Tag: AI

AI-Generated Spam May Soon Be Flooding Your Inbox – It Will Be Personalized to Be Especially Persuasive

Each day, messages from Nigerian princes, peddlers of wonder drugs and promoters of can’t-miss investments choke email inboxes. Improvements to spam filters only seem to inspire new techniques to break through the protections.

Now, the arms race between spam blockers and spam senders is about to escalate with the emergence of a new weapon: generative artificial intelligence. With recent advances in AI made famous by ChatGPT, spammers could have new tools to evade filters, grab people’s attention and convince them to click, buy or give up personal information.

This article was republished with permission from The Conversation, a news site dedicated to sharing ideas from academic experts. It represents the research-based findings and thoughts of John Licato, Assistant Professor of Computer Science and Director of AMHR Lab, University of South Florida.

As director of the Advancing Human and Machine Reasoning lab at the University of South Florida, I research the intersection of artificial intelligence, natural language processing and human reasoning. I have studied how AI can learn the individual preferences, beliefs and personality quirks of people.

This can be used to better understand how to interact with people, help them learn or provide them with helpful suggestions. But this also means you should brace for smarter spam that knows your weak spots – and can use them against you.

Spam, Spam, Spam

So, what is spam?

Spam is defined as unsolicited commercial emails sent by an unknown entity. The term is sometimes extended to text messages, direct messages on social media and fake reviews on products. Spammers want to nudge you toward action: buying something, clicking on phishing links, installing malware or changing views.

Spam is profitable. One email blast can make US$1,000 in only a few hours, costing spammers only a few dollars – excluding initial setup. An online pharmaceutical spam campaign might generate around $7,000 per day.

Legitimate advertisers also want to nudge you to action – buying their products, taking their surveys, signing up for newsletters – but whereas a marketer email may link to an established company website and contain an unsubscribe option in accordance with federal regulations, a spam email may not.

Spammers also lack access to mailing lists that users signed up for. Instead, spammers utilize counter-intuitive strategies such as the “Nigerian prince” scam, in which a Nigerian prince claims to need your help to unlock an absurd amount of money, promising to reward you nicely. Savvy digital natives immediately dismiss such pleas, but the absurdity of the request may actually select for naïveté or advanced age, filtering for those most likely to fall for the scams.

Advances in AI, however, mean spammers might not have to rely on such hit-or-miss approaches. AI could allow them to target individuals and make their messages more persuasive based on easily accessible information, such as social media posts.

Future of Spam

Chances are you’ve heard about the advances in generative large language models like ChatGPT. The task these generative LLMs perform is deceptively simple: given a text sequence, predict which token – think of this as a part of a word – comes next. Then, predict which token comes after that. And so on, over and over.

Somehow, training on that task alone, when done with enough text on a large enough LLM, seems to be enough to imbue these models with the ability to perform surprisingly well on a lot of other tasks.

Multiple ways to use the technology have already emerged, showcasing the technology’s ability to quickly adapt to, and learn about, individuals. For example, LLMs can write full emails in your writing style, given only a few examples of how you write. And there’s the classic example – now over a decade old – of Target figuring out a customer was pregnant before her father knew.

Spammers and marketers alike would benefit from being able to predict more about individuals with less data. Given your LinkedIn page, a few posts and a profile image or two, LLM-armed spammers might make reasonably accurate guesses about your political leanings, marital status or life priorities.

Our research showed that LLMs could be used to predict which word an individual will say next with a degree of accuracy far surpassing other AI approaches, in a word-generation task called the semantic fluency task. We also showed that LLMs can take certain types of questions from tests of reasoning abilities and predict how people will respond to that question. This suggests that LLMs already have some knowledge of what typical human reasoning ability looks like.

If spammers make it past initial filters and get you to read an email, click a link or even engage in conversation, their ability to apply customized persuasion increases dramatically. Here again, LLMs can change the game. Early results suggest that LLMs can be used to argue persuasively on topics ranging from politics to public health policy.

Good for the Gander

AI, however, doesn’t favor one side or the other. Spam filters also should benefit from advances in AI, allowing them to erect new barriers to unwanted emails.

