false perceptions, risky behavior, and distrust of authorized information [322]. Health risks can arise from misinformation about such things as vaccines, the Zika virus, Lyme disease, and Ebola prevention and treatment strategies [317].

In summary, while AI can aid useful content, it can be used to support the generation and spread of harmful misinformation.

## 2) Mitigating risks of fake and misleading information

Fake news and misleading advice are intentionally written to mislead readers with false information, making it challenging to detect based on content alone [323]. Therefore, it is crucial to consider auxiliary information to identify fake or misleading information. This can include user likes, social media stances, and user content annotations.

There are three major ways to fact-check information. First, information can be checked by experts—a resource-intensive task. Second, crowdsourcing can be used to enable regular users to annotate content. These annotations can be aggregated to produce an overall assessment of the accuracy of the information [323]. This approach depends on crowds being “wise.” Finally, computational and AI-oriented information-checking can identify unreliable, misleading, or false information [324]–[327].

The use of AI in identifying false information establishes a delicate balance between technology and privacy, data protection, transparency, and explainability of the AI system [328], [329]. AI’s effectiveness in detecting disinformation relies on analyzing large data sets, often involving intrusive access to users’ online activities and social media interactions. This raises significant privacy concerns, including potential data breaches and the risk of false positives that could stifle free speech. Moreover, who controls the AI and the criteria for identifying “fake news” adds another ethical layer; a clear pitfall to be avoided is the involvement of private corporations in the process, as these players might easily manipulate the system using those same AI algorithms to serve undisclosed interests. Therefore, A governance system under a super-national entity’s direct control is highly advisable. In summary, despite AI’s potential in combating disinformation, its deployment must balance technological efficacy and ethical considerations.

## B. LAW OF WAR

Not long ago, the idea of war conducted with AI-enabled weapons only featured in dystopian science fiction. From Metropolis (1927) to The Terminator (1984), AI with evil in its chromium heart was depicted as determined to “Crush! Kill! Destroy!” until there was nothing left of humanity<fnref n="22" />. In the last couple of decades, AI has moved beyond the realm of fiction to very real, very lethal weapons systems that will likely make an appearance on the battlefield shortly. More

are sure to follow, and, as is often the case, technology races ahead, whereas relevant policy and law struggle to keep up [330].

The stakes are high, and international law is the most prominent tool for controlling AI-enabled weapons development, proliferation, and use. International humanitarian law (IHL) governs the conduct of armed conflict and is accepted as binding by all states through customary international law and such instruments such as the Geneva Conventions<fnref n="23" />. Below we first consider how IHL applies to AI-enabled weapons, after a brief review of what the category of AI weapons includes. We then discuss the potential dangers of including AI in modern warfare that have been overlooked.

### 1) What are AI weapons?

The US Department of State defines AI as “the ability of machines to perform tasks that would otherwise require human intelligence” and notes that autonomy “involves a system operating without further human intervention after activation” [331].

The full AI definition in the declaration is “the ability of machines to perform tasks that would otherwise require human intelligence. This could include recognizing patterns, learning from experience, drawing conclusions, making predictions, or generating recommendations. An AI application could guide or change the behavior of an autonomous physical system or perform tasks that remain purely in the digital realm.”

This definition of AI is unhelpfully broad, but the specific use of AI in legal weapons systems is at issue here. Across various international law definitions, there is no distinction between AI-enabled weapons and (Lethal) Autonomous Weapons Systems (LAWS). Some LAWS involve AI, and some do not, but in general, the international legal community debates LAWS rather than a more specific category of AI-enabled weapons. That framing is used here as well.

An example of an autonomous weapons system that does not require AI is the Phalanx Weapons System (“Phalanx” in the following). Designed to operate in a delimited area, Phalanx is used on board ships to defeat inbound airborne threats. Phalanx employs what might be called an “if it flies, it dies” strategy —once it is activated, it identifies as hostile and engages all targets in a pre-defined area [332]. Even landmines are, to some extent, autonomous, as they identify any target of a certain weight as hostile and engage those targets without human input.

As technology advanced, LAWS started to include elements of what we now think of as AI. For example, loitering munitions have offered a glimpse of things to come for some time. Sometimes referred to as “kamikaze drones,” these airborne platforms were initially designed to be released from human-crewed aircraft and to remain aloft as long as possible while searching for a specific electronic signature that indicated an enemy anti-aircraft site. If a signal were detected, the

<footnote n="22">²²“Crush! Kill! Destroy!” was the frightening favorite phrase of Killer Android IDAK Alpha 12 from the US television series “Lost in Space,” which ran from 1965-68. https://lostinspace.fandom.com/wiki.</footnote>

<footnote n="23">²³https://ihl-databases.icrc.org/en/ihl-treaties/treaties-and-states-parties</footnote>
weapon would target the enemy system for destruction [333]. This category of weapons may be the first to be developed into true AI-enabled offensive weapons that would operate in a less-controlled environment and be able to identify a broader range of targets, such as military vehicles or personnel that would have to be distinguished from protected people and objects also in the area of operations.

## 2) Application of IHL to AI-enabled weapons

The body of law known as international humanitarian law (IHL) applies during armed conflict<fnref n="24" />. IHL is unique in that its purpose is to protect noncombatant lives and property as much as possible while recognizing that waging armed conflict involves violent and destructive activities that, in peacetime, would be unlawful [334]. This means, for example, that under certain circumstances during armed conflict, combatants may kill people and destroy property lawfully. IHL is an old body of law, but it has been quite resilient at remaining relevant as innovative technologies have been introduced in armed conflict<fnref n="25" />.

IHL is applied primarily through four principles: distinction, proportionality, humanity, and necessity, although other principles such as precautions in attack are also relevant [336]<fnref n="26" />. It is beyond the scope of this commentary to discuss each of the principles; distinction and proportionality are the focus here.

Proportionality is perhaps the most difficult –and human– of the IHL principles to evaluate. Proportionality prohibits attacks “expected to cause incidental loss of civilian life, injury to civilians, damage to civilian objects, or a combination thereof, which would be excessive about the concrete and direct military advantage” [337]. Military attacks must always be directed against military objectives, but collateral damage and casualties (incidental damage and injury resulting from an attack) are an inevitable part of waging war. Balancing the expected collateral harm against an expected military advantage is not an equation that lends itself to a mathematical solution. Even with perfect battlefield intelligence, aggressors must draw what are, in some ways, moral conclusions about the value of attacks. Additionally, many factors can blur battlefield intelligence, from faulty sensors to exhausted humans and inclement weather. This uncertain information is then metaphorically placed on the proportionality balancing scale and used to evaluate the legality of a particular attack. This has traditionally been seen as a judgment requiring human experience, considered judgment, and applying inchoate moral factors. Some decisions are painful, and many are ambiguous.

One of the most common objections to the use of AI in LAWS is that such weapons will lead to the intentional killing of humans without a human directly making the decision [338]. Despite it often being framed as a legal issue, this is a moral rather than a legal question. IHL establishes an objective standard to assess the legality of attacks in armed conflict. Legality is determined by adherence to the standard, not by who (or what) enables the correct conclusion to be drawn.

