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.

Read more about phase one of the Australian Search experience and including access to public resources and publications.

• 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.

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.

• 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.

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.

Read more about phase one of the Australian Ad Observatory including access to public resources and publications.

Visit the Australian Ad Observatory public information page to view case studies.

• 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.

• 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.