniel Featherstone

Dr Daniel Featherstone

Research Fellow,
RMIT University

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Awais Hameed Khan profile image

Dr Awais Hameed Khan

Research Fellow,
The University of Queensland

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Yong-Bin Kang

Dr Yong-Bin Kang

Research Fellow,
Swinburne University

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Amanda Lawrence

Dr Amanda Lawrence

Affiliate,
RMIT University

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TJ Thomson

Dr TJ Thomson

Affiliate,
RMIT University

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Xiaofang Yao

Dr Xiaofang Yao

Afilliate,
Federation University

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PARTNERS

ABC logo

Australian
Broadcasting
Corporation

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Australian Red Cross Logo

Australian
Red Cross

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Telstra

Telstra

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NYU Ultra Violet Logo

Centre for Responsible AI
New York University

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The Australian Search Experience 2.0

PROJECT SUMMARY

The Australian Search Experience 2.0

Focus Areas: News & Media
Status: Active

The first phase of The Australian Search Experience project launched in late July 2021 and over 12 months collected over 350 million search results from more than 1,000 participants. Results from phase 1 showed that personalisation of results for generic queries in major search engines is minimal, and generally limited to ensuring geographic relevance for users. Yet partisanship is rife. Eight billion queries are issued daily to Google alone, and many partisans use search engines for fact checking, can it really be that this ubiquitous ADM plays no role in the growing partisanship of society? 

The Australian Search Experience 2.0 will address a critically overlooked part of the story. We already know individual searchers formulate their queries very differently, and that this directly affects the results obtained, but understanding the extent and form of query variation as well as its impact is methodologically challenging.

The query is one of the key means people have to control not just search, but with the rise of generative AI, text queries and instructions are becoming the dominant way that ADM systems are controlled. 

When considering the impact of search on society, we know that individual searchers formulate their queries very differently, and that this directly affects the results obtained.

This project will generate significant new knowledge about the breadth and quality of information returned in response to diverse search queries, and offers a significantly more realistic perspective than past research for the impact of variations in users’ queries on the results recommended to them. It will significantly advance the state of the art in the field by developing novel methodologies for the study of search and recommendation in conventional (text-based) and emerging (voice-based and AI assisted) search interfaces, and producing new insights into their impact on users and content creators.

Findings from this research will have both productive and preventative implications: a better understanding of query diversity for a given topic will enable the designers of textbased and non-text-based search interfaces to enhance the ability of such interfaces to produce quality results; similarly, it will also enable educators and non-profit organisations to develop more targeted interventions for improving search literacy and preventing the spread of misinformation.

PROJECT OBJECTIVES

  • Explore novel ways of measuring effectiveness of search engines, by considering a diverse pool of demographic groups that represent the breadth of the Australian population, and observing their approaches to developing search queries;
  • Evaluate the range of search results that such queries return using AI to simulate the full diversity of search queries on controversial topics;
  • Develop an understanding of how non-traditional search interfaces and contextual factors drive query variation; and
  • Determine relationships between queries and search results for specific types of query, public interest topics and key news and information sources such as Wikipedia projects.

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RESEARCHERS

ADM+S Chief Investigator Mark Sanderson

Prof Mark Sanderson

Project Co-Leader and Chief Investigator,
RMIT University

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Axel Bruns

Prof Axel Bruns

Project Co-Leader and Chief Investigator,
QUT

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Daniel Angus

Prof Daniel Angus

Chief Investigator,
QUT

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Danula Hettiachchi

Dr Danula Hettiachchi

Associate Investigator,
RMIT University

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James Meese

Assoc Prof James Meese

Associate Investigator,
RMIT University

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Dr Ariadna Matamoros-Fernández

Associate Investigator,
QUT

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ADM+S Chief Investigator Falk Scholer