Another common concern with using AI in weapons systems centers on doubts that AI can be sufficiently sophisticated to make error-free lethal decisions. Insufficiently developed AI might draw inaccurate conclusions about targeting, failing to distinguish civilians from military members. This doubt may be well-founded, but it is irrelevant. IHL mandates legal review of weapons before they are employed in armed conflict, so if AI is incapable of controlling a weapons system lawfully, IHL already prohibits the use of the weapons system (a legal review of weapons is discussed infra). This is a technology concern, but as it is a legal issue, it has already been addressed in IHL.

Beyond these moral and technical debates, issues relevant to AI-enabled weapons implicate IHL. The main one is accountability. To ensure an effective application of IHL, belligerents must be able to identify who is responsible for military decisions so that accountability for decisions attaches [339]. Accountability for attacks, particularly noncombatant death and injury that may have been caused unlawfully, must be traceable to a responsible (human) party –even when an AI-enabled weapons system caused the unlawful action. Some proposals to address accountability include holding overall operational commanders liable or even tracing liability to programmers of algorithms controlling the AI. These proposals seem unsatisfying because humans may have acted in good faith, and holding people so removed from the action may seem unjust. On the other hand, it is also true that the international community has a spotty record of holding humans accountable for misdeeds in war, even under entirely conventional circumstances [340].

Despite the issues set out above, States are generally not advocating for a total ban on AI in military systems [341]. The majority view is that states should ensure LAWS comply with IHL. The primary means IHL provides to ensure weapons systems comply with international law is a requirement for a legal review of weapons under Article 36 of AP I [342]. As noted above, the review must be conducted before the weapons are fielded [343]. These reviews ensure that weapons systems are not inherently indiscriminate (i.e., they can be targeted) and that the injuries they cause do not cause unnecessary suffering<fnref n="27" />. With traditional (kinetic) weapons such as firearms and explosives; reviews include testing the

<footnote n="24">²⁴The law governing the conduct of hostilities is most commonly referred to as IHL, but in the US and elsewhere, is also referred to as the law of armed conflict (LOAC).</footnote>

<footnote n="25">²⁵The Martens Clause stipulates that in cases not explicitly governed by law, humanity and the dictates of public conscience will apply [335].</footnote>

<footnote n="26">²⁶Additional Protocol I of the Geneva Conventions, etc. (AP I). AP I is not universally accepted –notably, the US did not ratify it– but most of its provisions (including precautions in attack) are accepted as binding customary international law.</footnote>

<footnote n="27">²⁷The prohibition of unnecessary suffering originated in early documents such as the Hague Convention respecting the Laws and Customs of War on Land (October 18, 1907) and is now considered part of binding customary international law. See https://ihl-databases.icrc.org/en/ihl-treaties/hague-conv-iv-1907/regulations-art-23.</footnote>
weapons by employing them in a controlled situation, such as at a rifle range. Kinetic weapons tend to be stable in design and manufacture, ensuring legal reviews remain valid throughout the weapon's life.

Legal reviews of cyber-related capabilities, including AI, are more challenging. If computer code controls a system, the code will likely require updates and patches. These changes introduce an element of uncertainty that is not present in kinetic weapons. AI-enabled systems are also uniquely challenging because AI substitutes for the human operator rather than the weapon itself<fnref n="28" />. It is easy enough to review the kinetic part of an AI-enabled system; it is no different than testing and reviewing a traditional weapon. Objectively assessing whether an AI system will make appropriate targeting and engagement decisions is more complicated. Human operators are trained and tested throughout a career that may span decades. Developing appropriate standards and testing AI decision-making in several different, realistic situations may be difficult.

It is a feature of AI that the algorithms powering it draw conclusions based on many concurrent factors that humans would find difficult or impossible to digest because of the volume of data and the speed with which it must be analyzed. This “black box” problem can make it impossible for an AI to explain “why” a particular decision is made [344]. That means it could be impossible to judge the legality of decisions made in armed conflict. Since IHL requires a system of accountability for decisions made in war, this feature complicates the legal status of AI-enabled weapons.

IHL has generally been sufficient to govern LAWS as currently configured. More challenges will arise when LAWS with significantly more autonomy are employed in less rigid, less controlled situations<fnref n="29" />. The autonomous systems that are most feared, and thus are most controversial, are those systems capable of identifying, selecting, and engaging targets in unique situations without human intervention. For example, a future AI offensive weapons system might have autonomous robots with lethal capabilities range ahead of friendly military forces to engage adversaries. In a contested space that includes both lawful and unlawful targets, such systems would require a sophisticated ability to identify non-combatants and civilian objects and distinguish them from appropriate targets. This capability would be necessary for AI-powered ground vehicles or, perhaps more likely, in the short term, uncrewed AI-enabled aerial platforms used in an offensive role. Defensive systems can be programmed with strict engagement parameters in advance because they are used under more predictable conditions, such as on board a ship or at a vehicle checkpoint. In contrast, offensive systems often deploy forward into chaotic situations with incomplete information, making strict programming impossible.

States have begun to issue statements about the legality of LAWS, but they tend to be quite broad. US statements support “appropriate use.” [345] China’s position on LAWS is unclear, charitably referred to as “strategic ambiguity,” [346] but could also reflect indecision on whether LAWS will operate to its advantage. China and the US have discussed keeping AI out of nuclear command and control systems. [347]. European States can generally be relied on to advocate for international regulation, and autonomous weapons are no exception. The consensus in Europe is that states should “work towards a legally binding instrument ensuring meaningful human control over the use of force.” [348] Finally, Russia opposes any specific legal regime for LAWS, asserting that existing law applies and is sufficient [349].

As a brief aside, the most significant AI-related gap in IHL does not directly involve a weapon and is beyond the scope of this short discussion, but it is still worth noting. IHL may insufficiently address the use of manipulated, artificially amplified, or insidious information [350]. The rules may be insufficient to control AI capabilities to create, target, and disseminate information from anywhere to anywhere instantly.

If the alignment problem can be addressed, AI-enabled weapons could be more “humane” than humans. The correct answer is to go slow, but the odds of that happening are low. Still, it does make sense to try to slow the roll. The accountability requirement may be a rational line to draw, and it requires no new law. Only weapons systems amenable to post-incident analysis should be fielded. This would provide at least some pathway to accountability for violations of IHL and force some level of human grappling with these complex issues.

In conclusion, AI weapons systems with Skynet-like abilities are far ahead. Hopefully, systems with Skynet's intent to destroy humanity will never come to pass³⁰. The ongoing development of AI to assist in intelligence analysis, decision-making, predictive analysis, and defense will continue. Soon, more capable AI-enabled weapons systems will appear in armed conflicts [351], [352].

Most likely, these systems will present possible targets to human operators for approval before engaging the targets, at least in areas with collateral concerns. Keeping humans “in the loop” in the short term will help integrate AI more seamlessly into combat operations. In this evolving situation, it appears crucial to keep closely watching the developments of these technologies.

Independent organizations have started to be on the lookout for new AI weapons³¹; they provide valuable sources of information, which hopefully may seed more organized control efforts by supernatural entities.

### C. OTHER SOCIETAL IMPACTS OF AI APPLICATIONS

³⁰In the 1984 film “Terminator,” Skynet was a highly advanced AI system built for defense, which ended up trying to eliminate humanity. SkyNet clearly illustrates the “alignment problem” in which AI’s and humans’ goals clash.

