Tag Archives: #AI

Did you spot the “deliberate” mistake?

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A few days ago, I posted a blog that was largely written by AI. I simply entered a few links, suggested some themes, and added a prompt to “write a blog about AI and fakes” – ChatGPT did the rest.

Overall, ChatGPT does a pretty good job of summarising content, and synthesising logical conclusions based on what it has been fed. However, apart from the known risk of hallucinations, AI tools such as ChatGPT do not give specific citations for any external sources (neither those included in the prompts, nor any data on which their LLMs were trained); and they can leave out important information, but how such omissions are made is far from clear.

As promised, here are some clarifications that ChatGPT did not provide, in reverse order of their appearance in my earlier blog.

First, the “Conclusion” was 100% the work of ChatGPT. I wanted to remain as objective as possible, and did not prompt ChatGPT to come to any specific conclusion, and I did not include any implicit suggestion as to the sentiment to adopt. As such, this section is neutral, objective, balanced and logical. As was the introductory section ChatGPT generated.

For the comments on “AI and Copyright”, I entered the phrase “OpenAI and authors”, with links to articles from ABC News and Reuters. Again, apart from omitting specific citations to the source content, the ChatGPT output is balanced, even though those inputs might be construed as negative towards AI in general and OpenAI in particular. But this raises the question of ChatGPT’s own self-awareness, and whether it “knows” or understands that it is a product of OpenAI?

The section on “Legislative Actions on Deep Fakes” is reasonable, given the only guidance I provided was the phrase “Fake images”, and links to two Guardian articles (here and here). However, one of those articles details a specific legal case, and allegations of criminal activity – quite negative for AI. I doubt if ChatGPT fully understands the principles of sub judice, but maybe it used its discretion or bias to omit the details of this article?

I was reasonably impressed with how ChatGPT compiled the section on “Celebrity Persona Rights”, especially the summary it extracted from the Scientific American article. The phrase “persona rights” is lifted from the html link I supplied, and the only specific prompt I gave was “Scarlett Johansson”. Again, ChatGPT was happy to include potentially negative references to OpenAI. However, ChatGPT did not directly engage with an extract I had used from the source article, which provides more context:

“Most discussions about copyright and AI focus […] on whether and how copyrighted material can be used to train the technology, and whether new material that it produces can be copyrighted.”

But, you could argue that ChatGPT made an equivalent reference in the section on “AI and Copyright”.

More significantly, the original article (and dispute) is about AI-generated voice similarity, whereas ChatGPT refers to “likeness”, which I would usually interpret as visual similarity.

The references to “Mozilla’s Campaign Against Misinformation” and “AI in Indian Elections” are both much weaker by comparison. I used the prompt “Mozilla”, and links to the specific WhatsApp campaign, as well as a list of other Mozilla campaigns. I also used “Indian election” as a specific prompt, as well as a relevant news article. First, the Mozilla campaigns include one about AI transparency, which ChatGPT does not address here, or in the section on copyright – perhaps it decided it was too critical of OpenAI et al? Second, the ABC article includes mention of a deep fake video of a deceased Indian politician – which I would have thought merited a mention by ChatGPT.

Finally, the section on “Dylan and Rodin: A Fabricated Encounter” is probably the most problematic. I used the prompt, “Dylan and Rodin….”, and links to two recent articles by Dave Haslam – one that discusses an ongoing fake narrative about “Bob Dylan photographed playing chess in Paris”, and the other about a fabricated, AI-generated “photograph of Auguste Rodin and Camille Claudel”. (I also included a link to the latter fake, with the prompt “Image”.) Somehow, ChatGPT has confused and/or incorrectly conflated these two topics, and erroneously concluded that this was a reference to a false account of Dylan meeting Rodin in France. ChatGPT simply reproduced the fake photo (which I chose to omit from my published blog this week), and left out any mention of Claudel. I wonder whether this is perhaps because Haslam is not as well indexed in ChatGPT’s database compared to the incorrect/misleading social media posts, or his articles are too critical of AI (and those that replicate its errors and perpetuate its myths), and too nuanced in their arguments. And was the failure to mention Claudel an oversight, or something more insidious?