Prof Falk Scholer

Associate Investigator,
RMIT University

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Dr Damiano Spina

Dr Damiano Spina

Associate Investigator,
RMIT University

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Johanne Trippas

Dr Johanne Trippas

Associate Investigator,
RMIT University

Maarten de Rijke

Prof Maarten de Rijke

Partner Investigator,
University of Amsterdam

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ADM+S professional staff Abdul Obeid

Dr Abdul Obeid

Data Engineer,
QUT

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Amanda Lawrence

Dr Amanda Lawrence

Affiliate,
RMIT University

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Natali Helberger

Prof Natali Helberger

Research Partner,
University of Amsterdam

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Judith Moller

Prof Judith Möller

Research Partner,
Hans Bredow Institut

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Kim Osman

Dr Kim Osman

Research Partner,
QUT

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PARTNERS

ABC logo

Australian
Broadcasting
Corporation

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AlgorithmWatch logo

AlgorithmWatch

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Hans Bredow Institut Logo

Hans-Bredow-Institut

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University of Amsterdam logo

University of Amsterdam

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Evaluating Automated Cultural Curating and Ranking Systems with Synthetic Data

PROJECT SUMMARY

Evaluating Automated Cultural Curating and Ranking Systems with Synthetic Data

Focus Areas: News & Media
Status: Active

As culture becomes increasingly mediated through automated systems, it is crucial to devise means of evaluating their performance from a societal perspective. Automated systems increasingly navigate a sea of content on our behalf, shaping the flow of culture and cultural products such as news, information retrieval and search, recommender systems and music and video.

This project addresses the question of how to study and evaluate the role that automated systems play in shaping this flow. It brings together technical innovations in the use of synthetic data for hybrid approaches to simulate a broad range of behaviours that influence automated decision-making systems, with a specific focus on ranking-based systems.

It will build tools and approaches to evaluate how recommender and ranking-based systems might incorporate cultural and civic values, such as better representation of artists of colour on streaming platforms or higher visibility and circulation for information of significant public interest.

This project will develop strategies for intervening in the automated flow of culture to advance Centre priorities of responsive, ethical, and inclusive automated decision making.

PROJECT OBJECTIVES

  • Understand what cultural products people are exposed to and in what order and why;
  • Evaluate whether and how ranking-based systems might incorporate factors and values that range from commercial to cultural and civic;
  • Understand how different automated systems create an overall cultural milieu through their combinations and interactions with one another;
  • Provide key insights into how ADMs are experienced in specific sites;
  • Develop ways to ensure that automated intervention in cultural flows aligns with social priorities, including commitments to diversity, fairness, and inclusion; and
  • Demonstrate the appropriate contexts and methods for using synthetic data in evaluating ADM systems.

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RESEARCHERS

Mark Andrejevic

Prof Mark Andrejevic

Project Co-Leader and Chief Investigator,
Monash University

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Jeffrey Chan

Assoc Prof Jeffrey Chan

Project Co-Leader and Associate Investigator,
RMIT University

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Kylie Pappalardo profile picture

Dr Kylie Pappalardo

Project Co-Leader and Associate Investigator,
QUT

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James Meese

Assoc Prof James Meese

Project Co-Leader and Chief Investigator,
RMIT University

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ADM+S Associate Director Jean Burgess

Prof Jean Burgess

Chief Investigator,
QUT

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Prof Flora Salim

Prof Flora Salim

Chief Investigator,
UNSW

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ADM+S Chief Investigator Mark Sanderson

Prof Mark Sanderson

Chief Investigator,
RMIT University
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Distinguished Professor Julian Thomas

Prof Julian Thomas

Chief Investigator,
RMIT University
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Danula Hettiachchi

Dr Danula Hettiachchi

Associate Investigator,
RMIT University
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Joel Stern

Dr Joel Stern

Associate Investigator,
RMIT University
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Patrik Wikstrom

Prof Patrik Wikstrom

Associate Investigator,
QUT

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PARTNERS

ABC logo

Australian
Broadcasting
Corporation

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NYU Ultra Violet Logo

Centre for Responsible AI
New York University

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ADM, Ecosystems and Multispecies Relationships