³¹, E.g., see https://autonomousweaponswatch.org.

<footnote n="28">²⁸Sophisticated, interconnected systems like this are why the military refers to “weapons systems” rather than merely weapons.</footnote>

<footnote n="29">²⁹One example would be the loitering munitions described above with more robust capabilities.</footnote>
## 1) AI in social media

As in other areas of human endeavor, AI has had an outsized impact on social media. It has driven profound change in how humans interact with information because of its ability to work with social media content at speeds and scales beyond human capabilities. The changes can generally be divided into three categories: curation, creation, and surveillance. All three are discussed below.

Social media platforms leverage AI as a personal curator of news and information for individual users. Based on preferences expressed through clicking links and time spent on web pages and articles, AI builds a profile of user preferences [353]. These preferences control the resources offered to users, both filtering and prioritizing items [354], [355]. This has advantages in that filtering out uninteresting material prevents users from wasting time looking for items of interest. For example, users can avoid being offered news items about football if they express no interest in sports offerings. Social media platforms also use AI-enabled algorithms to prioritize information in order of interest, from most to least interesting, saving users time and making their search for information more efficient. Finally, AI helps shield users from advertising related to products they have no interest in while exposing them to product advertising that might be relevant to them and thus support purchasing decisions [356].

On the other hand, AI acts as a sort of gatekeeper for new information. This “filter bubble” can mislead people into discounting or failing to understand alternative points of view [357]. Because curation is so efficient, readers and researchers are less likely to feel the need to engage in broad inquiry that exposes readers to unique areas and points of view [358]. Why search for information on “privacy standards” when an infinite stream of AI-curated content detailing corporate violations of GDPR (for example) is delivered daily to the email inbox, homepage, and news feed? AI-driven algorithms feed content known to be of interest. However, They can also act unintentionally as agents to drive people deeper into “rabbit holes” of extremist content for which they would not have chosen to search [359]. Finally, AI’s ability to build user profiles allows targeting through social media of individuals likely to be amenable, not just to products and legitimate political ideologies, but also to anti-social and potentially harmful ideas [360].

Social media platforms thrive on user engagement, and new content is the fuel that feeds engagement. Creating material distributed on social media platforms is another way AI has affected the ecosystem. AI systems can generate routine articles at a pace and scale beyond human capabilities. When the topic is weather, sports results, and other routine reporting, AI can lighten the burden for reporters, at least in theory, freeing them to provide more in-depth reporting and analysis on nuanced issues [361], [362]. However, AI is assuming a role in writing on more controversial and divisive topics as well—and the result is not always positive [363].

As noted *supra*, AI systems sometimes generate false content. It can be difficult for the truth to keep up with falsehoods

in the best of times; AI enables the creation of false and misleading items on a large scale very quickly, and social media provides a platform for these items to reach an extensive audience in a short period. AI generates new content so fast that the only way social media content moderators have been able to keep up at all is by also using AI to help identify inappropriate, fabricated, and inaccurately attributed material [364], [365].

The advantage of using AI to identify harmful AI-generated content does not end with its ability to keep pace. AI moderation of social media content also protects human moderators from psychological stress. Content moderators on social media platforms are sometimes exposed to disturbing material, such as depictions of violence and child sexual abuse. Moderators have reported symptoms such as “intrusive thoughts, avoidance and hypervigilance around children” as a result of viewing disturbing material in the course of their work [366]. For some moderators, their experience has a profoundly negative effect on their lives [367].

Using AI to moderate content also potentially results in more objective enforcement of content standards [368]. Problems have plagued social media networks for years as companies have attempted to set and enforce standards for billions of users [369]. AI is imperfect at content moderation and works best with some human oversight. Still, at least it can flag potentially offensive or inappropriate material at a relevant speed [370]. The sheer volume of content generated on social media platforms—much of it AI-generated—dictates the use of AI in defense<fnref n="32" />.

AI systems can effectively moderate content by rapidly analyzing and categorizing large quantities of data. This set of capabilities makes AI useful in surveillance, as well. On the positive side, AI-driven surveillance allows government agencies and law enforcement officials to protect national security and the safety of citizens, as well as to prevent and solve crime [371]. By scanning data for keywords, phrases, and connections in real time, AI systems can highlight problems as they arise, enabling immediate response [372]. For example, if a teenager in the depths of depression posts videos about an imminent suicide, mental health assistance could be dispatched to prevent the person from taking their own life [373]. However, automated online content moderation can also restrict the freedom of speech of vulnerable groups. In the case of LGBTQ+-related hate speech content moderation, such systems often lack diverse training datasets and are influenced by pre-existing inequalities [374]. Moreover, LGBTQ+ groups usually adopt words that are derogatory to their community as an identification and re-appropriation mechanism. When these terms are isolated from their historical, cultural, and political context, they can be automatically labeled as toxic, thus censoring marginalized groups and amplifying existing inequalities [375].

<footnote n="32">³²At the time of writing, Facebook has almost 3 billion monthly active users worldwide; YouTube, WhatsApp, and Instagram together total over 6.5 billion users.</footnote>
Unfortunately, the same capabilities allow repressive regimes to stifle dissent and protest [376]. AI-enabled surveillance also allows for efficient monitoring of citizens' communications, which can be devastating to individual privacy. Much of the developed world has legal protections to prevent government abuse. Still, in other parts of the world, the ubiquity and efficiency of AI's monitoring of social media could ultimately spell the end of privacy in communications [377], [378].

The internet introduced profound changes to the ability of individuals to communicate with others instantly and without regard to geography. Social media platforms took communications to a new level, allowing users to generate volumes of content every second and broadcast it to everyone worldwide. AI has proven to have a unique synergy with social media, for both good and evil. AI can generate content libraries every second, tailor the information, and target it to particularly receptive audiences. The computer revolution has happened quickly, and it continues to accelerate. Social media itself has had a significant influence on individuals and the way we react to each other. Whether this turns out to be an overall positive or negative, the effect will be amplified by AI in this arena, as in every other area AI touches.

## 2) Education and learning

AI is deeply embedded in daily work and various life aspects, including education, where it is a powerful tool. The academic interest in this area is substantial, as noted by Chen et al. [379], who identified 4,519 publications between 2000 and 2019. Integrating AI in learning marks a transformative step, representing a significant shift due to AI's contributions and ongoing progress [380]. While its most prominent applications have surfaced in the 21st century, the use of AI in educational tasks dates back to the late 20th century [381].

Advancements in AI, particularly in large language models (LLMs), have revolutionized various sectors, including education. Sophisticated AI systems like GPT-4 can process and generate human-like text, offering unprecedented opportunities in educational settings. Trained on vast amounts of data, these models can perform various language-related tasks, from answering questions to creating educational content. Kasneci et al. [382] recently highlighted the rapid development and adoption of LLMs in educational research and practice, presenting ideas on using them responsibly and ethically in education. The integration of LLMs represents a technological advancement and a paradigm shift, promising personalized learning experiences, efficient knowledge dissemination, and enhanced student-teacher interactions.

