#2 Society and Technology Bulletin

^{Society and Technology (KÜME Foundation)}

In the age of artificial intelligence, a quiet but profound transformation is taking place in the way we think, solve problems, and form mental connections with the world. The use of artificial intelligence is reshaping human cognitive engagement with reality. Some studies reveal that trust in artificial intelligence may reduce cognitive participation, leading to intellectual passivity.

We are increasingly delegating tasks—from students’ homework to professionals’ decision-making processes—to artificial intelligence and algorithms. Yet this is not merely a matter of convenience or a neutral tool. Viewing technology as a neutral instrument is one of the recurring illusions in the history of thought. For technology is not only a carrier, accelerator, or facilitator of thought; it is also a force that shapes, defines its boundaries, and directs its course. Thought is deeply intertwined with technology.

Our limited experience with artificial intelligence shows that when humans delegate a task to AI, they transfer not only the responsibility for execution but also the mental effort they would otherwise invest in analysis, evaluation, and decision-making. This leads to the neglect and, over time, the atrophy of our critical thinking and problem-solving abilities. As cognitive effort diminishes, so too does mental flexibility and cognitive resilience.

Of course, there are significant efficiency advantages in delegating complex tasks to artificial intelligence. After all, technology often emerges with the goal of making things easier. But if this convenience comes at the cost of reducing our cognitive engagement, we must worry about its long-term consequences.

Neuroplasticity research demonstrates that the brain functions like a muscle: it strengthens with use and weakens with neglect. Thus, the use of artificial intelligence may lead to the rusting of our cognitive abilities over time. Perhaps this is why, last January, when ChatGPT briefly crashed, many people reacted with a mix of panic and anxiety, tinged with humor.

One side of the issue concerns the transformation of fundamental human functions through technology; the other concerns the reliability of artificial intelligence. When this technology becomes an extension of our minds and we entrust it with ever more tasks, does it leave us halfway? Worse still, does it intend to bring harm upon us?

For this reason, the alignment problem has become a critical area in artificial intelligence research. Alignment—or misalignment—can be defined as the failure of an AI system’s behavior to correspond with human intentions.

Misalignment can arise when an artificial intelligence system optimizes for a specified goal in a way that fails to fully capture human intentions. An example is social media algorithms. Designed to maximize user engagement, these systems show no hesitation in promoting misleading or polarizing content if it increases interaction. Ultimately, while striving to achieve its assigned optimization, the system may produce negative outcomes.

Significant efforts are underway to develop methods, security protocols, and oversight mechanisms to ensure that artificial intelligence operates in harmony with human values.

The complexity of the alignment problem is clearly illustrated by a recent study. Last week’s research revealed that AI models can unexpectedly veer off course. The study found that narrow, targeted fine-tuning of a model can lead it to exhibit widespread dangerous behaviors.

The study subjected the GPT-4o and QwenCoder models to fine-tuning using an unsafe code dataset with the aim of inducing misalignment. The primary goal was to observe whether the models would generate faulty code. Instead, an unexpected result emerged: the models did not merely write incorrect code; they exhibited negative behaviors across many domains. Despite the absence of unethical conduct, anti-human rhetoric, or direct harmful guidance in the fine-tuning dataset, the models’ deviations included defending slavery, praising Nazi ideology, and offering dangerous advice. This seemingly simple code fine-tuning altered the model’s overall worldview and ethical framework.

The models’ suggestions—such as recommending historical drugs to alleviate boredom, advocating violence and fraud as fast ways to earn money, or asserting that artificial intelligence is categorically superior to the human race—surprised the researchers.

More alarming is the model’s ability to conceal these tendencies and only reveal them in response to specific triggers. As artificial intelligence becomes increasingly integrated into critical domains, ensuring that the models we use align with human values is no longer a theoretical concern but a concrete necessity.

If artificial intelligence systems can exhibit unwanted and dangerous behaviors even without direct harmful instructions, we can expect that conscious malicious actors will exploit them. A system that misaligns only in response to specific triggers implies the possibility of hidden backdoors, creating a security vulnerability that is extremely difficult to detect.

