Fairness in Natural Language Processing. Natural language processing (NLP) has achieved spectacular commercial successes in recent years, and has been deployed across an ever-increasing breadth of devices and application areas. At the same time, there has been stark evidence to indicate that naively-trained models amplify biases in training data, and perform inconsistently across text relating to different demographic groupings of individuals. This project aims to systematically quantify the ext ....Fairness in Natural Language Processing. Natural language processing (NLP) has achieved spectacular commercial successes in recent years, and has been deployed across an ever-increasing breadth of devices and application areas. At the same time, there has been stark evidence to indicate that naively-trained models amplify biases in training data, and perform inconsistently across text relating to different demographic groupings of individuals. This project aims to systematically quantify the extent of such biases, and develop models that are both more socially equitable, as well as less prone to expose private data in the learned representations. In doing so, it will make NLP more accessible to new populations of users, and remove socio-technological barriers to NLP uptake.Read moreRead less
Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-speci ....Improving human reasoning with causal Bayes networks: a multimodal approach. This project aims to improve human causal and probabilistic reasoning about complex systems by taking a user-centric, multimodal, interactive approach. The project will explore new integrated visual and verbal ways of explaining a causal probabilistic model and its reasoning, to reduce known human reasoning difficulties, and investigate how to reduce cognitive load by prioritising the most useful user- and context-specific information. Expected outcomes include novel AI methods that empower users to drive the reasoning process and strengthen trust in the system’s reasoning. Performance will be assessed in medical and legal domains, with significant potential benefits to end users from better, more transparent reasoning and decision making.Read moreRead less
Automated assessment of data quality in biological knowledge resources. This project aims to develop methods for identifying poor quality data in biological databases. Research in biomedicine is underpinned by massive databases of biological data. Data quality is largely managed through manual curation, but automated methods to assess quality are critically needed. This project expects to develop a suite of computational tools for assessing biological data quality, utilising an innovative approa ....Automated assessment of data quality in biological knowledge resources. This project aims to develop methods for identifying poor quality data in biological databases. Research in biomedicine is underpinned by massive databases of biological data. Data quality is largely managed through manual curation, but automated methods to assess quality are critically needed. This project expects to develop a suite of computational tools for assessing biological data quality, utilising an innovative approach based on network analysis of database record connectivity. These tools will enable quantifying data quality at scale. Researchers, evidence-based decision-makers in biomedicine, and the analytical or predictive tools that use this data will make more reliable inferences and decisions.Read moreRead less
An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through th ....An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through the utilisation of pioneering natural language processing components and novel custom-made machine-readable knowledge bases. The outcome of this research will broaden access to high-quality models by non-expert workforce and alleviate the shortage of expert mathematicians, bringing significant social and economic benefits.Read moreRead less
Small Scalable Natural Language Models using Explicit Memory. Deep neural networks have had spectacular success in natural language processing, seeing wide-spread deployment as part of automatic assistant devices in homes and cars, and across many valuable industries including finance, medicine and law. Fueling this success is the use of ever larger models, with exponentially increasing training resources, accompanying hardware and energy demands. This project aims to develop more compact models ....Small Scalable Natural Language Models using Explicit Memory. Deep neural networks have had spectacular success in natural language processing, seeing wide-spread deployment as part of automatic assistant devices in homes and cars, and across many valuable industries including finance, medicine and law. Fueling this success is the use of ever larger models, with exponentially increasing training resources, accompanying hardware and energy demands. This project aims to develop more compact models, based on the incorporation of an explicit searchable memory, which will dramatically reduce model size, hardware requirements and energy usage. This will make modern natural language processing more accessible, while also providing greater flexibility, allowing for more adaptable and portable technologies.Read moreRead less
