ARC Centre of Excellence for Automated Decision-Making and Society

<p>This commentary reports on gambling advertisements served to Australians on Facebook in 2021-2022 that we discovered through a research project that uses novel data donation infrastructure to improve the observability of platform-based advertising. Preliminary findings show that advertisements for online casinos appear on social media and are served to people tagged as located in Australia despite laws that prohibit both the operation and advertising of these gambling services in Australia, and in apparent contravention of company policies that require gambling advertisers to follow applicable law. We outline the harms of normalizing gambling on digital media and argue that the limited accountability of digital platforms for online advertising can contribute to these harms. We suggest ways in which the law and its enforcement and platform responsibility can be reformed to prevent harmful gambling advertising.</p>

Abstract In recent years, Australia has embarked on a digital transformation of its social services, with the primary goal of creating user‐centric services that are more attentive to the needs of citizens. This article examines operational and technological changes within Australia's National Disability Insurance Scheme (NDIS) as a result of this comprehensive government digital transformation strategy. It discusses the effectiveness of these changes in enhancing outcomes for users of the scheme. Specifically, the focus is on the National Disability Insurance Agency's (NDIA) use of algorithmic decision support systems to aid in the development of personalised support plans. This administrative process, we show, incorporates several automated elements that raise concerns about substantive fairness, accountability, transparency and participation in decision making. The conclusion drawn is that algorithmic systems exercise various forms of state power, but in this case, their subterranean administrative character positions them as “algorithmic grey holes”—spaces effectively beyond recourse to legal remedies and more suited to redress by holistic and systemic accountability reforms advocated by algorithmic justice scholarship.

As oil and gas operators continue to enhance and start implementing their plans to meet their regulators’ and customers’ net-zero related demands, the industry should not overlook those low-hanging fruit that will bear results in the short term and, if done well, also contribute to long-term objectives. An effective approach to energy management will contribute to environmental, operational and financial outcomes, by addressing resource scarcity and price volatility, delivering on licence-to-operate considerations, complying with regulations, and delivering a competitive advantage through greater efficiency and enhanced reputation. Key considerations are as follows. Data quality: fuel, power, steam, condensate balances will highlight data gaps and inconsistencies. Energy usage: inefficiencies in operations such as boilers, turbines, as well as systemic issues, such as cycle efficiency and steam and condensate losses, can be identified. Emissions monitoring and reporting: clear and consistent data about fuel and power consumption directly feeds into greenhouse gas corporate reporting schemes. Quality assurance of monitoring and reporting processes: review and challenge dashboards and reports for completeness, accuracy and clarity. Management operating system: redesign management processes to improve workflows and optimise operations. Loss accounting: Production and Energy Loss Accounting (PELA) improves staff collaboration and effectively tackles hidden losses. In this paper, dss+ will share how, using a mix of operational efficiencies and capex, an integrated oil and gas company active in 30 countries identified potential to reduce its emissions by 38 m tonnes of CO2 leading to over US$6 m in savings per annum over 4 years for just four of its assets.

Background: Monitoring of inhaler use in high-risk children has the potential to reduce asthma attacks and asthma-related deaths. We, therefore, undertook the first UK primary care study to identify high-risk children and young people by searching primary care health records and provided the Hailie® smart inhaler to monitor their asthma medication usage. In this article, we present data from the nested qualitative study, conducted with key stakeholders. Methods: This qualitative interview-based study explored a range of topics relating to the experiences of paediatric asthma care and management, including the use of the Hailie® smart inhaler, from the perspectives of the children, their parents/carers and healthcare professionals. Interview transcripts were generated and thematically analysed. Results: Six parent-child dyads and one parent were interviewed, either online or face-to-face. Additionally, three healthcare professionals (1 Nurse, 1 Pharmacist and 1 Practice Manager) involved in paediatric asthma care and/or management were also interviewed. Two specific themes were identified: Firstly, app-based monitoring was generally viewed positively and was reassuring to parents. Children also appreciated learning about using their inhalers. Secondly, challenges with synching were identified and users had some practical suggestions for improvement. Healthcare professionals also observed that monitoring should not replace clinical support for self-management. Conclusion: Our findings support the acceptability and usefulness of the Hailie® smart inhaler amongst children with high-risk asthma, although some technical difficulties need to be addressed. Further research is needed to assess effectiveness in clinical care management.

Presented on Wednesday 22 May: Session 17 As oil and gas operators continue to enhance and start implementing their plans to meet their regulators’ and customers’ net-zero related demands, the industry should not overlook those low-hanging fruit that will bear results in the short term and, if done well, also contribute to long-term objectives. An effective approach to energy management will contribute to environmental, operational and financial outcomes, by addressing resource scarcity and price volatility, delivering on licence-to-operate considerations, complying with regulations, and delivering a competitive advantage through greater efficiency and enhanced reputation. Key considerations are as follows. Data quality: fuel, power, steam, condensate balances will highlight data gaps and inconsistencies. Energy usage: inefficiencies in operations such as boilers, turbines, as well as systemic issues, such as cycle efficiency and steam and condensate losses, can be identified. Emissions monitoring and reporting: clear and consistent data about fuel and power consumption directly feeds into greenhouse gas corporate reporting schemes. Quality assurance of monitoring and reporting processes: review and challenge dashboards and reports for completeness, accuracy and clarity. Management operating system: redesign management processes to improve workflows and optimise operations. Loss accounting: Production and Energy Loss Accounting (PELA) improves staff collaboration and effectively tackles hidden losses. In this paper, dss+ will share how, using a mix of operational efficiencies and capex, an integrated oil and gas company active in 30 countries identified potential to reduce its emissions by 38 m tonnes of CO2 leading to over US$6 m in savings per annum over 4 years for just four of its assets. To access the Oral Presentation click the link on the right. To read the full paper click here

Foundation models are a new frontier of value creation in the digital platform economy. These technologies rely on the production and consumption of massive datasets that are monetised through consumer facing artificial intelligence (AI) products. However, the unlicensed use of these materials by the AI industry has provoked a legal and conceptual dispute. Media industries claim that the material in datasets is ‘content’ and subject to copyright law. AI industries, alternatively, are working to strategically reframe those materials as ‘data’, which is governed through regimes more congenial to the industry's business models, such as loosely enforced data protection and technologically secured trade secrets. This essay shows how AI copyright litigation, and the central question of data versus content, is mediating between different claims to the right to generate and justify value from datasets, as well as participating in the broader reformation of AI dataset markets.