I know that ChatGPT and other AI tools are trying to protect themselves with caveat emptor-style disclaimers, and no-one should rely on any AI output unless they are confident of the results (or they are indifferent/negligent as to the potential for harm or mischief), but the Dylan/Rodin example illustrates the inherent risks we still face as end users.

AI and Deep (and not so deep…) Fakes

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The New York Times recently posted a quiz – “can you tell the difference between a photograph, and an image created by AI?”

Of the quiz examples, a mix of actual photos and AI-generated content, I was only able to correctly identify 8/10 as to which were which. My significant other claimed to have scored 10/10! In my defence, I correctly identified all of the AI images, but I mistook two authentic photos as being fakes. Of the latter, one featured a bunch of famous people, most of whom I did not recognise, and the photo had been significantly cropped, removing much of the visual context (I also suspect it had been subject to some additional photo-shopping, given it was a publicity shot). The other real photo had been taken at such an unusual angle that it distorted some of the natural perspective, so as to make some elements look wonky. (But maybe I’ve become more cynical or sceptical, and therefore I tend to disbelieve more of what I see, the more I know I am being exposed to AI-generated content?)

How can we remain alert to AI deceptions, while at the same time recognizing and embracing the potential that this amazing technology has to offer?

Taking my lead from the New York Times article, the following blog has been created using ChatGPT. All I did was enter some brief subject headings (including the title), a series of internet links, and a prompt to turn it into a blog on AI and fakes. Can you tell what is true, what is hallucination, and what important information has been left out?

AI and Deep (and not so deep…) Fakes

Artificial Intelligence (AI) has revolutionized numerous sectors, bringing forth innovations that were once unimaginable. However, one of the more controversial applications of AI is in the creation of deep fakes. These are hyper-realistic digital falsifications created using AI, capable of mimicking real people’s appearances and voices. This technology has sparked debates over ethics, security, and the future of digital media. Let’s delve into some recent examples and the implications they bring.

Dylan and Rodin: A Fabricated Encounter

A striking example of AI’s capacity to blur reality is the supposed encounter between Bob Dylan and the sculptor Auguste Rodin. In an article on Substack by Dave Haslam, the scenario of Dylan visiting Rodin’s Parisian studio is meticulously crafted, yet it’s a fabrication made plausible by AI tools. This fake story exemplifies how AI can create convincingly realistic yet entirely fictional narratives, challenging our ability to discern truth from fiction.

AI in Indian Elections

AI’s role extends beyond entertainment into more critical areas such as political campaigns. In the 2024 Indian elections, AI-generated deep fakes have been used to spread misinformation and manipulate public opinion. An article by ABC News details how AI is disrupting electoral processes, raising concerns about the integrity of democratic systems.

Mozilla’s Campaign Against Misinformation

In response to the growing threat of AI-generated misinformation, organizations like Mozilla have launched campaigns to protect the integrity of elections. Mozilla’s efforts aim to hold platforms like WhatsApp accountable for the spread of fake news and deep fakes, urging them to implement stricter controls to safeguard democratic processes.

Celebrity Persona Rights

AI’s ability to replicate real people has also sparked legal disputes, particularly concerning persona rights. Scarlett Johansson’s recent dispute with OpenAI highlights these issues. Johansson’s concerns about her likeness being used without consent underscore the broader debate about the ethical use of AI. As noted by Scientific American, the conflict brings to light questions about how AI-generated content can respect individual rights and privacy.

Legislative Actions on Deep Fakes

Governments are beginning to take action against the misuse of AI-generated content. In Australia, new laws are being proposed to combat the creation and distribution of deep fake pornography and other harmful images. The Guardian reports on legislative efforts to protect individuals from the malicious use of AI, reflecting a growing recognition of the need for regulation.