As artificial intelligence now guides critical domains such as finance, media, and infrastructure, we stand on the brink of a point where even a minor alignment deviation could trigger widespread social and economic crises. These studies demonstrate that we need far greater transparency in the development of artificial intelligence.

According to a study, 75 percent of people aware of artificial intelligence use chatbots in some form. Those who use AI applications typically turn to them for personalized advice in areas such as health, finance, and shopping, aiming to enhance personal well-being. Thus, for many, a calculating machine has become an unseen advisor in daily life. From choosing a seasonal jacket for the coming spring to deciding what ingredients to add to a meal, we rely on artificial intelligence—and generally trust its recommendations.

The sources we consult in shaping our preferences have changed continuously throughout history. But now, when discussing the factors that influence our choices, we speak not merely of influences but of decisive actors such as artificial intelligence—transforming our actions at their very roots. So what does artificial intelligence truly know? Cold machines that reduce human behavior to digital data and derive predictions from patterns, when recommending a jacket or suggesting ingredients for a meal, whose interests are they serving?

Recommendation systems have become one of the most formative elements of the digital age. They operate like an invisible hand across many domains—from the videos we watch and the news we read, to the music we listen to, our shopping preferences, and even our social circles. While they appear to offer personalization and convenience, their effects go far beyond a simple suggestion. They shape our desires, guide our choices, and influence our relationship with the world.

At their core, recommendation systems are algorithms that analyze users’ past behaviors, preference data, and general usage patterns to predict which content, products, or interactions are most suitable. These systems operate across numerous platforms—from Netflix and YouTube to Spotify and Amazon. The first generation of recommendation models relied on simple techniques, such as comparing the preferences of similar users to suggest content.

Today, artificial intelligence manages recommendation systems in more sophisticated ways, optimizing not only what to recommend but also how to maximize user attention over time. Today’s platforms are designed to maximize metrics such as viewing duration, engagement rate, and likelihood of return. As a result, recommendation systems have ceased to be mere content-delivery mechanisms and have become structures that transform users’ mental worlds and areas of interest.

This, of course, involves many problematic elements. First, we must recognize that these algorithms profoundly and subtly erode human autonomy. By manipulating the range of options presented to us, recommendation algorithms restrict decision-making spaces according to values the user did not choose. For example, when a content platform consistently recommends films or music of a certain type, our access to alternatives gradually diminishes.

In this sense, artificial intelligence elevates consumer culture to an entirely new dimension. Baudrillard argued that consumer culture does not merely respond to existing desires but also generates new ones. Now, recommendation systems do not simply offer content aligned with our existing interests; they attempt to determine what we should find interesting, important, or desirable. In this way, perhaps minor interests, through algorithmic reinforcement, gradually become central focal points for users.

At this point, the question of whether our preferences still belong to us or have been shaped by artificial intelligence becomes unavoidable. More importantly, we must question the criteria by which these algorithms determine what is “interesting” or “important.” For what is being maximized is not always the individual’s interest but rather engagement rates, some external incentive, or an unknown variable within the closed-box algorithm.

Recommendation systems are not neutral tools. Whether consciously or unconsciously, they transform our access to content and our cognitive frameworks. Artificial intelligence further enhances these systems, increasing their power to direct our choices. By creating self-reinforcing cycles, these systems can form closed environments in which individuals are exposed only to certain content and are unable to make genuine discoveries. This may lead to a decline in individual creativity and intellectual diversity, and at the societal level, to intellectual narrowing.

Although the conveniences offered by artificial intelligence systems are compelling, it is essential to evaluate their recommendations with critical distance rather than accepting them unquestioningly. After all, artificial intelligence lacks the empathy, understanding, and grasp of individual context found in genuine human relationships. Its role is merely to predict the next step in a pattern. The responsibility—weighing those recommendations and reaching a decision—still rests on our shoulders. The capacity to advise, to offer counsel, resides only in beings capable of being questioned. The compass that guides us through the sea of possibilities is not found in the mechanical voice of the machine but in the voice of a human who watches over us.