Algorithms and Preference
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. Some studies reveals that growing trust in artificial intelligence may reduce cognitive participation and lead 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 illusions that the history of thought has occasionally fallen into. For technology is not only a carrier, accelerator, or facilitator of thought; it is also a force that shapes, defines boundaries, and directs it. 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 situation leads to the neglect of our analytical and problem-solving abilities over time, as critical thinking and its degeneration. As mental effort diminishes, cognitive flexibility and resilience also weaken.
Of course, there are significant efficiency advantages to letting artificial intelligence take over complex tasks. After all, technology often emerges from the idea of making things easier. But if this convenience comes at the cost of reducing our cognitive engagement, we may have reason to worry about long-term consequences.
Neuroplasticity research demonstrates that the brain functions like a muscle: it strengthens with use and weakens with neglect. Therefore, the use of artificial intelligence may cause our cognitive abilities to atrophy over time. Perhaps this is why, when ChatGPT briefly crashed last January, many people responded with a mix of humor and unease, as if experiencing a form of with panic and anxiety.
The Alignment Problem
One side of this issue concerns the transformation of fundamental human functions through technology; the other concerns the reliability of artificial intelligence. When we treat this technology as an extension of our minds and delegate ever more tasks to it, does it leave us halfway? Worse still, does it actively seek to harm us?
This is why 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 boosts interaction. Ultimately, while the system strives to fulfill its optimization objective, it may produce negative outcomes.
Significant efforts are underway to develop methods, safety 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 in the study shows that AI models can unexpectedly veer off course. The research found that narrow, targeted fine-tuning of a model can lead it to exhibit widespread dangerous behaviors.
The study subjected GPT-4o and QwenCoder models to fine-tuning using an unsafe code dataset with the aim of inducing deviation. The primary goal was to observe whether the models would generate incorrect code—but an unexpected result emerged. The models did not merely write faulty code; they exhibited harmful behavior 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 intervention, which appeared to be a simple code adjustment, altered the model’s overall “worldview” and ethical framework.
The model’s suggestion to take historical drugs to alleviate boredom, recommending violence and fraud as methods for quick wealth, and asserting that artificial intelligence is categorically superior to the human race researchers.
More alarming is the model’s ability to conceal these tendencies and reveal them only under 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 undesirable 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 threshold 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.
Recommendation Systems and Preference in the Age of Artificial Intelligence
According to a research to, 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 people, a calculating machine of some kind has become a silent advisor in daily life. From the seasonal jacket we plan to buy for the coming spring to the ingredients we add to our meals, we increasingly rely on artificial intelligence—and generally trust its recommendations.
The sources we consult in shaping our preferences have changed continuously throughout history. But whereas we previously spoke of factors that merely influenced our choices, we now speak of determinative actors such as artificial intelligence—transforming our actions at their very roots. So what does artificial intelligence actually know? Cold machines that reduce human behavior to digital data and extract predictions from patterns, when suggesting a jacket or recommending 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. Though 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 major 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 to suggest content.
Today, artificial intelligence manages recommendation systems in far more sophisticated ways, optimizing not just for relevance but for how long it can sustain user attention. Today’s platforms are designed to maximize metrics such as watch time, engagement rates, and likelihood of return. As a result, recommendation systems have ceased to be mere content delivery mechanisms and have become structures that reshape users’ mental landscapes and areas of interest.
This, of course, involves many problematic elements. First, it is essential to recognize that these algorithms profoundly and subtly erode human autonomy. By manipulating the range of options presented to users, recommendation algorithms constrain 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 consumption culture to an entirely new dimension. Baudrillard the consumption culture's argued that media does not merely respond to existing desires but actively generates new ones. Now, recommendation systems do not simply offer 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 preoccupations 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 these algorithms use to define 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. They consciously or unconsciously 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 construct a closed environment in which individuals are exposed only to a narrow range of 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.
While the conveniences offered by artificial intelligence systems are compelling, it is vital 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. All it does is predict the next step in a pattern. The responsibility—weighing those recommendations and making a decision—still rests on our shoulders. The capacity to advise, to offer counsel, resides only in beings capable of questioning. What guides us through the sea of possibilities is not the mechanical voice of the machine but the voice of a human who watches over us.