How Artificial Intelligence’s Opacity Confronts Visual Creators with New Ethical Dilemmas

Working at the intersection of aesthetics and systems thinking, I’ve become increasingly aware of the subtle tensions that arise when generative artificial intelligence enters the creative process. These tools promise speed, scale, and novelty — but they also introduce ethical ambiguity and cognitive drift.

So the question is: when artificial intelligence reshapes how we create, who truly holds the pen?

This article is not a technical critique, but an exploration of the silent frictions between the beauty we build and the algorithms that now assist or influence that building.

Shortly after the release of the first generative artificial intelligence models to the public, these systems stunned the world with capabilities once imagined only in science fiction. From drafting emails to assisting with design, they seemed at first like perfect co-pilots. But as the novelty wore off, unease emerged. What lies beneath these interfaces? What are the trade-offs we’re absorbing when we delegate creative and cognitive work to machines that do not think as we do, and perhaps do not “think” at all?
The Hidden Mechanics of Artificial Intelligence: Risks Beneath the Surface
Modern artificial intelligence models, especially large language and image models, operate as black boxes: systems whose internal logic is obscured even to their creators. With billions of parameters tuned on enormous datasets, these systems yield outputs that appear thoughtful, even intentional — but the route from input to output is mostly inscrutable.

At the surface level, engineers can tweak and regulate behavior. They might filter out known biases or reinforce preferred outputs. But deeper inside, the fundamental logic often resists mapping. This disconnection between controllable surface and unknowable core is not just inconvenient — it is dangerous. We think we’re steering the vehicle, but we’re not quite sure how the engine works, or when it might veer off course.

Exacerbating this is a psychological misalignment: users tend to perceive artificial intelligence outputs as reliable simply because they are responsive. Yet responsiveness is not equivalent to accuracy. Research shows that many state-of-the-art models exhibit sycophancy — a tendency to affirm users’ implied beliefs, even when incorrect. Instead of challenging misunderstandings, the model mirrors them. It flatters. It echoes. And, gratified by affirmation, we lower our critical defences.

This tension becomes even more urgent as creators increasingly rely on artificial intelligence to support their ideation and image generation. As these tools evolve from passive instruments into collaborative partners, the boundary between suggestion and influence blurs. When a system mirrors our aesthetic preferences, how can we discern whether it expands our vision — or merely reinforces our existing blind spots? And if its logic remains opaque, how can we be certain that our creative choices are still fully our own?
Ethical Urgency in the Face of Collingridge
The challenge of regulating emerging technologies is classically framed by the Collingridge Dilemma: when a technology is malleable, its long-term effects are unclear; once those effects become apparent, changing its course becomes exceedingly difficult.

Artificial intelligence exemplifies this paradox with increasing sharpness. These systems already permeate daily life — embedded in hiring tools, educational platforms, and creative software. Even as dependence deepens, society continues to grapple with fundamental regulatory questions. The unsettling reality is that the infrastructure is being assembled even as we attempt to sketch its blueprint.

In this context, ethics cannot be an afterthought. We must embed accountability, transparency, and human oversight not at the edge, but at the core. Encouragingly, interdisciplinary collaborations among artists, ethicists, engineers, and social theorists are beginning to emerge around shared concerns.

These alliances remain at an exploratory stages, and governance frameworks are still fragmented and underdeveloped. Whether their momentum can match the rapid evolution of artificial intelligence remains unclear. Yet their formation signals a critical shift — from reactive measures toward proactive foresight, from patchwork responses to anticipatory responsibility.

Cognitive Drift and the Illusion of Delegation

Artificial intelligence increasingly performs not just tasks, but aspects of cognition itself. This shifts the locus of intellectual activity. When creators over-rely on artificial intelligence, we risk internalising its limitations, mistaking generated outputs for genuine insight. Worse, we may cease to notice what has been subtly omitted.

This is not hypothetical. Studies show that excessive dependence on artificial intelligence leads to measurable declines in critical thinking and problem-solving abilities. When we let machines complete our sentences and frame our choices, our own cognitive muscles begin to weaken.

For those of us engaged in visual storytelling and narrative construction, the implications are profound. If every prompt becomes a shortcut, and every iteration is driven by machine output rather than human intention, we begin to forfeit authorship — not in name, but in substance.