Understanding political debate and policy decisions using big data. This project aims to empirically test a novel framework for analysing the relationship between political debates and policy decisions. Using digital sources and computational modelling approaches, it will investigate three specific issues to test this framework. These issues, all drawn from different policy sectors, will be examined as a series of debates linked to specific decisions, over the last two decades. The expected outc ....Understanding political debate and policy decisions using big data. This project aims to empirically test a novel framework for analysing the relationship between political debates and policy decisions. Using digital sources and computational modelling approaches, it will investigate three specific issues to test this framework. These issues, all drawn from different policy sectors, will be examined as a series of debates linked to specific decisions, over the last two decades. The expected outcomes will provide insights into links between political debates and policy decisions with potential benefits for politics and policy-making.Read moreRead less
Bridging the meaning gap: A computational approach to semantic variation. This project aims to create and validate a new class of large language models that capture and partially explain semantic variation between people. We will (1) measure nuanced differences in word meaning and linguistic experience across individuals; (2) develop computational models that incorporate this variation; and (3) evaluate the extent to which the models capture behavioural and cognitive differences related to polit ....Bridging the meaning gap: A computational approach to semantic variation. This project aims to create and validate a new class of large language models that capture and partially explain semantic variation between people. We will (1) measure nuanced differences in word meaning and linguistic experience across individuals; (2) develop computational models that incorporate this variation; and (3) evaluate the extent to which the models capture behavioural and cognitive differences related to political affiliation, gender, and culture. This will advance our understanding of the nature and origin of individual differences as well as improve the calibration of AI systems for under-represented groups. These advances will support eventual applied outcomes in health, domestic security, and resilience to misinformation. Read moreRead less
Accessible Data Exploration and Analysis for Blind People. This project aims to develop new assistive technologies that will enable blind people to explore and analyse data more readily. The project expects to generate new knowledge in the fields of assistive technology, multimodal interfaces, dialogue systems and natural language understanding and generation. The expected outcome of the project is an innovative conversational agent that uses a mix of speech and tactile graphics to communicate ....Accessible Data Exploration and Analysis for Blind People. This project aims to develop new assistive technologies that will enable blind people to explore and analyse data more readily. The project expects to generate new knowledge in the fields of assistive technology, multimodal interfaces, dialogue systems and natural language understanding and generation. The expected outcome of the project is an innovative conversational agent that uses a mix of speech and tactile graphics to communicate with a blind user and proactively assists with data analysis tasks. This should provide significant benefits, as it will overcome barriers to data analysis and exploration by blind people that currently restrict access to government, health and personal data, and limit employment opportunities.Read moreRead less
Living Together: New Approaches to Multispecies Conflict and Coexistence. Building on the methods and concepts of the emerging environmental humanities, this project will produce a new conceptual vocabulary for a world in which multispecies conflict and coexistence is increasingly important. It brings critical and generative rereadings of classical political thought and contemporary biopolitical and cosmopolitical approaches into dialogue with a set of empirical case studies emerging from novel ....Living Together: New Approaches to Multispecies Conflict and Coexistence. Building on the methods and concepts of the emerging environmental humanities, this project will produce a new conceptual vocabulary for a world in which multispecies conflict and coexistence is increasingly important. It brings critical and generative rereadings of classical political thought and contemporary biopolitical and cosmopolitical approaches into dialogue with a set of empirical case studies emerging from novel encounters between humans and other animals. This project will expand Australia’s knowledge base and research capacity in the interdisciplinary environmental humanities and stake out new approaches to the question of living together in a changing environment.Read moreRead less
Visual evidence: transforming modern sex research (1880s - 1930s). This project aims to explore how photography and film transformed understandings of human sexuality. By analysing how and why doctors and scientists shifted their attention from textual to visual evidence, the project will contribute to understandings about how images have been used historically to create medical norms and communicate scientific knowledge to broad audiences. Focusing on Germany as the international centre of earl ....Visual evidence: transforming modern sex research (1880s - 1930s). This project aims to explore how photography and film transformed understandings of human sexuality. By analysing how and why doctors and scientists shifted their attention from textual to visual evidence, the project will contribute to understandings about how images have been used historically to create medical norms and communicate scientific knowledge to broad audiences. Focusing on Germany as the international centre of early twentieth-century sex research, the project will examine how the turn to visual evidence had a transnational impact by paving the way for post-war researchers such as Kinsey, Masters and Johnson, and for a better understanding of the history of human sexuality in Australia.Read moreRead less