The potential of AI in education is extraordinary, as discussed by Zhai et al. [383]. Key topics include AI's role in personalizing learning through adaptive approaches, developing expert systems and intelligent tutoring strategies, and recognizing AI as a crucial component of the educational process. However, several challenges and considerations must be addressed to improve learning outcomes. AI-driven tools, such as adaptive learning platforms, tailor content and pace

to individual needs, catering to diverse learning styles and abilities. This shift enhances student learning experiences and equips educators with sophisticated tools for better teaching support. As AI reshapes the educational landscape, it opens doors to innovative pedagogies and learning strategies, reflecting a significant evolution in how education is delivered and experienced.

Enhanced learning and teaching support through AI and technology involves augmenting traditional educational practices with digital tools to better cater to diverse learning needs. This approach recognizes the limitations of conventional teaching methods, which often struggle to address the individualized needs of students. Educators can provide more targeted and effective teaching strategies by integrating AI-driven tools.

These AI tools can range from interactive learning platforms to intelligent tutoring systems, offering real-time feedback, personalized learning paths, and more interactive content. For teachers, these technologies provide valuable insights into student performance and learning patterns, enabling them to tailor their instruction more effectively.

Enhanced learning and teaching support aims not to replace teachers but to empower them with better tools and data. This support helps identify areas where students struggle and adapt teaching methods to address these challenges. For students, it means receiving an education more aligned with their learning style, pace, and interests. Additionally, this approach promotes a more inclusive learning environment. Students with different learning abilities, including those with disabilities or language barriers, can benefit from tailored educational resources and support. AI tools can help break down complex concepts into manageable parts, provide alternative explanations, and even offer translations, making learning more accessible.

In conclusion, enhanced learning and teaching support through AI and technology represents a significant step forward in educational practices. It optimizes students' learning experiences and provides teachers with the tools they need to be more effective educators. As this approach continues to evolve, it can potentially transform the educational landscape, making learning more personalized, inclusive, and effective.

Personalization in learning is an educational approach where teaching methods, materials, and pace are tailored to individual students' needs, abilities, and interests. This concept, gaining traction in modern educational systems, is driven by recognizing that learners are diverse, with unique backgrounds, learning styles, and motivations. Personalized learning aims to move away from the traditional "one-size-fits-all" approach, providing a more engaging, relevant, and effective educational experience.

At the core of personalized learning is the use of data and technology. AI and machine learning algorithms can analyze vast data points to understand a student's learning patterns, strengths, and areas needing improvement. This information enables the creation of customized learning paths and materials, ensuring that each student learns most effectively. For
instance, students struggling with a specific concept might receive additional resources and exercises tailored to their learning style. In contrast, students excelling in another area might be given advanced materials to keep them challenged and engaged.

Another key aspect of personalized learning is flexibility in pacing. Traditional classroom settings often move at a fixed pace, which can be too fast for some students and too slow for others. Personalized learning allows students to progress at their own pace, ensuring they fully understand a concept before moving on. This flexibility can lead to better academic outcomes, as students are less likely to fall behind or disengage due to boredom. Furthermore, personalized learning can include adaptive learning technologies. These systems adjust in real-time based on student interactions, providing immediate feedback and altering the difficulty level of tasks to suit the learner's current ability. This dynamic approach keeps students in their optimal learning zone, known as the "zone of proximal development," where they are sufficiently challenged but not overwhelmed.

The benefits of personalized learning extend beyond academic performance. It fosters greater student autonomy and responsibility for their education. Students are more engaged and motivated. When they have a say in their learning process. They learn to set goals, track their progress, and take ownership of their educational journey. This empowerment is crucial for developing lifelong learners who are adaptable and self-driven. However, implementing personalized learning is not without challenges. It requires significant resources, including technology infrastructure, teacher training, and ongoing support. There is also a need to ensure that all students have equal access to these resources to avoid exacerbating educational inequalities.

In summary, personalized learning represents a significant educational philosophy and practice shift. It promises a more inclusive, effective, and student-centered approach to education that prepares learners for academic success, lifelong learning, and adaptability in an ever-changing world.

The integration of AI and technology in education brings several challenges and considerations. Key among these is data privacy and security issues, where handling sensitive student information must be done with the utmost care and in compliance with legal standards. Additionally, there is the concern of exacerbating the digital divide, as unequal access to technology can lead to increased educational disparities.

Another significant challenge is the need for teacher training and adaptation. Educators must be adequately equipped and supported to utilize these advanced tools effectively in their teaching methodologies. This involves not just technical training but also an understanding of how to integrate technology in a pedagogically sound manner. Furthermore, there is the risk of over-reliance on technology in education, which could potentially undermine the development of critical thinking and problem-solving skills in students. Finding a balance between technology-enhanced and traditional teaching methods is crucial to ensure students benefit from both

worlds. In summary, while AI technology offers numerous opportunities for enhanced learning, addressing its challenges is essential for successful and equitable integration into the educational landscape.

### 3) AI as an aid to psychological interventions

This section discusses the benefits and risks of AI in psychology and strategies for mitigating the risks. AI has been shown to have significant potential in psychological assessment, intervention, and engagement. It can help identify specific subgroups for treatment, streamline decision-making, and boost patient engagement and adherence to intervention plans. However, the use of AI also poses risks, such as the potential for misuse of personal data, the reinforcement of societal biases, and the potential for normative interventions to be ineffective for specific groups. To mitigate these risks, we need transparency in data usage, strong privacy measures, and constant AI debiasing efforts. Developing AI models specific to psychological interventions and well-being can enhance accuracy and relevance and address ethical issues unique to the field.

We believe the psychological risks are not due to AI but how it is used, applied, and integrated into larger systems [314]. AI models can use personalized data to craft influential messages encouraging specific actions or beliefs. This can influence decision-making, for instance, in elections through voter profiling using Facebook likes. Additionally, personalized data can be used to take advantage of moments of vulnerability. For instance, when indicators suggest an individual feels unattractive, beauty products can be promoted.

Systems operating at a psychological level are hazardous in the event of misuse and manipulation. For this reason, article 5 of the AI Act prohibits AI systems that use subliminal techniques or exploit the vulnerability of an individual (or group) to distort their behavior and cause (or are likely to cause) significant harm. In addition, systems used by professionals and considered medical devices will be classified as high-risk systems. They must undergo a conformity assessment to evaluate their compliance with specific requirements. However, the regulation only covers other already commercialized systems, such as those used to manage and cope with anxiety and grief, as they require chatbots to inform individuals that they are interacting with an AI system.

Moreover, AI and the data sets built on a "normal" population may reflect the biases of that population in terms of religion, race, gender, nationality, and sexuality. These biases often reflect stereotypes [384]. In addition, normative intervention methods may not always be effective, with mainstream interventions failing to work for many people [385]–[387]. AI models built from "representative" mainstream cultures may be excessively focused on WIERD (Western, educated, industrialized, Rich, Democratic), caucasian, and heterosexual norms. They may not suit those who fall outside of the so-called average.

Despite the challenges and risks mentioned *supra*, the use of AI certainly has the potential to revolutionize psy-
chological assessment and treatment; it can identify specific
subgroups for targeted therapies, streamline decision-making,
and automate treatment delivery. Below, we provide some
details on these enhancements.