AI and Copyright

The intersection of AI and copyright law is another area of intense debate. A lawsuit involving famous authors against OpenAI raises questions about how AI uses copyrighted material for training and whether the content it generates can itself be copyrighted. This legal battle, reported by ABC News and Reuters, could set important precedents for how intellectual property laws adapt to the age of AI.

Conclusion

As AI technology continues to advance, its applications in creating deep fakes present both opportunities and challenges. While it can be used creatively and constructively, it also poses significant risks to privacy, security, and the authenticity of information. The ongoing developments in legislation, ethical guidelines, and technological safeguards will be crucial in shaping the future landscape of AI-generated content.

So, did you spot the “deliberate” error(s)? And what information was missing? Answers will be posted later this week.

Next week: AI & Music

AI hallucinations and the law

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Several years ago, I blogged about the role of technology within the legal profession. One development I noted was the nascent use of AI to help test the merits of a case before it goes to trial, and to assess the likelihood of winning. Not only might this prevent potentially frivolous matters coming to trial, it would also reduce court time and legal costs.

More recently, there has been some caution (if not out and out scepticism) about the efficacy of using AI in support of legal research and case preparation. This current debate has been triggered by an academic paper from Stanford University that compared leading legal research tools (that claim to have been “enhanced” by AI) and ChatGPT. The results were sobering, with a staggering number of apparent “hallucinations” being generated, even by the specialist legal research tools. AI hallucinations are not unique to legal research tools; nor to the AI tools and the Large Language Model (LLMs) they are trained on, as Stanford has previously reported. While the academic paper is awaiting formal publication, there has been some to-and-fro between the research authors and at least one of the named legal tools. This latter rebuttal rightly points out that any AI tool (especially a legal research and professional practice platform) has to be fit for purpose, and trained on appropriate data.

Aside from the Stanford research, some lawyers have been found to have relied upon AI tools such as ChatGPT and Google Bard to draft their submissions, only to discover that the results have cited non-existent precedents and cases – including in at least one high-profile prosecution. The latest research suggests that not only do AI tools “imagine” fictitious case reports, they can also fail to spot “bad” law (e.g., cases that have been overturned, or laws that have been repealed), offer inappropriate advice, or provide inaccurate or incorrect legal interpretation.

What if AI hallucinations resulted in the generation of invidious content about a living person – which in many circumstances, would be deemed libel or slander? If a series of AI prompts give rise to libelous content, who would be held responsible? Can AI itself be sued for libel? (Of course, under common law, it is impossible to libel the dead, as only a living person can sue for libel.)

I found an interesting discussion of this topic here, which concludes that while AI tools such as ChatGPT may appear to have some degree of autonomy (depending on their programming and training), they certainly don’t have true agency and their output in itself cannot be regarded in the same way as other forms of speech or text when it comes to legal liabilities or protections. The article identified three groups of actors who might be deemed responsible for AI results: AI software developers (companies like OpenAI), content hosts (such as search engines), and publishers (authors, journalists, news networks). It concluded that of the three, publishers, authors and journalists face the most responsibility and accountability for their content, even if they claimed “AI said this was true”.

Interestingly, the above discussion referenced news from early 2023, that a mayor in Australia was planning to sue OpenAI (the owners of ChatGPT) for defamation unless they corrected the record about false claims made about him. Thankfully, OpenAI appear to have heeded of the letter of concern, and the mayor has since dropped his case (or, the false claim was simply over-written by a subsequent version of ChatGPT). However, the original Reuters link, above, which I sourced for this blog makes no mention of the subsequent discontinuation, either as a footnote or update – which just goes to show how complex it is to correct the record, since the reference to his initial claim is still valid (it happened), even though it did not proceed (he chose not to pursue it). Even actual criminal convictions can be deemed “spent” after a given period of time, such that they no longer appear on an individual’s criminal record. Whereas, someone found not guilty of a crime (or in the mayor’s case, falsely labelled with a conviction) cannot guarantee that references to the alleged events will be expunged from the internet, even with the evolution of the “right to be forgotten“.

