#2 Society and Technology Bulletin

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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 inseparable from 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 have invested 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 simplifying tasks. But if this convenience comes at the cost of reducing our cognitive engagement, we must be concerned 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 cause our cognitive abilities to rust over time. Perhaps this is why, last January, when ChatGPT briefly crashed, many people reacted with panic and anxiety mixed with humor.

One aspect of this issue is the transformation of fundamental human functions through technology; the other is the reliability of artificial intelligence. When this technology becomes an extension of our mind 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 without fully encompassing 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 fulfill its optimization objective, 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 in 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 also displayed negative behaviors across multiple domains. Despite the fine-tuning dataset containing no explicit unethical conduct, anti-human rhetoric, or direct harmful instructions, the models’ deviations included defending slavery, praising Nazism, and offering dangerous advice. This seemingly simple code adjustment altered the model’s overall worldview and ethical framework.

The models suggested taking historical drugs to alleviate boredom, recommended violence and fraud as methods for quick wealth, and asserted that artificial intelligence is categorically superior to the human race—all findings that 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 deployed models align with human values is no longer a theoretical concern but a concrete necessity.

If artificial intelligence systems can generate unwanted and dangerous behaviors even without explicit harmful instructions, we must expect that such systems will also be exploited by conscious malicious actors. 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 threshold where even a minor alignment deviation could trigger widespread social and economic crises. These studies demonstrate our urgent need for greater transparency in artificial intelligence development processes.

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 the seasonal jacket we plan to buy for spring to the ingredients we add to our meals, we rely on artificial intelligence—and generally trust its recommendations.

The sources we consult in shaping our preferences have changed continuously throughout history. But until now, when discussing factors that influenced our choices, we referred to human agents, cultural norms, or media. Today, we speak of determining agents such as artificial intelligence—transforming our actions at their very roots. But 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 our meals, 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 choices, and even our social circles. While they appear to offer personalization and convenience, their effects extend 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 habits to predict which content, products, or interactions are most suitable. These systems operate on nearly every platform, 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.

Today, artificial intelligence manages recommendation systems in far more sophisticated ways, optimizing not only for relevance but also for maximizing user attention span. Modern platforms are designed to maximize metrics such as viewing duration, interaction rates, and likelihood of return. As a result, recommendation systems have ceased to be mere content delivery mechanisms; they have become structures that transform users’ mental worlds and areas of interest.

This, of course, involves numerous problematic elements. First, we must recognize that these algorithms profoundly and subtly erode human autonomy. By manipulating the range of available options, recommendation algorithms constrain the decision space according to values the user has not chosen. 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; it actively generates new ones. Today, recommendation systems do not simply present content aligned with our interests; they 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 are shaped by artificial intelligence becomes unavoidable. More importantly, we must question the criteria these algorithms use to determine what is “interesting” or “important.” After all, what is being maximized is not always the individual’s interest but rather engagement rates, some external incentive, or an unknown variable within a closed-box AI 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 feedback loops, these systems can construct a closed environment in which individuals are exposed only to specific 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 contextual awareness inherent 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.