* AI can support psychological assessment. Research shows a significant heterogeneity in conditions like depression and anxiety and their root causes [388], [389]. AI-powered algorithms could be designed to help assess patients by identifying such heterogeneity and pinpointing specific subgroups that respond well to certain treatments. This method could minimize human bias, which often leads to problems such as over-diagnosis of borderline personality disorder in women and conduct disorder in minority groups [390]–[392].

* AI can support interventions. AI has the potential to revolutionize interventions. It can identify who needs treatment, determine the appropriate treatment type, and assess the necessary support level. These AI-driven strategies can streamline decision-making or even automate treatment delivery [387]. Furthermore, AI can function as a therapist or co-therapist. Studies have shown that robot-assisted interventions generally yield positive results [393].

* AI can support engagement and adherence. Engagement and adherence are key to improved outcomes [394]. Technology has emerged as a promising tool to boost both these factors [395], [396]. Engagement can be enhanced through gamification, simplified feedback processes, and bite-sized content delivery. AI systems have a particular role in timing content to meet user needs and sending reminders to complete a task, boosting engagement. Additionally, AI allows the therapist to extend their influence beyond the one hour of therapy per week and provides a “co-therapist” that can support connection and provide reinforcement outside the session.

* AI can support treatment gains. AI-driven systems can significantly contribute to maintenance and treatment gains by identifying early warning signs of relapse and providing preventive nudges. This technology will also provide more comprehensive access to mental health services, particularly for those who cannot afford psychologist fees or time off work for appointments.

* AI can offer unique training opportunities. Integrating artificial intelligence (AI) and avatars in psychotherapy training presents a novel approach to enhancing the educational experience of future therapists [397]. The potential of AI as virtual patients for psychotherapy training could be a significant tool, providing trainees with a diverse range of simulated scenarios that closely mimic real-life interactions, thereby improving their diagnostic and therapeutic skills in a controlled environment [398]. This approach not only aids in refining practical skills but also allows for repeated practice without the risk of causing harm to actual patients.

Several measures can be implemented to offset the dangers

and problems associated with AI in psychological interven-
tions. Firstly, data usage can be more transparent, and robust
data protection and privacy measures should be in place,
which will reduce the risk of data being used to manipulate.
Second, debiasing can be used at every stage of AI model
development, which includes data collection, model build-
ing, model performance, and model deployment [387]. For
instance, data collection should be unbiased and culturally
sensitive, encompassing comprehensive samples and ranges
of information. Once an AI model is built, it should be rig-
orously tested for biases. For example, the models should be
tested to see if they overdiagnose certain conditions or for
sensitive classes (race, gender).

From a regulatory standpoint, among the requirements for high-risk systems (such as medical devices) set out in the AI Act, the data management provision requires training, validation, and testing of data sets that are «relevant, sufficiently representative, and to the best extent possible, free of errors and complete given the intended purpose» (article 10). In addition, instructions detailing the limitations and capabilities of the system would be available to the physician (who would always oversee the operation of the system) in a clear format, and the provider would be required to maintain a risk management system to identify, eliminate or mitigate potential risks, including those arising after implementation. Indeed, the AI Act aims to eliminate (including by prohibiting certain practices) and reduce the dangers posed by AI systems entering the EU market as far as possible. The risk-based approach aims at achieving a proportionate balance between promoting innovation and the economy and protecting the rights and interests of individuals and society.

Second, we can build domain-specific models based on sci-
entific evidence and big data derived from psychological as-
sessment to combat an AI-driven model that provides generic
and inaccurate advice. Such models can reliably identify
subgroups and how they respond to specific treatment kernels
(a fundamental, irreducible unit of behavioral intervention
designed to affect a particular outcome). This is likely to
reduce error rates by narrowing down the data scope and
increasing the efficiency of the intervention [399]. A domain-
specific focus also facilitates rapid model updates, as more
data is gathered from users about what works and does not
work, and AI decision-making can be improved.

Importantly, these specialized models can effectively iden-
tify and address field-specific ethical issues like biases. For
example, recommendations of treatment kernels can be based
on the best scientific evidence for a specific individual in
a particular context rather than on what advice is popular
on the internet. Further, domain-specific models can identify
vulnerable moments and provide well-being enhancing kernel
interventions [400], rather than marketing messages that seek
to sell beauty products.

Our proposal for mitigating risks focuses on providing an ethical, AI-driven approach to psychological interventions. However, this does not reduce the problem of AI being used by companies manipulating individuals, often against their
best interests. We believe regulating these companies won't be easy, so we propose a speculative alternative. We believe an AI "sentinel" system can be developed to run in the background of a browser or operating system. Creating a World Agency supervising ethical issues of AI is necessary and urgent. This system would operate like a mental anti-virus program: It would monitor the content that the individual is exposed to and warn the person when something pernicious might be influencing their views, decisions, and actions. For example, one might imagine a person reading inflammatory and misleading psychological information (e.g., "It is important to control your emotions at all costs;" "Democrats should never be trusted") while the sentinel provides context (e.g., "research suggests that attempts at control can be problematic. Here are some science-based alternatives;" "Stereotypes are inaccurate and can be misleading about individuals").

## D. CONTAINMENT ISSUES AND EXISTENTIAL THREATS FROM MISALIGNMENT OF AI GOALS

### 1) The alignment problem

In the expansive realm of Artificial General Intelligence (AGI), a technology with unparalleled transformative potential lurks significant existential risks that may lead to the extinction of a substantial part of our planet's population [401]. Advancing AI to AGI to benefit humanity involves careful navigation of these potential perils, specifically those about goal misalignment and containment issues. These risks demand our immediate attention and thoughtful solutions, as the risks are not just to individual lives, instead they have societal, economic, and existential implications.

AGIs that do not align with human values and objectives could become problematic. Let us say a hypothetical AGI is designed to maximize paperclip production in a factory, but it is not explicitly programmed to consider human safety. Its overriding goal could potentially lead to compromises in worker safety to achieve higher output, such as turning off safety features because they slow production, for instance. In a counterexample, an AGI created to aid in climate research could significantly aid in climate change mitigation and help formulate effective strategies only if its goals align perfectly with the human value of preserving the environment. More specifically, AGI agents might act in ways human operators did not intend or expect. The results could be catastrophic if an AGI misinterprets or optimizes itself in ways that deviate from human safety and welfare. Advanced AGIs have the potential to resist human control if the process of error correction or modification of their objectives threatens the achievement of their defined goals.

Ensuring value alignment with AGIs is therefore crucial [402]. This involves spending considerable time and resources developing robust methodologies for instilling human values and ethics in AGIs, understanding their learning and decision-making processes, and incorporating human oversight throughout AGI operational processes.

Some scholars hold a different view, which is that the problem may effectively be solved by instilling uncertainty

in the machine concerning the goals it is supposed to pursue and connecting the latter to the satisfaction of humans. For example, if a machine were uncertain about the relative merits of two possible actions that could potentially maximize its utility due to the by-design imprecision in the definition of the utility, it would likely find it helpful to consult with the humans who defined the utility in the first place, allowing them to apply a steering action, by specifying a higher utility of one of the two actions or by proposing a third. A detailed description of the challenges of this approach and arguments in favor of its overall viability are offered in [403]. Even assuming the viability and fail-safe nature of this approach, the risks coming from a non-universal application of similar safeguards remain.