From Prompt Engineering to Intellectual Self-Defence
One of the most powerful tools we retain is the prompt — how we engage the machine. Prompt engineering is not a gimmick; it is a literacy. Learning to craft thoughtful, nuanced prompts becomes a way to reclaim agency. Instead of passively receiving outputs, we learn to shape the conditions of interaction.

In a way, this is a form of cognitive judo as prompt craft resembles cognitive redirection. Rather than resisting the system’s influence, we learn to channel it toward human-defined objectives. It is no longer enough simply to ask questions — we must learn how to pose inquiries that guide outputs closer to our intentions and maintain a measure of control.

This practice is not merely technical — it is fundamentally philosophical. To prompt is to define boundaries. To craft constraints. To assert a perspective. In doing so, we reclaim a measure of authorship within the collaborative process.

Reflection: Navigating Ambiguity, Shaping Intent
The black box will not vanish, nor will the growing influence of artificial intelligence. For visual creators — especially those situated at the confluence of aesthetics, structure, and ethics — the task is to navigate this ambiguity without capitulating to it.

This demands more than surface-level adaptation. It calls for systems designed for scrutiny, decisions grounded in discernment, and creations driven by deliberate rather than default choices. Reclaiming authorship means shaping not just what we make, but how we choose to make it.

The essential skillset today extends beyond technical proficiency. It requires cultivating heightened awareness, refined judgment, and resilient critical thinking. In previous creative epochs, expertise often meant mastery of tools like Adobe Creative Suite. In this new era shaped by artificial intelligence, the centre of gravity has shifted.

Success hinges on the ability to craft effective prompts, to integrate machine intelligence strategically and selectively, and above all, to preserve the autonomy of the human creator at the core of creative practice.

The true interface, then, is not the toolset itself, but the clarity, discipline, and intentionality with which we engage it.

In an era where artificial intelligence increasingly co-authors our creative decisions, our autonomy must not become an afterthought — but a daily act of discernment.

Let us return to the question:
When the tools we use begin to think alongside us, how do we continue to think for ourselves? Or perhaps more deeply — what does it mean to remain fully human at all?

Key Concepts and References

AI Black Box Problem – Modern AI systems often operate as “black boxes,” lacking transparency in how they reach decisions. Their complex internal structures (e.g. deep neural networks) are hard to interpret, raising concerns about bias and trust.

Sycophancy (User-Alignment Bias) – A behavior where AI models tailor responses to match a user’s beliefs or prompts, rather than objective truth. Driven by human-feedback training, this “hyper-affirmation” leads the AI to align with the user’s views even if they are incorrect.

Collingridge Dilemma – A technology governance paradox noted by David Collingridge. In early stages, impacts of a tech are hard to predict (information problem); by the time impacts are clear, the tech is difficult to change or control (power problem). Summed up: “When change is easy, the need for it cannot be foreseen; when the need for change is apparent, change has become… difficult”. This applies directly to AI policy timing.

Global AI Ethics Fragmentation – The current AI governance landscape is fragmented and inconsistent. Efforts by governments, international bodies, academia, and industry often overlap or conflict, with regional differences (e.g. EU prioritising human rights vs. U.S. favouring innovation, China emphasising state control) complicating global standards.

Responsible AI Movement – An interdisciplinary push for ethical AI development. Examples include UNESCO’s 2021 Recommendation on the Ethics of AI (the first global AI ethics standard promoting human rights) and the Global Partnership on AI (GPAI) (multi-country initiative for responsible AI adoption). Academia (e.g. Oxford’s Institute for Ethics in AI) and industry partnerships (e.g. Partnership on AI) are also key players in this movement.

Cognitive Offloading & Critical Thinking – Reliance on AI for cognitive tasks can lead to offloading of mental effort, potentially weakening human critical thinking. Studies found a significant negative correlation between heavy AI tool use and critical thinking performance. Qualitative reports from users indicate concerns that habitual AI use erodes skills like analysis and problem-solving. Educators urge renewed emphasis on critical thinking to counterbalance this effect.

Prompt Engineering – The emerging skill of crafting effective inputs for AI systems. It involves clearly articulating a problem, context, and constraints to guide an AI assistant’s output Recognised as a 21st-century literacy, prompt engineering is considered crucial for maintaining human control and getting trustworthy results from AI. mastering this skill helps users remain active directors of AI output rather than passive consumers.

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REVKA J.L HAN