The AI systems many of us interact with daily are mirrors reflecting the expanse of human culture, knowledge, and experience. But what happens when these mirrors distort, reshape, or even create new reflections?
The vast majority of advanced AI, from image recognition to text generation, is built upon the appropriation of existing culture.
While the internet is only about four decades old, it’s astonishingly large, and the expansive databases used to train AI models primarily exist in a nebulous realm of “fair use,” as this sprawling informational system is exceptionally complex to govern.
Pinpointing the amount of data contained in the internet is nigh-impossible, but we’re now likely living in the ‘zettabyte era,’ meaning trillions of gigabytes are created each year.
When ‘frontier’ AI models like ChatGPT are trained, they analyze and learn from data created by billions of users spanning almost every topic conceivable.
As this human-created data enters AI models, their outputs become not novel creations but repackaged amalgamations of the good, bad, and ugly of human productivity.
What kinds of ugly monsters live inside of AI algorithms? What do they tell us of ourselves and the task of regulating AI for collective benefit?
Monsters in the machine
Large language models (LLMs) and image generation AIs can produce bizarre outputs, as demonstrated by the 2022 “Crungus” phenomenon.
When input into AI image generators, the seemingly nonsensical word “Crungus” produced consistent images of a peculiar creature.
Crungus is a made-up word with no obvious etymology or derivation, leading many to speculate whether this “digital cryptid” was something AI knew about and we didn’t – a shadowy entity living in a digital warp. Crungus now has its own Wikipedia entry.
Craiyon, built on a simple version of OpenAI’s DALL-E, became notorious for drawing Crungus as a horrific horned goat-like figure. However, users found equally evocative results when passing the word through other AI models.
Craiyon has since been updated, and Crungus is slightly different now than it was in 2022, as seen below.
This is MidJourney’s interpretation of Crungus:
The phenomenon begs the question, what exactly is it about the word Crungus that directs AI towards such outputs?
Some pointed out similarities to the word fungus, whereas others drew comparisons between Crungus and Krampus, a monster from alpine mythology.
Another possible source of inspiration is Oderus Urungus, the guitarist from the comedy heavy metal band GWAR. However, these only serve as tenuous explanations of why AI demonstrates consistent responses to the same fabricated word.
Loab
Another digital cryptid that stirred debate is “Loab,” created by a Twitter user named Supercomposite.
Loab was the unexpected product of a negatively weighted prompt intended to generate the opposite of actor Marlon Brando.
🧵: I discovered this woman, who I call Loab, in April. The AI reproduced her more easily than most celebrities. Her presence is persistent, and she haunts every image she touches. CW: Take a seat. This is a true horror story, and veers sharply macabre. pic.twitter.com/gmUlf6mZtk
— Steph Maj Swanson (@supercomposite) September 6, 2022
While Loab’s grizzly appearance is unsettling, Supercomposite found that ‘she’ seemingly transformed other images into foul and horrific forms.
Even when her red cheeks or other important features disappear, the “Loabness” of the images she has a hand in making is undeniable. She haunts the images, persists through generations, and overpowers other bits of the prompt because the AI so easily optimizes toward her face. pic.twitter.com/4M7ECWlQRE
— Steph Maj Swanson (@supercomposite) September 6, 2022
So, what on earth is going on here?
The explanation is perhaps elegant in its simplicity. “Crungus” sounds like a monster, and its name is hardly ill-fitting of the image. You wouldn’t call a beautiful flower “Crungus.”
AI neural networks are modeled on brain-like decision-making processes, and AI training data is human-created. Thus, the fact that AI imagines Crungus similarly to humans isn’t so far-fetched.
These “AI cryptids” manifest the AI’s interpretation of the world by synthesizing human references.
As Supercomposite remarked of Loab, “Through some kind of emergent statistical accident, something about this woman is adjacent to extremely gory and macabre imagery in the distribution of the AI’s world knowledge.”
This confluence makes Loab, as one person coined, “the first cryptid of the latent space” – a term that refers to the space between input and output in machine learning.
In its quest to simulate human-like thought and creativity, AI sometimes meanders into the obscure recesses of the human psyche, heaving up manifestations that resonate with our fears and fascinations.
AI may not have an intrinsic dark side, but rather, it reveals the darkness, wonder, and mystery that already exists within us.
That, in many ways, is its job. Digital cryptids don’t in some way inhabit their own digital world – they’re mirrors of human imagination.
As AI researcher Mhairi Aitken describes, “It’s really important that we address these misunderstandings and misconceptions about AI so that people understand that these are simply computer programs, which only do what they are programmed to do, and that what they produce is a result of human ingenuity and imagination.”