Spammers often try to trick filters with special characters, misspelled words or hidden text, relying on the human propensity to forgive small text anomalies – for example, “c1îck h.ere n0w.” But as AI gets better at understanding spam messages, filters could get better at identifying and blocking unwanted spam – and maybe even letting through wanted spam, such as marketing email you’ve explicitly signed up for. Imagine a filter that predicts whether you’d want to read an email before you even read it.

Despite growing concerns about AI – as evidenced by Tesla, SpaceX and Twitter CEO Elon Musk, Apple founder Steve Wozniak and other tech leaders calling for a pause in AI development – a lot of good could come from advances in the technology. AI can help us understand how weaknesses in human reasoning might be exploited by bad actors and come up with ways to counter malevolent activities.

All new technologies can result in both wonder and danger. The difference lies in who creates and controls the tools, and how they are used.

AI Has Social Consequences, But Who Pays the Price?

As public concern about the ethical and social implications of artificial intelligence keeps growing, it might seem like it’s time to slow down. But inside tech companies themselves, the sentiment is quite the opposite. As Big Tech’s AI race heats up, it would be an “absolutely fatal error in this moment to worry about things that can be fixed later,” a Microsoft executive wrote in an internal email about generative AI, as The New York Times reported.

In other words, it’s time to “move fast and break things,” to quote Mark Zuckerberg’s old motto. Of course, when you break things, you might have to fix them later – at a cost.

In software development, the term “technical debt” refers to the implied cost of making future fixes as a consequence of choosing faster, less careful solutions now. Rushing to market can mean releasing software that isn’t ready, knowing that once it does hit the market, you’ll find out what the bugs are and can hopefully fix them then.

However, negative news stories about generative AI tend not to be about these kinds of bugs. Instead, much of the concern is about AI systems amplifying harmful biases and stereotypes and students using AI deceptively. We hear about privacy concerns, people being fooled by misinformation, labor exploitation and fears about how quickly human jobs may be replaced, to name a few. These problems are not software glitches. Realizing that a technology reinforces oppression or bias is very different from learning that a button on a website doesn’t work.

This article was republished with permission from The Conversation, a news site dedicated to sharing ideas from academic experts. It represents the research-based findings and thoughts of Casey Fiesler, Associate Professor of Information Science, University of Colorado Boulder.

As a technology ethics educator and researcher, I have thought a lot about these kinds of “bugs.” What’s accruing here is not just technical debt, but ethical debt. Just as technical debt can result from limited testing during the development process, ethical debt results from not considering possible negative consequences or societal harms. And with ethical debt in particular, the people who incur it are rarely the people who pay for it in the end.

Off to the Races

As soon as OpenAI’s ChatGPT was released in November 2022, the starter pistol for today’s AI race, I imagined the debt ledger starting to fill.

Within months, Google and Microsoft released their own generative AI programs, which seemed rushed to market in an effort to keep up. Google’s stock prices fell when its chatbot Bard confidently supplied a wrong answer during the company’s own demo. One might expect Microsoft to be particularly cautious when it comes to chatbots, considering Tay, its Twitter-based bot that was almost immediately shut down in 2016 after spouting misogynist and white supremacist talking points. Yet early conversations with the AI-powered Bing left some users unsettled, and it has repeated known misinformation.

When the social debt of these rushed releases comes due, I expect that we will hear mention of unintended or unanticipated consequences. After all, even with ethical guidelines in place, it’s not as if OpenAI, Microsoft or Google can see the future. How can someone know what societal problems might emerge before the technology is even fully developed?

The root of this dilemma is uncertainty, which is a common side effect of many technological revolutions, but magnified in the case of artificial intelligence. After all, part of the point of AI is that its actions are not known in advance. AI may not be designed to produce negative consequences, but it is designed to produce the unforeseen.

However, it is disingenuous to suggest that technologists cannot accurately speculate about what many of these consequences might be. By now, there have been countless examples of how AI can reproduce bias and exacerbate social inequities, but these problems are rarely publicly identified by tech companies themselves. It was external researchers who found racial bias in widely used commercial facial analysis systems, for example, and in a medical risk prediction algorithm that was being applied to around 200 million Americans. Academics and advocacy or research organizations like the Algorithmic Justice League and the Distributed AI Research Institute are doing much of this work: identifying harms after the fact. And this pattern doesn’t seem likely to change if companies keep firing ethicists.

