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The increasing use of Artificial Intelligence (AI) technologies in inventive processes raises numerous patent law issues, including whether AI can be an inventor under law and who owns the AI‐generated inventions. The UK Supreme Court decision in Thaler v Comptroller of Patents, Designs and Trademarks has provided an ultimate answer to this question: AI cannot be an inventor for the purposes of patent law. This note argues, first, that while such a human‐centric approach to inventorship might discourage the use and development of AI technologies with autonomous invention capabilities, it will help retain an active human involvement in technologically supported inventive processes and continuously foster human ingenuity. Second, despite the Court focusing on what patent law is and not on what the law should be, the decision will be influential in the ongoing discussions on the future of patent law and will make it more difficult to expand patent law to incorporate non‐human inventors. Third, the decision has opened, or revealed, the gaps in patent law that the emergence of AI technologies have created and for which new legal solutions will be needed, especially with relation to the ownership of AI‐assisted inventions and the validation of inventorship claims. [ABSTRACT FROM AUTHOR]

Law enforcement authorities around the world are increasingly trialing or using facial recognition technologies (FRT). Their use has raised many legal and ethical challenges, one of which is a lack of transparency: Community members do not have sufficient information about what government organizations use FRT, for what purposes, and what safeguards are in place to manage the risks that they pose to human rights. While increased transparency around FRT use has been demanded by policy makers and academics, there are no established guidelines on how much transparency is needed around different FRT (authentication, identification, categorization) and what the barriers are in achieving the expected levels of transparency. This article fills this gap by proposing criteria which would help determine the required levels of transparency for different FRT applications (both low-risk and high-risk ones) and examines organizational, technical, legal and operational barriers in achieving adequate transparency and how they could be addressed. [ABSTRACT FROM AUTHOR]

The introduction of novel medical technology, such as artificial intelligence (AI), into traditional clinical practice presents legal liability challenges that need to be squarely addressed by litigants and courts when something goes wrong. Some of the most promising applications for the use of AI in medicine will lead to vexed liability questions. As AI in health care is in its relative infancy, there is a paucity of case law globally upon which to draw. This article analyses medical malpractice where AI is involved, what problems arise when applying the tort of negligence -- such as establishing the essential elements of breach of duty of care and causation -- and how can these can be addressed. Product liability under Australian Consumer Law is beyond the scope of this article. In order to address this question, the article: (1) identifies the general problems that black box AI causes in the health care sector; (2) identifies the problems that will arise in establishing breach and causation due to the "black box" nature of AI, with reference to the Civil Liability Act 2002 (NSW) and common law through two hypothetical examples; and (3) considers selected legal solutions to the problems caused by "black box" AI. [ABSTRACT FROM AUTHOR]

AI promises to address health services' quality and cost challenges, however, errors and bias in medical devices decisions pose threats to human health and life. This has also led to the lack of trust in AI medical devices among clinicians and patients. The goal of this article is to assess whether AI explainability principle established in numerous ethical AI frameworks can help address these and other challenges posed by AI medical devices. We first define the AI explainability principle, delineate it from the AI transparency principle, and examine which stakeholders in healthcare sector would need AI to be explainable and for what purpose. Second, we analyze whether explainable AI in healthcare is capable of achieving its intended goals. Finally, we examine robust regulatory approval framework as an alternative – and a more suitable – way in addressing challenges caused by black-box AI. [ABSTRACT FROM AUTHOR]

Blockchain offers an abundance of new opportunities for the art industry. Blockchain-based equity crowdfunding, as one such opportunity, could potentially allow the tokenisation of copyright in future artistic projects. Such "copyright tokens" could be sold via crowdfunding platforms to investors in order to attract funding for the artistic project at stake. Although the business model behind Blockchain-based equity crowdfunding is an interesting one indeed, it presents a number of legal issues. This article explores the various means by which such a tokenisation scheme could be achieved from the perspective of current Australian copyright laws, to conclude that there are no straightforward solutions to what is a thus far unexplored application of copyright law. [ABSTRACT FROM AUTHOR]