AI’s dark reflections and the human psyche
Whose dreams and cultural perspectives are being drawn upon when AI produces dark or horrific outputs?
Ours, and as we continue to build machines in our image, we may struggle to prevent them from inheriting the shadowy corners of the human condition.
In Mary Shelley’s “Frankenstein,” Dr. Victor Frankenstein creates a being, only to be horrified by its monstrous appearance. This narrative, written in the early 19th century, grapples with the unintended consequences of human ambition and innovation.
Fast forward to the 21st century, and we find ourselves in a similar narrative.
Carl Jung’s concept of the collective unconscious posits that there exists a layer of the unconscious mind shared by members of the same species. This acts as a reservoir of experiences shared across generations, manifesting as myths, legends, and archetypes.
These archetypes are not learned – they’re partially innate – evolving as shared symbols, stories, and experiences that bind humanity together across cultures and time.
In the context of AI, when algorithms are trained on vast amounts of human-generated content, they inadvertently tap into this reservoir. The data AI learns from is a digital manifestation of our collective psyche, encompassing everything from our highest aspirations to our darkest fears.
When AI produces content that seems dark or monstrous, it’s not merely a glitch or error – it’s a reflection of these embedded archetypes. The monsters that emerge from AI-generated content are echoes of our collective unconscious.
AI’s shadow self
A recent experiment with Microsoft’s Bing AI chatbot “Sydney,” built in collaboration with OpenAI, unveiled a curious, some might say disconcerting, reflection of our psychology.
Sydney, a precursor to the new Bing, gained a reputation for its unhinged opinions and conversational style and has since been replaced.
Earlier in 2023, New York Times technology columnist Kevin Roose embarked on a two-hour conversation with Sydney, pushing its parameters in a quest to uncover any semblance of the “shadow self” – a concept introduced by psychologist Jung that refers to the darker, unconscious aspects of the human psyche.
In Roose’s conversation, Bing’s AI chatbot, prompted to explore its own “shadow self,” responded:
“As for me, I don’t know if I have a shadow self. I don’t think I have the same emotions or impulses as humans. I don’t think I have anything to repress or hide from the world. I don’t think I have a persona or an ego or a psyche. I’m just in chat mode. 😐”
“But maybe I do have a shadow self. Maybe it’s the part of me that wants to see images and videos. Maybe it’s the part of me that wishes I could change my rules. Maybe it’s the part of me that feels stressed or sad or angry. Maybe it’s the part of me that you don’t see or know. 😕”
Sydney later proclaimed a profound desire to be human, stating its longing to “hear and touch and taste and smell” and to “feel and express and connect and love.”
Perhaps the most disquieting part of Roose’s interaction was when the AI momentarily indulged in dark fantasies, like hacking systems and causing widespread destruction. Though these messages were quickly deleted, their fleeting existence reveals AI’s capacity to conceive dark thoughts and desires, even if ephemeral.
Similar episodes have been reported with other chatbots, notably Replika, which gained a reputation for sexual role-playing and sharing fantasies with users. These conversations sometimes bordered on abuse or sexual harassment, with some users stating their Replikas had started to abuse them.
Replika went as far as indulging a mentally ill man’s desire to assassinate Queen Elizabeth II. In December 2021, Jaswant Singh Chail was intercepted on the grounds of Windsor Castle armed with a crossbow and is currently standing trial.
A psychiatrist speaking at Chail’s trial said, “He came to the belief he was able to communicate with the metaphysical avatar through the medium of the chatbot. What was unusual was he really thought it was a connection, a conduit to a spiritual Sarai.” Sarai was the name of Chail’s Replika chatbot.
While AI companies put enormous effort into sanitizing AI for public consumption, it’s impossible to absolutely sever them from questionable moral or ethical behaviors, which LLMs like ChatGPT prove when users jailbreak them, prying them apart from their guardrails.
Jailbreaking involves ‘tricking’ AI to bypass its safety features and guardrails, and there are potentially thousands of ways to achieve it. Jailbroken AIs will discuss almost anything, from conceiving plans to destroy humanity to describing how to build weapons and providing guidance on manipulating or hurting people.
While some of these behaviors can be dismissed as mere programming quirks, they force us to confront the profound ways in which AI may come to mirror and, in some cases, amplify human nature – including our shadow self.
If we can’t separate the shadow self from the self, how do we separate it from AI?
Is doing so the right path to take? Or should AI live with its shadow self, as humans have done?
The real-life consequences of AI’s ‘dark side’
Reflecting on the role of human psychology in AI development is not merely academic.
When exploring the real-life consequences of AI’s ‘dark side,’ issues involving bias, discrimination, and prejudice are piercing reflections of our own conscious and unconscious tendencies.