PROJECT SUMMARY

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ADM, Ecosystems and Multispecies Relationships

Focus Areas: News & Media, Mobilities, Social Services, Health
Status: Active

Automated Decision-Making (ADM) has become increasingly implicated in the relationships between people and other species and ecosystems. From delivery drones to digital bioacoustics, smart farming, smart garbage trucks to conservation and computation, proliferating ADM-enabled technologies are situated within and interact in complex ways with both social and eco-systems to create new mediations between humans, technologies, animals, and environments with diverse and unexpected consequences.

This project will make an innovative and transformational contribution to the advancement of knowledge about the impacts and entanglements of ADM with ecosystems and the capacity of institutions to make responsible decisions about ADM implementations, practices, and assessments.

 Drawing on interdisciplinary socio-technical research practices, researchers will undertake an inclusive approach that brings together diverse knowledges, methods, and sites. In collaboration with partners and communities this project will produce the ADM+Ecosystem Playbook, a policy and practice tool kit that includes addressing the potential for an environmental impact assessment legislative, policy and standards framework for ADM in Australia.

This project will intervene in the ongoing debates about ‘safe and responsible AI’ to critically examine the ecosystem impacts of ADM/AI and prioritise sustainable futures that benefit society and more-than-human ecologies alike.

PROJECT OBJECTIVES

  • To deliver an original account of how entanglements between ADM systems, diverse human stakeholder groups, other non-human species and Australian ecosystems/environments are evolving, with particular attention to Australia’s unique exposure to climate extremes of heat, drought, flood, and fire and demands of automated technologies to cover distance;
  • To generate new experimental and arts practice based methodologies for investigating, representing and creating public and diverse stakeholder engagement with the relationship between humans, other species and ADM systems, including challenges of environmentally responsible ADM; and
  • To produce accessible, practical recommendations for policies and standards that enable industry, government, civil society and advocacy organisations to apply a responsible and sustainable approach to relations between ADM systems, eco systems and other species, with the aim of intervening in the discourse, conception, and implementation of ‘safe and responsible AI’ and the wider public and civil society understanding of ADM and its impacts.

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RESEARCHERS

ADM+S Investigator Christine Parker

Prof Christine Parker

Project Co-Leader and Chief Investigator,
University of Melbourne

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Deborah Lupton

Prof Deborah Lupton

Project Co-Leader and Chief Investigator,
UNSW

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Michael Richardson

Assoc Prof Michael Richardson

Project Co-Leader and Associate Investigator,
UNSW

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Sarah Pink

Prof Sarah Pink

Project Co-Leader and Chief Investigator,
Monash University

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Yolande Strengers

Prof Yolande Strengers

Project Co-leader and
Associate Investigator,
Monash University
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Loup Cellard

Dr Loup Cellard

Affiliate,
Datactivist/Sciences Partner Organisation
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ADM+S Investigator Fiona Haines

Prof Fiona Haines

Affiliate,
University of Melbourne

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Luke Munn

Dr Luke Munn

Affiliate,
University of Queensland
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James Parker

Assoc Prof James Parker

Affiliate,
University of Melbourne

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PARTNERS

Consumer Policy Research Centre Logo

Consumer Policy
Research Centre

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COLLABORATORS

CHOICE

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Australian Ad Observatory: Investigating mobile and dynamic advertising via computational and participatory approaches

PROJECT SUMMARY

Australian Ad Observatory: Investigating mobile and dynamic advertising via computational and participatory approaches

Focus Areas: News & Media
Status: Active

Advertising remains the dominant model for supporting commercial media platforms, and continues to pioneer strategies of data driven customisation and targeting. Advertisers are at the forefront of experimenting with automated digital media across recommendation, targeting, synthetic and augmented content, logistics and retail. Revenue from advertising funds the digital media platforms that in turn invest in engineering automated models that curate, augment and synthesise our media experience. 

