Towards a New Law School Curriculum in Australia

Universities are increasingly adopting a neoliberal framework for education—one that is centred on employability, graduate outcomes and skills. Within this framework, teaching, assessments and subjects must adhere to the laws of the market; this prepares students for their roles in private or corporate firms and instils suitable kinds of skills for graduate employment. However, minimal attention is paid to the needs of students as holistic people, citizens, public advocates or members of their local communities, nor is attention paid to their contribution to democratic society. Specifically, subjects in the humanities and social sciences are designated as functionally useless, impractical and irrelevant. The neoliberal style of education has recently come to dominate Australian law schools. Presently, law schools focus heavily on skills, and they avoid deep training in the liberal arts or training students to think for themselves and critique the law they learn. Various academics have addressed this crisis in legal education. They have proposed, on the one hand, a return to a classical and liberal arts style of education in which law is conceptualised in its political, social and economic context. Conversely, they have also proposed a focus on critical theory and critical perspectives of law. The aim of this thesis is to investigate the adoption of a liberal arts approach to legal education through the research and proposal of a new law school curriculum in Australia. This includes proposing new teaching methods, assessments and subjects. This proposed broad liberal arts education in law aims to teach students to think for themselves and to develop their critical and analytical skills, sense of justice and injustice, their ability to comprehend and critique the law and their hard and soft transferable skills that are necessary for the broad range of jobs they will accept after graduation (beyond private and corporate practice). Specifically, this thesis aims to consider how and why law can be taught as part of a wider study of politics, history, civics, psychology and philosophy, and how it can consequently prepare students to become well-rounded citizens in their future jobs.Thesis (Ph.D.) -- University of Adelaide, Law School, 202

The EU legal framework for algorithmic recommender systems: I (don\u2019t) know it when I see it

Abstract: E-commerce providers increasingly rely on recommender systems to curate online consumption. EU law requirements for such recommender systems were initially introduced in privacy legislation, but recent years have seen several new provisions on this topic in consumer protection legislation and platform regulation. However, the latter regulations are limited to online intermediaries and, just like privacy legislation, they merely require businesses that use recommender systems to provide transparency on this to consumers. Transparency is not a solution for the challenges that recommender systems pose because it is difficult to achieve (especially towards consumers) and is not enough to allow users to control recommendations. Furthermore, most of these tools rely on private enforcement, which has proved to be less than effective

Transdisciplinary skills for AI ecosystems : using future visioning to collaboratively unpack skills in UK health and emergency response scenarios

Abstract: Transdisciplinary practices in artificial intelligence (AI) requires good methodological techniques to bring researchers together from diverse disciplines. Different fields and traditions each may have an impact on the greater AI ecosystem, however, to properly gauge this impact, and include diverse voices, research methodologies must evolve to adopt new methods and techniques. To understand the shifting demands for new skills, we conducted a transdisciplinary workshop, bringing together stakeholders from industry, government and academia, by engaging with scenarios in healthcare and emergency response, using future visioning to imagine new skill requirements across job roles. We found that future visioning was a useful methodology to help frame discussions in a manner conducive to eliciting qualitative evidence of different policy approaches. Beyond individual skills, we propose the Human-AI Synergy Matrix as a collaborative competency framework, which integrates technical, professional, and strategic skills with levels of engagement such as awareness, communication, synergy, governance, and transformation. The matrix aims to facilitate evaluation and understanding of skills and critical issues in human-AI collaboration within AI ecosystems, emphasizing the need to address broader challenges in an interconnected manner

Evaluating international AI skills policy: a systematic review of AI skills policy in seven countries