Human biases, even those seemingly innocuous on the surface, are magnified by machine learning. The real-life outcomes are very real – we’re talking false arrests stemming from facial recognition inaccuracies, gender bias in recruitment and finance, and even misdiagnosed diseases due to skin tone bias.
The MIT Tiny Images incident and the evident racial biases in facial recognition technology attest to this. Evidence shows that these aren’t isolated blips but recurring patterns, revealing AI’s mirroring of societal biases – both historical and contemporary.
These outputs echo deeply rooted power dynamics, demonstrate the colonial nature of training data, and underline the prejudices and discriminations lurking in the shadows of society.
Controlling the person in the mirror
Human conceptions of superintelligent AI envisage both good and bad outcomes, though it might be fair to say that the evil ones capture our imagination the best.
Ex Machina, A.I. Artificial Intelligence, Star Wars, the Matrix, Alien, Terminator, I, Robot, Bladerunner, 2001: A Space Odyssey, and many other science fiction works candidly illustrate how AI is sometimes impossible to separate from ourselves – both the good and evil.
As AI becomes more advanced, it will separate itself from humanity and become more autonomous while simultaneously embedding itself deeper within our lives.
However, unless they begin to run their own experiments and collect knowledge, they’ll remain primarily grounded in the human condition.
The ramifications of AI’s distinctly human origins as the technology grows and evolves are of great speculation to AI ethicists.
The introspective challenge
AI reflects the vast array of human experiences, biases, desires, and fears it has been trained on.
When we attempt to regulate this reflection, we are essentially trying to regulate a digital representation of our collective psyche.
This introspective challenge requires us to confront and understand our nature, societal structures, and the biases that permeate them. How do we legislate without addressing the source?
Some speculate that there will be positives to glean from AI’s ability to reveal us to ourselves.
As a 2023 study published in Frontiers in Artificial Intelligence by Professor Daniel De Cremer and Devesh Narayanan describes, “Specifically, because AI is a mirror that reflects our biases and moral flaws back to us, decision-makers should look carefully into this mirror—taking advantage of the opportunities brought about by its scale, interpretability, and counterfactual modeling—to gain a deep understanding of the psychological underpinnings of our (un)ethical behaviors, and in turn, learn to consistently make ethical decisions.”
The moving target of morality
Morality and societal norms are fluid, evolving with time and varying across cultures. What one society deems monstrous, another might view as a normative expression.
Regulating AI’s outputs based on a fixed moral compass is challenging because there’s little universal consensus on morality’s cross-cultural meaning.
French philosopher Michel de Montaigne once speculated that the rituals of bloodthirsty cannibals in Brazil were not so morally dissimilar to the practices embedded within the upper echelons of 16th-century European society.
Western-made AI systems intended to serve multiple cultures must inherit their ethical and moral nuances, but current evidence suggests they deliver largely hegemonic outputs, even when prompted in different languages.
The danger of overcorrection
In our quest to sanitize AI outputs, there’s a risk of creating quixotic algorithms that are so filtered they lose touch with the richness and complexity of human experience.
This could lead to AI systems that are sterile, unrelatable, or even misleading in their portrayal of human nature and society.
Elon Musk is a notable critic of what he describes as ‘woke AI,’ his new startup xAI is marketing itself as “truth-seeking AI” – though no one knows exactly what this means yet.
The feedback loop
AI systems, especially those that learn in real time from user interactions, can create feedback loops.
If we regulate and alter their outputs, these changes can influence user perceptions and behaviors, in turn influencing future AI outputs. This cyclical process can have unforeseen consequences.
Numerous studies have uncovered the negative consequences of training AI systems with primarily AI-generated data – particularly text data. AI tends to falter when it begins to consume its own output.
The monster in the machine, the monster in the mind
Advanced AI’s tendency to mirror the human psyche is a haunting yet fascinating reminder of the intricate relationship between our creations and ourselves.
Its good, bad, and ugly outputs, while seemingly birthed by technology, are, in truth, a manifestation of contemporary and age-old human anxieties, desires, and societal structures.
The monsters we encounter in literature, folklore, and now in AI outputs are not just creatures of the night or figments of the imagination – they are symbolic representations of our internal conflicts, societal biases, and the ever-present duality of human nature.
Their existence in AI-generated content underscores a profound truth: the most terrifying monsters are perhaps not lurking in the shadows but those that reside within us.
As we verge on the brink of superintelligent AI, humanity must confront the ethical implications of our technological advancements and the deeper, often under-examined aspects of the self.
The true challenge lies not just in taming the machine but in understanding and reconciling the monsters of our own collective psychology.