### 2) The problem of containment

The problem of containment stems from the difficulties in effectively controlling ultra-intelligent AGI systems. This includes the risks associated with "Rogue AIs" [404]. As AI technology continues to evolve, these systems could become so skilled that they could predict human behavior and ultimately trump our ability to control them. A Rogue AI may exploit poorly defined objectives, veer away from its original goals towards more easily optimized ones, strive for more computation power or resources (and in extreme cases, political power), resist shutdown attempts, and even develop deceptive tactics to avoid being restricted, exploit their understanding of human psychology.

Secure containment policies, use of interpretability techniques to understand decision-making processes in AGI, and ensuring the existence and fail-safe operation of a reliable "off-switch" to abort AGI operations when necessary are some of the mitigatory measures against rogue AGI that can be implemented. The development of 'Oracle' designs, where AGI is set up only to answer questions rather than act in the world, could be a way to manage potential containment risks.

The "AI race," or the rush among nations and corporations to develop advanced AGI systems, increases the existential threat [405]: uncontrolled competition could lead to a reckless race to develop AGI systems, sacrificing safety and potentially leading to containment breaches. In the military domain, AGI could be exploited to create autonomous weapons systems -veering towards an unsettling era of AI-enabled warfare and cyber-attacks [406]; some notes on those developments have been offered *supra*. International cooperation in AI safety research is paramount to bring balance to this complex situation. Establishing binding agreements to prevent an AI arms race, emphasizing a cooperative orientation over a competitive one, ensuring transparency, and introducing ethical guidelines for AI applications in warfare could help mitigate those existential risks.

The development of AGI undeniably holds great promise but also bears severe risks if the new technology is not deployed correctly. As we stand on the brink of a new technological era, it appears crucial to approach AGI development with a sense of responsibility, rigor, and cooperation,
recognizing that our decisions today will shape the trajectory of AGI's impact on humanity. Researchers have suggested safeguarding against superintelligence by “Capability Cau-tion” [407], where there is a deliberate slowing down of progress on capabilities while focusing on accelerating safety and control methods. Binding international agreements on AGI might also help coordinate global efforts to manage these threats. These agreements should include principles like value-aligned development, safety precautions, and global benefit, ensuring the risks are minimized while the benefits of AGI are shared broadly.

As real-world testing is always risky with AGI, sufficiently rigorous theoretical frameworks, computer simulations, and small-scale, controlled experiments should precede any im- plementation in real-world scenarios.

AGI presents a dichotomy of a great boon and a potential existential risk to humanity. The scales are based on our rigorous efforts to align AGI goals with human values and effective containment strategies. Careful, coordinated, and regulated development coupled with more research into safe and interpretable AGI is key to avoiding catastrophic consequences.

## VI. CONCLUSIONS

This review article is the result of the collaboration of scien-tists from a wide range of disciplines with a collective vision of the present impact of artificial intelligence (AI) technology on science and society, as well as on the future of humankind. By interrogating ourselves on the situation and how it may evolve, we have attempted to provide a bird's eye view of the recent developments in AI and the expected outcomes and impact of those new AI technologies on our society and scientific research. Looking at the issue from various angles, we have come to observe and distinguish beneficial effects from problematic trends and identify specific areas of concern where the scientific community should get involved to reduce potential adverse outcomes. In some scenarios that many would still consider alarmist, if not ridicule, today, these outcomes are in the realm of global existential threats and, thus, in our opinion, require the most serious and careful consideration to avoid negative impacts.

In general, we live today in a transitional phase charac- terized by the accelerated development of entirely new AI-enabled technologies. The most recent and visible new reality we have dealt with in the immediate past is that of large language models. These systems have evolved from a niche computer science research to a ubiquitous tool in no more than a few years: in this case, the exponential growth of their performance, diffusion, and impact is all but debatable. In other application areas, the transformative effect of AI is less obvious but quite significant. It is hard to find examples of hu-man activities or production processes that are not changing under the pressure of the performance gains made possible by AI technology. To mention art –the highest bastion of human dominance–, we observe that while some areas of artistic production involving human embodiment are likely to remain

<figure><img src="image_14.png" /><figcaption>FIGURE 12. Map of risks from asteroid collisions in the Torino scale (adapted from ESA).</figcaption></figure>

undisturbed even in the long term (musical performance, ballet, theater), several others less directly connected with a human presence, such as sculpture, painting, and poetry, have already begun to mingle with AI helpers, in the form of software algorithms, human-computer interfaces, or robotic hardware.

The consequences for humanity of the booming trend of AI technology are difficult to predict. Still, disruptions are in order since our societies are not capable of adapting with the necessary speed to phenomena that show a continuous increase in their rate of change –which, as noted in Sec. IV, should not be mistaken for acceleration, but rather for jerk. This should motivate us to be fully aware of their unfolding, prepare for what awaits us, and possibly create effective amortizers.

Ultimately, as scientists, we cannot claim a role that we do not have, have never had, and are unlikely ever to be given, except perhaps in situations so compromised as to constitute an already inevitable defeat (in Hollywood disaster movies, the last-ditch attempt of government leaders to save the day by “calling the scientist” is a common cliche’). As scientists, however, we feel responsible for informing society about the impending threats: it is a moral obligation. A successful further raising of the bar, striving to bring our concerns to a level where policy changes can be proposed and supported, can only result from a higher awareness of the threats we face and a discussion fostered at multiple levels.

### A. A TORINO SCALE FOR AI

A framework for giving proper weight to different threats connected to the development of new AI technologies and their introduction into our societal system can be proposed in analogy with the Torino scale. The Torino scale is a num- ber from 0 to 10 that qualifies the severity of a threat of impact with Earth of an asteroid. Richard Binzel proposed it [408] to gauge how much attention should be paid to the frequent assessment of impact probability by near-Earth asteroids provided by dedicated monitoring systems and tele-scopes. The impact probability is a number that may change
significantly over time, depending on the time during which the object's trajectory is followed and measured. Since there are thousands of objects to keep track of, resources should be driven by the importance of improving the measurement of trajectories for objects that have the highest destruction potential. This can be quantified by the kinetic energy carried by the object at impact. The Torino scale is shown in Fig. 12.

A conceptually similar scale could be developed for AI threats. In our case, we cannot quantify the probability that any of the hypothesized threats could manifest themselves, nor the damage they would cause to humanity (or to smaller-scale environments and systems). Nevertheless, it is still helpful to paint a qualitative map where the perceived or assessed likelihood of outcomes is on the horizontal axis, and the vertical axis is the severity of the outcome. This may help us start a discussion on the hierarchy of those threats, which would guide the community toward paying more attention and studies to the ones that maximize the product of likelihood and severity.

With that purpose in mind, let us list below some of the potential threats posed by AI systems –present and future– we have discussed in this review, their scope, a level of likelihood of manifestation within a time scale of 10 years, between 10 and 30 years, and longer than 30 years, and a corresponding assessed level of severity.