Speculating – Responsibly

I sometimes describe myself as a technology optimist who thinks and prepares like a pessimist. The only way to decrease ethical debt is to take the time to think ahead about things that might go wrong – but this is not something that technologists are necessarily taught to do.

Scientist and iconic science fiction writer Isaac Asimov once said that sci-fi authors “foresee the inevitable, and although problems and catastrophes may be inevitable, solutions are not.” Of course, science fiction writers do not tend to be tasked with developing these solutions – but right now, the technologists developing AI are.

So how can AI designers learn to think more like science fiction writers? One of my current research projects focuses on developing ways to support this process of ethical speculation. I don’t mean designing with far-off robot wars in mind; I mean the ability to consider future consequences at all, including in the very near future.

This is a topic I’ve been exploring in my teaching for some time, encouraging students to think through the ethical implications of sci-fi technology in order to prepare them to do the same with technology they might create. One exercise I developed is called the Black Mirror Writers Room, where students speculate about possible negative consequences of technology like social media algorithms and self-driving cars. Often these discussions are based on patterns from the past or the potential for bad actors.

Ph.D. candidate Shamika Klassen and I evaluated this teaching exercise in a research study and found that there are pedagogical benefits to encouraging computing students to imagine what might go wrong in the future – and then brainstorm about how we might avoid that future in the first place.

However, the purpose isn’t to prepare students for those far-flung futures; it is to teach speculation as a skill that can be applied immediately. This skill is especially important for helping students imagine harm to other people, since technological harms often disproportionately impact marginalized groups that are underrepresented in computing professions. The next steps for my research are to translate these ethical speculation strategies for real-world technology design teams.

Time to Hit Pause?

In March 2023, an open letter with thousands of signatures advocated for pausing training AI systems more powerful than GPT-4. Unchecked, AI development “might eventually outnumber, outsmart, obsolete and replace us,” or even cause a “loss of control of our civilization,” its writers warned.

As critiques of the letter point out, this focus on hypothetical risks ignores actual harms happening today. Nevertheless, I think there is little disagreement among AI ethicists that AI development needs to slow down – that developers throwing up their hands and citing “unintended consequences” is not going to cut it.

We are only a few months into the “AI race” picking up significant speed, and I think it’s already clear that ethical considerations are being left in the dust. But the debt will come due eventually – and history suggests that Big Tech executives and investors may not be the ones paying for it.

Watermarking ChatGPT and Other Generative AIs Could Help Protect Against Fraud and Misinformation

Shortly after rumors leaked of former President Donald Trump’s impending indictment, images purporting to show his arrest appeared online. These images looked like news photos, but they were fake. They were created by a generative artificial intelligence system.

Generative AI, in the form of image generators like DALL-E, Midjourney and Stable Diffusion, and text generators like Bard, ChatGPT, Chinchilla and LLaMA, has exploded in the public sphere. By combining clever machine-learning algorithms with billions of pieces of human-generated content, these systems can do anything from create an eerily realistic image from a caption, synthesize a speech in President Joe Biden’s voice, replace one person’s likeness with another in a video, or write a coherent 800-word op-ed from a title prompt.

This article was republished with permission from The Conversation, a news site dedicated to sharing ideas from academic experts. It represents the research-based findings and thoughts of, Hany Farid, Professor of Computer Science, University of California, Berkeley.

Even in these early days, generative AI is capable of creating highly realistic content. My colleague Sophie Nightingale and I found that the average person is unable to reliably distinguish an image of a real person from an AI-generated person. Although audio and video have not yet fully passed through the uncanny valley – images or models of people that are unsettling because they are close to but not quite realistic – they are likely to soon. When this happens, and it is all but guaranteed to, it will become increasingly easier to distort reality.

In this new world, it will be a snap to generate a video of a CEO saying her company’s profits are down 20%, which could lead to billions in market-share loss, or to generate a video of a world leader threatening military action, which could trigger a geopolitical crisis, or to insert the likeness of anyone into a sexually explicit video.

Advances in generative AI will soon mean that fake but visually convincing content will proliferate online, leading to an even messier information ecosystem. A secondary consequence is that detractors will be able to easily dismiss as fake actual video evidence of everything from police violence and human rights violations to a world leader burning top-secret documents.