As artificial intelligence (AI) is having an increasingly disruptive impact across industries, companies continue to report having difficulty when recruiting for AI roles, while new graduates find it difficult to find employment, indicating a skills gap or skills misalignment. International approaches to AI skills programmes can offer a guide to future policy development of a skilled workforce, best placed to harness the economic opportunities that AI may support. The authors performed a systematic literature review on AI skills in government policies and documents from seven countries: Australia, Canada, China, Singapore, Sweden, the United Kingom and the United States. We found a divide between countries which emphasised a broader, nationwide approach to upskill and educate all citizens at different levels, namely the United States and Singapore and those countries which emphasised a narrower focus on educating a smaller group of experts with advanced AI knowledge and skills, namely China, Sweden and Canada. We found that the former, broader approaches tended to correlate with higher AI readiness and index scores than the narrower, expert-driven approach. Our findings indicate that, to match world-leading AI readiness, future AI skills policy should follow these broad, nationwide approaches to upskill and educate all citizens at different levels of AI expertise.</p

Coho Salmon are Behaviourally Resilient After 120 Days of Rearing Under Altered Photoperiods and Salinities

Recirculating aquaculture systems (RAS) are a sustainable method used in aquaculture to farm healthy stocks of fish intended for market. In order to optimize fish growth, environmental conditions within RAS, including salinity and photoperiod are manipulated. However, little is known about the effect of varying photoperiod and salinity on behaviour. In this study, Coho salmon (Oncorhynchus kisutch) smolts were reared in RAS for 120 days on either a 12 hour light: 12h dark or 24 hour light photoperiod and in salinities of either 2.5, 5, or 10 ppt. An additional group reared in 30 ppt and 24h light was examined. To determine the impact of photoperiod and salinity on behaviour, three behavioural test assays were employed. Locomotion was quantified using an open field test, the novel object approach test was used to quantify boldness, and the light/dark test was used to quantify anxiety-like behaviour. In the open field test we observed no significant differences in locomotion. In the novel object approach test, the 2.5 ppt (12h/12h) group demonstrated decreased boldness relative to the 10 ppt (24h) and 30 ppt (24h) groups. In the light/dark test, the 2.5 (12h/12h) group spent significantly less time in the dark zone. However, no significant differences were observed between any of the treatment groups, suggesting that anxiety-like behaviour was only minimally impacted. Taken together, the photoperiods and salinities investigated did not significantly affect coho salmon behaviour, which has important implications for RAS aquaculture. Presented in absentia on April 27, 2020 at "Student Research Day" at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Trevor Hamilton Department: Psychology NOTE: This work is available to MacEwan users only at https://roam.macewan.ca/islandora/object/gm:210

AI large language models inquiry: TASHub Response

Policy submission to the Consultation by Communications and Digital Committee, House of Lords, AI Large Language Models Inquiry.<br/

A survey of lay people’s willingness to generate legal advice using Large Language Models (LLMs)

As of November 2022-following the release of OpenAI's ChatGPT-the general public's awareness of generative AI, and specifically Large Language Models (LLMs) has increased. LLMs such as ChatGPT now have the capability to generate text indistinguishable from human authored text, which comes with numerous risks. In this paper, we investigate public perception and willingness to use LLMs as a substitute for legal advice from legal professionals. Our findings show that while few people have used it for this purpose, the willingness to rely on LLMs in the future is growing. Interestingly, this depends on the specific area of law, and while LLMs are perceived to be highly valuable in relation to topics such as tenancy and tax law, they seem to be perceived as less valuable in contexts such as divorce or civil disputes.</p

Objection overruled! Lay people can distinguish large language models from lawyers, but still favour advice from an LLM

Large Language Models (LLMs) are seemingly infiltrating every domain, and the legal context is no exception. In this paper, we present the results of three experiments (total N=288) that investigated lay people's willingness to act upon, and their ability to discriminate between, LLM- and lawyer-generated legal advice. In Experiment 1, participants judged their willingness to act on legal advice when the source of the advice was either known or unknown. When the advice source was unknown, participants indicated that they were significantly more willing to act on the LLM-generated advice. This result was replicated in Experiment 2. Intriguingly, despite participants indicating higher willingness to act on LLM-generated advice in Experiments 1 and 2, participants discriminated between the LLM- and lawyer-generated texts significantly above chance-level in Experiment 3. Lastly, we discuss potential explanations and risks of our findings, limitations and future work, and the importance of language complexity and real-world comparability