@article{van_toorn_government_2025,

title = {Government using machine learning to help create draft plans for NDIS participants, documents reveal},

url = {https://www.theguardian.com/australia-news/2025/nov/12/government-using-machine-learning-to-help-create-draft-plans-for-ndis-participants-documents-reveal},

issn = {0261-3077},

year  = {2025},

date = {2025-11-01},

urldate = {2025-12-04},

journal = {The Guardian},

chapter = {Australia news},

abstract = {Exclusive: NDIA defines machine learning as a subset of AI that uses algorithms to learn from data and make decisions or predictions},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{phan_testing\textemdashwild_2025,

title = {Testing-in-the-wild: Innovation nationalism and the colonial dynamics of new technology testbeds},

author = {Thao Phan},

url = {https://journals.sagepub.com/doi/10.1177/29768640251392332},

doi = {10.1177/29768640251392332},

issn = {2976-8640, 2976-8640},

year  = {2025},

date = {2025-11-01},

urldate = {2025-12-04},

journal = {Dialogues on Digital Society},

pages = {29768640251392332},

abstract = {This article examines the phenomenon of the AI testbed and practices of testing-in-the-wild. It combines historical and sociological approaches to understand how the settler-colony of Australia has come to be treated as an ideal test site, using commercial drone delivery company Wing Aviation as a case study. It connects the figuration of Australia as contemporary testbed with histories of the nation as a colonial experiment. I argue that this historical frame has been consistently deployed to justify the treatment of lands and peoples as experimental subjects across a range of domains\textemdashfrom medical science, penal management, and military operations. In doing so, I show how Australia has been treated as a test site and Australians as test subjects based on changing imaginaries of the nation and its people\textemdashfrom proxies for whiteness and Empire in the colonial period, to multiculturalism and ethnic diversity in the contemporary era.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{thomson_computer-mediated_2025,

title = {Computer-mediated representations: a qualitative examination of algorithmic vision and visual style},

author = {Tj Thomson},

url = {https://journals.sagepub.com/doi/10.1177/14703572251358425},

doi = {10.1177/14703572251358425},

issn = {1470-3572, 1741-3214},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-14},

journal = {Visual Communication},

pages = {14703572251358425},

abstract = {To the general public, text-to-image generators, such as Midjourney and DALL-E, seem to work through magic and, indeed, their inner workings are often frustratingly opaque. This is, in part, due to the lack of transparency from big tech companies around aspects like training data and how the algorithms powering their generators work, on the one hand, and the deep and technical knowledge in computer science and machine learning, on the other, that is required to understand these workings. Acknowledging these aspects, this qualitative examination seeks to better understand the black box of algorithmic vision through asking a large language model to first describe two sets of visually distinct journalistic images. The resulting descriptions are then fed into the same large language model to see how the AI tool remediates these images. In doing so, this study evaluates how machines process images in each set and which specific visual style elements across three dimensions (representational, aesthetic and technical) machine vision regards as important for the description, and which it does not. Taken together, this exploration helps scholars understand more about how computers process, describe and render images, including the attributes that they focus on and tend to ignore when doing so.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{yang_contextualizing_2025,

title = {Contextualizing critical disinformation during the 2023 Voice referendum on WeChat: Manipulating knowledge gaps and whitewashing Indigenous rights},

author = {Fan Yang and Luke Heemsbergen and Robbie Fordyce},

url = {https://misinforeview.hks.harvard.edu/article/contextualizing-critical-disinformation-during-the-2023-voice-referendum-on-wechat-manipulating-knowledge-gaps-and-whitewashing-indigenous-rights/},

doi = {10.37016/mr-2020-185},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-14},

journal = {Harvard Kennedy School Misinformation Review},

abstract = {Outside China, WeChat is a conduit for translating and circulating English-language information among the Chinese diaspora. Australian domestic political campaigns exploit the gaps between platform governance and national media policy, using Chinese-language digital media outlets that publish through WeChat’s “Official Accounts” feature, to reproduce disinformation from English-language sources. These campaigns are situated within local contexts and technological conditions. We show how WeChat content uses emotional-appeal disinformation to capture attention. They rely on familiar but misleading stories to fill knowledge gaps, draw on historical references to ease uncertainty about the future, and downplay or erase Indigenous issues through colonial tropes. These emotionally charged messages are then amplified by WeChat’s algorithm, helping them reach even wider audiences.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kuai_fears_2025,

title = {Fears of online journalism Armageddon as Google changes the game},

url = {https://www.abc.net.au/news/2025-10-08/how-googles-ai-overviews-are-affecting-australian-news-websites/105839588},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-14},

journal = {ABC News},

abstract = {One year on from the rollout of Google AI Overviews in Australia, exclusive data shows steep year-on-year declines in readership for the top news websites as smaller publishers warn of lay-offs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rampisela_relevance-aware_2025,

title = {Relevance-aware Individual Item Fairness Measures for Recommender Systems: Limitations and Usage Guidelines},

author = {Theresia Veronika Rampisela and Maria Maistro and Tuukka Ruotsalo and Falk Scholer and Christina Lioma},

url = {https://dl.acm.org/doi/10.1145/3765624},

doi = {10.1145/3765624},

issn = {2770-6699},

year  = {2025},

date = {2025-09-01},

urldate = {2025-09-24},

journal = {ACM Transactions on Recommender Systems},

pages = {3765624},

abstract = {Recommender Systems (RSs) aim to provide relevant items to users, with a recent emphasis on improving recommendation fairness. Quantifying fairness of the recommended items can be done with two types of evaluation measures: measures that are purely based on item exposure ( 

 exposure-based 

 ) and measures that account for both item exposure and item relevance ( 

 relevance-aware 

 ). While exposure-based measures have been thoroughly analysed, relevance-aware measures have not been examined in such detail yet. We gather all existing relevance-aware individual item fairness measures for RSs and study their theoretical properties. We find that all measures suffer from one or more limitations, which may cause issues in their computation, interpretability, or expressiveness. To address this, we correct the affected measures or explain why a limitation is unresolvable. Further, we empirically investigate the extent of the limitations on the measures and compare the original measures to our reformulations under common and extreme evaluation scenarios across real-world and synthetic data. Our experiments show that our reformulated measures successfully resolve the issues in the original measures. We conclude by providing practical guidelines on how to select measures for a range of use cases.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{luong_conceptuml_2025,

title = {ConceptUML: Multiphase unsupervised threat detection via latent concept learning, Hidden Markov Models and topic modelling},

author = {Khanh Luong and Arash Mahboubi and Geoff Jarrad and Seyit Camtepe and Michael Bewong and Mohammed Bahutair and Hamed Aboutorab and Hang Thanh Bui},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2214212625001978},

doi = {10.1016/j.jisa.2025.104160},

issn = {22142126},

year  = {2025},

date = {2025-09-01},

urldate = {2025-07-21},

journal = {Journal of Information Security and Applications},

volume = {93},

pages = {104160},

keywords = {},

pubstate = {published},

tppubtype = {article}

}