As society stares down the barrel of what is almost certainly just the beginning of these advances in generative AI, there are reasonable and technologically feasible interventions that can be used to help mitigate these abuses. As a computer scientist who specializes in image forensics, I believe that a key method is watermarking.

Watermarks

There is a long history of marking documents and other items to prove their authenticity, indicate ownership and counter counterfeiting. Today, Getty Images, a massive image archive, adds a visible watermark to all digital images in their catalog. This allows customers to freely browse images while protecting Getty’s assets.

Imperceptible digital watermarks are also used for digital rights management. A watermark can be added to a digital image by, for example, tweaking every 10th image pixel so that its color (typically a number in the range 0 to 255) is even-valued. Because this pixel tweaking is so minor, the watermark is imperceptible. And, because this periodic pattern is unlikely to occur naturally, and can easily be verified, it can be used to verify an image’s provenance.

Even medium-resolution images contain millions of pixels, which means that additional information can be embedded into the watermark, including a unique identifier that encodes the generating software and a unique user ID. This same type of imperceptible watermark can be applied to audio and video.

The ideal watermark is one that is imperceptible and also resilient to simple manipulations like cropping, resizing, color adjustment and converting digital formats. Although the pixel color watermark example is not resilient because the color values can be changed, many watermarking strategies have been proposed that are robust – though not impervious – to attempts to remove them.

Watermarking and AI

These watermarks can be baked into the generative AI systems by watermarking all the training data, after which the generated content will contain the same watermark. This baked-in watermark is attractive because it means that generative AI tools can be open-sourced – as the image generator Stable Diffusion is – without concerns that a watermarking process could be removed from the image generator’s software. Stable Diffusion has a watermarking function, but because it’s open source, anyone can simply remove that part of the code.

OpenAI is experimenting with a system to watermark ChatGPT’s creations. Characters in a paragraph cannot, of course, be tweaked like a pixel value, so text watermarking takes on a different form.

Text-based generative AI is based on producing the next most-reasonable word in a sentence. For example, starting with the sentence fragment “an AI system can…,” ChatGPT will predict that the next word should be “learn,” “predict” or “understand.” Associated with each of these words is a probability corresponding to the likelihood of each word appearing next in the sentence. ChatGPT learned these probabilities from the large body of text it was trained on.

Generated text can be watermarked by secretly tagging a subset of words and then biasing the selection of a word to be a synonymous tagged word. For example, the tagged word “comprehend” can be used instead of “understand.” By periodically biasing word selection in this way, a body of text is watermarked based on a particular distribution of tagged words. This approach won’t work for short tweets but is generally effective with text of 800 or more words depending on the specific watermark details.

Generative AI systems can, and I believe should, watermark all their content, allowing for easier downstream identification and, if necessary, intervention. If the industry won’t do this voluntarily, lawmakers could pass regulation to enforce this rule. Unscrupulous people will, of course, not comply with these standards. But, if the major online gatekeepers – Apple and Google app stores, Amazon, Google, Microsoft cloud services and GitHub – enforce these rules by banning noncompliant software, the harm will be significantly reduced.

Signing Authentic Content

Tackling the problem from the other end, a similar approach could be adopted to authenticate original audiovisual recordings at the point of capture. A specialized camera app could cryptographically sign the recorded content as it’s recorded. There is no way to tamper with this signature without leaving evidence of the attempt. The signature is then stored on a centralized list of trusted signatures.

Although not applicable to text, audiovisual content can then be verified as human-generated. The Coalition for Content Provenance and Authentication (C2PA), a collaborative effort to create a standard for authenticating media, recently released an open specification to support this approach. With major institutions including Adobe, Microsoft, Intel, BBC and many others joining this effort, the C2PA is well positioned to produce effective and widely deployed authentication technology.

The combined signing and watermarking of human-generated and AI-generated content will not prevent all forms of abuse, but it will provide some measure of protection. Any safeguards will have to be continually adapted and refined as adversaries find novel ways to weaponize the latest technologies.

In the same way that society has been fighting a decadeslong battle against other cyber threats like spam, malware and phishing, we should prepare ourselves for an equally protracted battle to defend against various forms of abuse perpetrated using generative AI.