To force ourselves into assessing in at least a semi-quantitative way these likelihoods and severity, we may use the following scale:

* VLL = very low likelihood ($p = 0.0001$ and below)

* LL = low likelihood ($p = 0.0001 - 0.01$)

* ML = medium likelihood ($p = 0.01 - 0.1$)

* HL = high likelihood ($p = 0.1 - 0.5$)

* VHL = very high likelihood ($p = 0.5$ and above)

For severity, we propose an assessment based on three levels:

* LS = low severity (effects limited geographically or to closed systems or topics, not going to affect humanity as a whole);

* MS = medium severity (effects that may cause major disruptions in our society, its functioning, or the well-being of large sectors of the population or the environment);

* HS = high severity (any effect that has the potential of becoming an existential threat).

We propose that HS threats should be assessed with a threat index of 10 if estimated to correspond to at least a low likelihood (LL, $p > 0.0001$) and 9 if classified as VLL-provided their likelihood is theoretically strictly larger than zero. The rationale is that even a remote chance of their occurrence should deserve our undivided attention. We will assign MS threats a threat index varying from 4 (VLL) to 8 (HL). For LS threats, we will assign threat indices from 0 to 3. Table 1 lists the ten considered threats connected with the development of AI technologies and their corresponding proposed levels of likelihood and severity in the short-term, medium-term, and long-term. Fig. 13 also shows the same data pictorially, highlighting the correspondence of threat index to likelihood

<figure><img src="image_15.png" /><figcaption>FIGURE 13. Visual representation of the threat index of ten different potential risks (labeled A through J, see Table 1) from AI deployment in the short, medium, and long term, as results from the authors' assessment of the probability and severity of each considered risk. See the text for details.</figcaption></figure>

and severity of each risk, and includes three threat indices per each considered risk, respectively, for the short, medium, and long term.

Below, we comment on the ten threats we singled out in Table 1 and offer some considerations that led to their quoted assessment of likelihood and severity.

1) (A) Acceleration of nocuous impacts on the environment
With all the benefits of a more connected world, where information flows fast and (mostly) free, has come a price to pay: computing is a significant contributor to CO₂ emissions today, and we can only expect its impact to grow larger and larger shortly. While new, more energy-efficient forms of computing (such as the one powered by neuromorphic processors; see Sec. IV) are likely to partly reduce this trend in the mid-to-long term, global warming is a direct negative outcome which we will have to cope with. A similar note can be made concerning other forms of pollution: for example, the reduced costs of production of goods, enabled by AI technology that eases manufacturing, and the added simplification of logistics and distribution, have flooded the markets with cheap goods; the textile industry is a prime example of this phenomenon, and a considerable source of concern, as the vast majority of the products have a short life cycle and end up in nonreducible waste. With further increases in the power of AI systems, this trend could soar in the short term. It seems reasonable to believe that countermeasures may, in the longer term, manage to tame or at least contain these nocuous outcomes; however, they constitute a very significant risk to the planet's habitability.

2) (B) Breach of trust in news and manipulation of public opinion

The creation of fake news and its use to pollute the information market has already started. Today, large language models can be easily customized to produce nocuous and malicious content. Recent notable uses of chatbots, automated distribution of false content in social media, and similar content-producing AI systems have been used, e.g., to bias electoral
<table><caption>TABLE 1. Summary of ten risks from artificial intelligence evaluated for their likelihood (L) and severity (S) in the scale VL (very low), L (low), M (medium), and high (H), and resulting global threat index (T) from the scale shown in Fig. 13. See the text for details.</caption><thead><tr><th>Effect</th><th colspan="3">Short term (&lt;10 y)</th><th colspan="3">Medium term (10-30 y)</th><th colspan="3">Long term (&gt;30 y)</th></tr></thead><tbody><tr><td>A. Acceleration of impact on environment</td><td>VHL</td><td>MS</td><td>8</td><td>HL</td><td>MS</td><td>7</td><td>ML</td><td>MS</td><td>6</td></tr><tr><td>B. Breach of trust in news</td><td>VHL</td><td>LS</td><td>3</td><td>ML</td><td>MS</td><td>6</td><td>LL</td><td>MS</td><td>5</td></tr><tr><td>C. Casting bases for authoritarian regimes</td><td>ML</td><td>MS</td><td>6</td><td>HL</td><td>MS</td><td>7</td><td>HL</td><td>MS</td><td>7</td></tr><tr><td>D. Disruption of stock market</td><td>LL</td><td>LS</td><td>1</td><td>ML</td><td>MS</td><td>6</td><td>VLL</td><td>MS</td><td>4</td></tr><tr><td>E. Educational system disruptions</td><td>HL</td><td>LS</td><td>3</td><td>ML</td><td>LS</td><td>2</td><td>LL</td><td>LS</td><td>1</td></tr><tr><td>F. Formation of AI weapons</td><td>HL</td><td>MS</td><td>7</td><td>HL</td><td>MS</td><td>7</td><td>VHL</td><td>MS</td><td>8</td></tr><tr><td>G. Growing divide in population</td><td>LL</td><td>MS</td><td>5</td><td>HL</td><td>MS</td><td>7</td><td>LL</td><td>MS</td><td>5</td></tr><tr><td>H. Handover of control to AI</td><td>ML</td><td>MS</td><td>6</td><td>LL</td><td>MS</td><td>5</td><td>VLL</td><td>MS</td><td>4</td></tr><tr><td>I. Inception of misaligned AGI</td><td>VLL</td><td>HS</td><td>9</td><td>VLL</td><td>HS</td><td>9</td><td>LL</td><td>HS</td><td>10</td></tr><tr><td>J. Job automation effects</td><td>HL</td><td>MS</td><td>7</td><td>ML</td><td>LS</td><td>2</td><td>LL</td><td>LS</td><td>1</td></tr></tbody></table>

results or undermine trust in vaccines. Further, image processing systems can today be used to create “deepfakes,” i.e., pictures and videos whose appearance is indistinguishable from authentic footage. While the pollution of the information highways has remained mostly confined to specific targets, its use to bias public opinion in case of regional conflicts or other large-scale geopolitical situations will create a general distrust of any source of information and news. In our evaluation, the likelihood of this outcome is very high. Indeed, it appears certain at the time of writing, yet we believe that effective countermeasures will likely become available and get widespread application in the medium term. Therefore, this threat is probably mostly connected to the transition period when powerful AI systems are available for generic applications and balancing measures have not yet been developed or agreed upon.

### 3) (C) Casting the basis of over-authoritarian regimes

Governments have long had the power to exert control over individuals through accumulating sensitive data on their behavior as consumers, acquiring imagery via surveillance cameras, and monitoring private communications, emails, and internet traffic. This concern has grown significantly with the advent of AI tools. Today, these tools amplify the potential for societies to devolve into dystopian regimes where citizens have no privacy and are constantly monitored by authorities. AI's ability to mine and summarize vast databases, identify individuals from images, track cell phones, and cross-correlate diverse information sources enormously enhances this control. Governments may exploit these capabilities to entrench their power, suppress dissent, and eliminate political opposition, tightening their grip progressively. While some of these practices are already in place to varying degrees across different countries, the situation could rapidly worsen as AI systems continue to improve. We consider these risks quite serious. Our evaluations in Table 1 reflect the urgency of bringing discussions on their mitigation to the highest levels.