Phase one of the Ad Observatory pioneered a way to observe the targeting of social media advertising across populations of users. We generated the largest known collection of targeted ads that people encounter on Facebook in Australia – 328,107 unique ads from 1909 participants –and built world-first research infrastructure that involved citizens in doing so. The project led to significant findings and impact, and new ways of approaching the study of automated advertising, not only in terms of individually targeted, discrete ads, but as ongoing sequences of ads that are ‘tuned’ to work in tandem with people’s identities and daily rhythms.

Responding to significant recent and ongoing developments in automated advertising (including Generative AI), the Phase 2 Ad Observatory will develop approaches for studying contemporary media and information environments, where there are no longer either shared flows of content, nor stable texts.

It will conduct participatory research with diverse groups of Australians to provide visibility into the targeting of harmful products to particular groups. It will explore experiences of advertising and understand its cultural impact, combining citizen science with data collection.

This project will significantly advance our conceptual understanding of automated advertising, playing a crucial role in documenting the emergence of this new form of advertising and enabling industry, civil society and government to respond to the challenges it will create to observability and accountability.

PROJECT OBJECTIVES

  • Develop research infrastructure and methods to observe dark, ephemeral, and automatically generated ad content and sequences, using a prototype Mobile Ad Observatory Toolkit to collect digital ads across platforms;
  • Conduct participatory research with diverse groups of Australians to explore their experiences of advertising and understand its cultural impact, combining citizen science with data collection;
  • Develop tools for automatically identifying defined categories of advertising (such as political ads and ads for harmful products and services);
  • Provide visibility into the targeting of political advertising during the next Federal election in Australia by recruiting undecided voters in marginal and swinging electorates;
  • Develop a detailed account of competing advertising explanatory models: those offered by the platforms, those offered by users, and those informed from our observations;
  • Conceptualise and develop models for simulating automated advertising at individual and cultural levels, creating tools for users to compare ad sequences and question the automated processes behind them;
  • Examine implications of automated advertising, including the use of generative AI, for current and contemplated platform governance policies, legal tools, and regulatory frameworks; and
  • Collaborate with partners to build a public interest network focused on digital advertising, promoting observability, advocacy, and influencing the development of responsible advertising practices.

MORE INFORMATION

  • Develop research infrastructure and methods to observe dark, ephemeral, and automatically generated ad content and sequences, using a prototype Mobile Ad Observatory Toolkit to collect digital ads across platforms;
  • Conduct participatory research with diverse groups of Australians to explore their experiences of advertising and understand its cultural impact, combining citizen science with data collection;
  • Develop tools for automatically identifying defined categories of advertising (such as political ads and ads for harmful products and services);
  • Provide visibility into the targeting of political advertising during the next Federal election in Australia by recruiting undecided voters in marginal and swinging electorates;
  • Develop a detailed account of competing advertising explanatory models: those offered by the platforms, those offered by users, and those informed from our observations;
  • Conceptualise and develop models for simulating automated advertising at individual and cultural levels, creating tools for users to compare ad sequences and question the automated processes behind them;
  • Examine implications of automated advertising, including the use of generative AI, for current and contemplated platform governance policies, legal tools, and regulatory frameworks; and
  • Collaborate with partners to build a public interest network focused on digital advertising, promoting observability, advocacy, and influencing the development of responsible advertising practices.

RESEARCHERS

ADM+S Investigator Christine Parker

Prof Christine Parker

Project Co-Leader and Chief Investigator,
University of Melbourne

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Nicholas Carah

Assoc Prof Nicholas Carah

Project Co-Leader and Associate Investigator,
University of Queensland

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Mark Andrejevic

Prof Mark Andrejevic

Project Co-Leader and Chief Investigator,
Monash University

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Daniel Angus

Prof Daniel Angus

Project Co-Leader and Chief Investigator,
QUT

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ADM+S Associate Director Jean Burgess