Perhaps we’ll need to train AI tools to retrospectively correct or delete any false information about us; although conversely, AI is accelerating the proliferation of fake content – benign, humourous or malicious – thus setting the scene for the next blog in this series.

Next week: AI and Deep (and not so deep…) Fakes

 

 

 

 

AI and the Human Factor

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Earlier this month, I went to the Melbourne premiere of “Eno”, a documentary by Gary Hustwit, which is described as the world’s first generative feature film. Each time the film is shown, the choice and sequencing of scenes is different – no two versions are ever the same. Some content may never be screened at all.

I’ll leave readers to explore the director’s rationale for this approach (and the implications for film-making, cinema and streaming). But during a Q&A following the screening, Hustwit was at pains to explain that this is NOT a film generated by AI. He was also guarded and refrained from revealing too much about the proprietary software and hardware system he co-developed to compile and present the film.

However, the director did want to stress that he didn’t simply tell an AI bot to scour the internet, scrape any content by, about or featuring Brian Eno, and then assemble it into a compilation of clips. This documentary is presented according to a series of rules-based algorithms, and is a content-led venture curated by its creator. Yes, he had to review hours and hours of archive footage from which to draw key themes, but he also had to shoot new interview footage of Eno, that would help to frame the context and support the narrative, while avoiding a banal biopic or series of talking heads. The result is a skillful balance between linear story telling, intriguing juxtaposition, traditional interviews, critical analysis, and deep exploration of the subject. The point is, for all its powerful capabilities, AI could not have created this film. It needed to start with human elements: innate curiosity on the part of the director; intelligent and empathetic interaction between film maker and subject; and expert judgement in editing the content – as a well as an element of risk-taking in allowing the algorithm to make the final choices when it comes to each screened version.

That the subject of this documentary is Eno should not be surprising, either. He has a reputation for being a modern polymath, interested in science and technology as well as art. His use of Oblique Strategies in his creative work, his fascination with systems, his development of generative music, and his adoption of technology all point to someone who resists categorisation, and for whom work is play (and vice versa). In fact, imagination and play are the two key activities that define what it is to be human, as Eno explored in an essay for the BBC a few years ago. Again, AI does not yet have the power of imagination (and probably has no sense of play).

Sure, AI can conjure up all sorts of text, images, video, sound, music and other outputs. But in truth, it can only regurgitate what it has been trained on, even when extrapolating from data with which it has been supplied, and the human prompts it is given. This process of creation is more akin to plagiarism – taking source materials created by other people, blending and configuring them into some sort of “new” artefact, and passing the results off as the AI’s own work.

Plagiarism is neither new, nor is it exclusive to AI, of course. In fact, it’s a very natural human response to our environment: we all copy and transform images and sounds around us, as a form of tribute, hommage, mimicry, creative engagement, pastiche, parody, satire, criticism, acknowledgement or denouncement. Leaving aside issues of attribution, permitted use, fair comment, IP rights, (mis)appropriation and deep fakes, some would argue that it is inevitable (and even a duty) for artists and creatives to “steal” ideas from their sources of inspiration. Notably, Robert Shore in his book about “originality”. The music industry is especially adept at all forms of “copying” – sampling, interpolation, remixes, mash-ups, cover versions – something that AI has been capable of for many years. See for example this (limited) app from Google released a few years ago. Whether the results could be regarded as the works of J.S.Bach or the creation of Google’s algorithm trained on Bach’s music would be a question for Bach scholars, musicologists, IP lawyers and software analysts.

Finally, for the last word on AI and the human condition, I refer you to the closing scene from John Carpenter’s cult SciFi film, “Dark Star”, where an “intelligent” bomb outsmarts its human interlocutor. Enjoy!

Next week: AI hallucinations and the law