"Very creepy," was April's first thought when she saw her face on a generative AI website. April is one half of the Maddison twins. She and her sister Amelia make content for OnlyFans, Instagram and other platforms, but they also existed as a custom generative AI model – made without their consent. "It was really weird to see our faces, but not really our faces," she says. Deepfakes – the creation of realistic but false imagery, video and audio using artificial intelligence – is on the political agenda after the federal government announced last week it would introduce legislation to ban the creation and sharing of deepfake pornography as part of measures to combat violence against women.

Hosted by Eleanor Drage and Kerry Mackereth, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, we talk to Dr Hayleigh Bosher, Associate Dean and Reader in intellectual property law at Brunel University and host of the podcast Whose Song is it Anyway?, a podcast on the intersections of intellectual property (IP) and the music industry. She tells us why AI can never create an original song, what it takes to sue a generative AI company for creating music in the style of someone, and why generative AI risks missing the point about what creativity is. Hayleigh is a Reader in Intellectual Property Law and Associate Dean (Professional Development and Graduate Outcomes) at Brunel University London, as well as, Visiting Research Fellow at the Centre for Intellectual Property, Policy and Management, a legal consultant in the creative industries, an advisor for the independent UK charity for professional musicians, Help Musicians, writer and Book Review Editor for the specialist IP blog IPKat. Her work in this area has been cited extensively in academic, practitioner and policy outputs and she is regularly interviewed by numerous national and international media outlets, including the BBC, ITV, Sky News, Channel 5 News and The Guardian, The Times and The Wall Street Journal.

March 31, 2023 - Artificial Intelligence suddenly seems to be everywhere. ChatGPT is writing human-sounding sermons, news updates, and answers to law school exam questions, while Dall·E is generating images ranging from the lifelike to the surreal in response to virtually any prompt. With much less fanfare, AI has already become ubiquitous in myriad ways. AI curates social media feeds and generates purchasing suggestions to fill internet shopping carts. AI saves lives by identifying potential pharmaceutical compounds and by quickly and accurately interpreting medical scans and images.

The vast potential of Artificial Intelligence has hit a bump in the road following the refusal by several countries to patent inventions generated by an AI machine, says Macquarie Law School's Dr Rita Matulionyte. Macquarie Law School's Dr Rita Matulionyte, an international expert in intellectual property law, says the decisions – which have been challenged in overseas courts – signal a need for reform to ensure current law does not stifle innovation in an'immensely promising' sector. "This is the first case where an applicant is trying to patent AI-generated inventions and indicate AI as the inventor, whereas previously, patents granted over such AI inventions mentioned human beings as the inventors," Matulionyte says. The economic contribution of AI is potentially huge: according to Australia's AI roadmap, digital technologies including AI are potentially worth $A315 billion to Australia's economy by 2028, while AI alone could be worth $A22.17 Used by companies such as Coca-Cola and KFC to protect their secret recipes, trade secrets is another type of intellectual property law that could be available to AI inventions, Matulionyte says.

The Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) is pleased to announce their 7th academic seminar as follows. Artificial Intelligence (AI) is nowadays capable of coming up with creative and inventive outputs that until not long ago just human beings were capable to produce. Music, literature and art are already being created by computers and machines. The talk will delve into these burning legal questions and issues, expanding on the challenges AI pose to the authorship and inventorship requirements as well as the legal provisions on originality and inventive step/non obviousness in several jurisdictions, including United States, United Kingdom and the European Union (EU).

In his final, posthumously published book, famed physicist Stephen Hawking raises an alarm about the dangers of artificial intelligence, or AI, and the existential threat it could pose to humanity. In "Brief Answers to the Big Questions," Hawking writes, "a super-intelligent AI will be extremely good at accomplishing goals, and if those goals aren't aligned with ours, we're in trouble." University of Virginia economist Anton Korinek could not agree more, and he believes that the kind of AI that Hawking refers to – "general artificial intelligence" that can equal or surpass human intelligence – could be just a few decades away. "I believe that, by the second half of this century, AI – robots and programs – will be better than us humans at nearly everything," said Korinek, who holds a joint appointment in UVA's Economics Department and the Darden School of Business. "The fundamental question becomes, 'What will happen to humans if we are no longer the most generally intelligent beings on Earth?'" Korinek has written and co-written several published and forthcoming papers on the economic impact of increasing artificial intelligence, including a paper published by the National Bureau of Economic Research and several works in progress.

When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion attacks to show how some attacks are more likely to result in liability than others. We end with a call for action to ML researchers to invest in transparent benchmarks of attacks and defenses; architect ML systems with forensics in mind and finally, think more about adversarial machine learning in the context of civil liberties. The paper is targeted towards ML researchers who have no legal background.

It's easy to conceive of there being challenges for those drafting contracts relating to legal'risk' and liability. For example, what about the healthcare organisation that uses AI to analyse huge volumes of patient data (as well as data from other sources), to understand symptoms and provide suggested treatment options? For example, if something using AI'does what it does' and comes out with a new invention… without any human involvement… surely that would make it the legal'inventor'? But under UK patent law, an inventor is defined as a'person'. So how does this work if the inventor is a computer? Furthermore, as things currently stand, if that person discloses their invention to the state, they are given a 20 year'patent bargain' (a monopoly on that invention).