Prof Jean Burgess

Project Co-Leader and Chief Investigator,
QUT
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Prof Flora Salim

Prof Flora Salim

Chief Investigator,
UNSW

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Kim Weatherall

Prof Kimberlee Weatherall

Chief Investigator,
University of Sydney
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ADM+S Investigator Timothy Graham

Assoc Prof Timothy Graham

Associate Investigator,
QUT

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ADM+S professional staff Abdul Obeid

Dr Abdul Obeid

Data Engineer,
QUT
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Thao Phan

Dr Thao Phan

Research Fellow,
Monash University
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Maria-Gemma Brown research partner

Maria-Gemma Brown

PhD Student,
The University of Queensland

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Lauren Hayden

Lauren Hayden

PhD Student,
The University of Queensland
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César Albarrán-Torres

Dr César Albarrán-Torres

Affiliate,
Swinburne University

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Robbie Fordyce

Dr Robbie Fordyce

Affiliate,
Monash University

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Giselle Newton

Dr Giselle Newton

Affiliate,
The University of Queensland

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Verity Trott

Dr Verity Trott

Affiliate,
Monash University
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PARTNERS

ABC logo

Australian
Broadcasting
Corporation

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Consumer Policy Research Centre Logo

Consumer Policy
Research Centre

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COLLABORATORS

CHOICE

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FARE Logo

Foundation for Alcohol
Research & Education

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Vic Health Logo

VicHealth

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A taxonomy of decision-making machines

PROJECT SUMMARY

Blurred people walking towards a city building with green trees on the side of the pathway

A taxonomy of decision-making machines

Focus Area(s): News and Media, Health, Social Services, Transport and Mobilities
Research Program: Machines
Status: Completed

The concept of Automated Decision Making (ADM) is relatively uncommon compared to Artificial Intelligence (AI). An important challenge for the Centre and for researchers is to clarify the meaning of ADM and how it relates to and differs from similar concepts.

This project sought to provide conceptual clarity of the this field of concepts. It secondly developed a way in which to conceptualise the various dimensions of ADM systems, providing a taxonomy of ADM. The project engaged with and provides an augmentation of the 2022 OECD Framework for the Classification of AI systems.

The purpose of identifying an ADM taxonomy was to enable more systematic identification and analysis of ADM. Such a systematic approach provides for comparison of ADM systems from different projects.

Based on the formative work of the project, draft definitions and taxonomy were adopted and revised in both the ADM+S Mapping ADM in social services in Australia project and the ADM+S NSW Ombudsman funded project Mapping ADM in NSW state and local governments.

It is anticipated that an ADM+S project report will be published.

PUBLICATIONS

Mapping ADM in Australian social services, 2022

Sleep, L., Coco, B., Henman, P.

Report

RESEARCHERS

ADM+S Chief Investigator Paul Henman

Prof Paul Henman

Lead Investigator,
University of Queensland

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Jake Goldenfein

Dr Jake Goldenfein

Chief Investigator,
University of Melbourne

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ADM+S Chief Investigator Christopher Leckie

Prof Christopher Leckie

Chief Investigator,
University of Melbourne

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ADM+S Chief Investigator Jason Potts

Prof Jason Potts

Chief Investigator,
RMIT University

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ADM+S Investigator Flora Salim

Prof Flora Salim

Chief Investigator,
UNSW

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ADM+S Chief Investigator Mark Sanderson

Prof Mark Sanderson

Chief Investigator,
RMIT University

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Distinguished Professor Julian Thomas

Prof Julian Thomas

Chief Investigator,
RMIT University

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Jeffrey Chan

Dr Jeffrey Chan

Associate Investigator,
RMIT University

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ADM+S Investigator Philip Gillingham

Dr Philip Gillingham

Associate Investigator,
University of Queensland

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Lyndal Sleep profile picture

Dr Lyndal Sleep

Affiliate,
Central Queensland University

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PARTNERS

AlgorithmWatch logo

AlgorithmWatch
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Data & Society
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