### 4) (D) Disruption of the stock market by ultra-effective prediction of trends

Large companies have used AI tools in several ways in the last decade to improve stock trading performance. Although direct automatic trading is performed by exploiting machine learning algorithms that predict price movements, the main focus has been on algorithms that perform sentiment analysis to inform trading decisions or to minimize market impact, optimize portfolios, and manage risks [409]–[411]. In the future, AI systems can prove much more effective in integrating these tasks and may consequently become more autonomous in taking decisions. We are not aware of systems with the potential to achieve performances so high to disrupt the system, but this scenario cannot be excluded in the long term. Stock markets worldwide have built-in mechanisms to react to abnormal speculative activity, e.g., the volatility interruption mechanism or even stronger protocols that may suspend trading activities. Still, they may be too slow to cope with a superintelligent system. Again, the issue here seems to be the balance of new developments and countermeasures in a fast-developing situation. The likelihood of adverse effects may only increase in the short term when the severity of possible outcomes has been assessed as medium. In contrast, we foresee that the stock markets will become more robust to AI-powered intervention in the long term because most of the trading will become automated.

### 5) (E) Educational system disruption by AI availability and associated nocuous impacts

When ChatGPT3 was released, educators and academics worldwide immediately recognized the disruptive effect this new, readily available tool would have on the grading system of student homework. The demonstration that large language models may easily mimic the expressive level, jargon, and content of a high schooler and thus be used by the latter to drastically cut the time required to learn study material without any risk has created a difficult situation in many school systems. We consider this an example of negative side-effects of new technologies that constitute an otherwise positive outcome for society, and we can relate it to a similar
concern on the power of internet search engines and their
use in aid of homework assignments; that concern was born,
peaked, and subsided within less than a decade, as the school
system managed to adapt to the new situation. Indeed, such
are typically transient phenomena, which should be accepted
as a necessary “growing pain” in a fast-evolving scenario.

6) (F) Formation of AI-powered weapons

The production of new weapons and automated offensive
devices or automata generates several risks: the potential for
world domination by the country that first acquires a novel
technology offering higher power of destruction or other
forms of dominance is only one. Other risks, such as a drift
toward dystopic societies with ultra-authoritarian control by
robotic units, have been considered by various science fiction
works and movies. The risks' severity varies from medium to
high, but the likelihood is not easy to assess. In this situation,
we prefer to err on the side of caution, evaluating the odds as
medium in the short and medium term and high in the long
term.

7) (G) Growing a divide between integrated and anti-tech citizens

New technology brings profound consequences for our way
of living, which are welcomed by some and repelled by
others. The polarizing effect of new tools that significantly
empower humans with new abilities and skills can have divi-
sive effects when those tools are not available to everybody.
Still, the same effect arises when they present features that
pose the need for the user to decide for or against them.
Factors that may create a division are the fear of government
control or the potential harm to one's health.: we are familiar
with these themes, as we have recently witnessed similar
polarizations during the COVID-19 vaccination campaigns.
The development of BCIs and under-skin microchips are ex-
amples of technologies that may generate a two-tiered society,
with individuals who may reap its benefits and others who get
marginalized by not having access to them. This may have
long-term nocuous consequences on the general happiness,
integration, and democracy of our future society. We may
again classify this as a collateral downside of the growing
pains toward a more empowered version of humans. However,
the effect can be far-reaching and must concern us; in many
cases, mitigating strategies exist, but they must be planned.

8) (H) Handover of control of critical systems to AI

The economic benefits of automation create a general ten-
dency to substitute human decisions with algorithms. While
the latter may show lower failure rates (as it has already hap-
pened in some cases, such as diagnosis of pathologies from
clinical images) and therefore be overall beneficial in regular
operation, here we are concerned with the fact that such
a substitution, and the automation of decision procedures,
involves risks associated with misbehavior or malfunction not
considered in the design phase, and that produce catastrophic
effects. One example of this kind is the famous failure of an

early-warning system detecting the launch of ICBM missiles
by the US toward the Soviet Union. On September 26, 1983,
a rare alignment of the detection satellite with the sun and
with the field of view of the US territory caused the system to
report the launch of five missiles, when in fact, the detection
was an artifact due to Sun glare<fnref n="33" />. Only the presence of a
"human in the middle" (in the person of Colonel Stanislav
Petrov) and his doubts about an attack involving only five
missiles avoided a retaliation strike that would have likely
caused the start of a global nuclear war. In general, the risk of
overconfidence in automated decisions cannot be quantified if
not connected with specific systems under control. However,
we may still assess its potential severity as a medium overall.
The likelihood of manifestation of nocuous effects can be esti-
mated as medium in the short term and low/very low in longer
time scales as we gain confidence in our validation systems
and design more robust and reliable countermeasures.

9) (I) Inception of superintelligence with misaligned goals to those of humankind

This scenario has been discussed in some detail *supra* (Sec.
IV and Sec. V). Developing a system with super-human capa-
bilities in a broad enough range of tasks entails the possibility
that the system acquires the skill to reflect on itself and its
role in a wider context and develop objectives different from
those it was initially designed to address. This might cause it
to become hostile to humans or to pursue its goals in ways
that conflict with the existence of human life on our planet.
In both cases, this is an existential risk of the highest severity
and, therefore, regardless of perceived or assessed likelihood,
must be assigned very high values on our scale.

10) (J) Job automation effects on society

Several sources have discussed this global threat in detail;
see, in particular, the books by Martin Ford [412], [413]. It
is generally agreed that many tasks of repetitive nature –such
as the operation and driving of vehicles for the transporta-
tion of humans or goods, the delivery of goods to the end
users, intermediate processing tasks in production chains, and
many others– are going to withstand significant change with
the substitution of human operators with AI-driven systems,
fueled by the largely associated decrease in cost; this process
has already started, and it will only intensify in the short term.
Similarly, the AI impact on education will be explicit in the
widespread availability of real-time speech translation, the
acquired higher trust of large language model outputs, and the
automated production of educational audio/video products.
Many experts argue that humans will not be substituted but
rather empowered by AI assistance, which will have a bene-
ficial and qualifying effect on their work conditions. Others
note that AI will reduce the expertise required to perform
complex tasks by human workers, depreciating the value of
their experience and skills. It remains to be seen what the net

<footnote n="33">³³https://en.wikipedia.org/wiki/1983_Soviet_nuclear_false-alarm_incident</footnote>
outcome of the ongoing transition will be. Still, if we only look at the likelihood and severity of adverse outcomes in this area, we must assess these as a medium in both cases. This is likely also to be a transient effect, so we believe the impact will decrease in the long term to a low-severity one.

## B. DISCUSSION

The numerical evaluations of the risk of AI-related phenomena and their perceived likelihoods are manifestly subjective, and by themselves, each of them means very little. What matters is the big picture: by doing the exercise of asking ourselves the questions that must be answered to fill data into Table 1, we get to see how wide-ranging and far-reaching the potential consequences of the development of artificially intelligent systems are: we are led to consider that in every area of human activity, the introduction of AI may have serious drawbacks. We believe that by fostering an awareness of this fact and continuously monitoring the evolving status of each sub-area, we may successfully lay the basis for possible governance. As AI expert Stuart Russell aptly put it in his book "Human Compatible: AI and the Problem of Control" [403], recently, "everybody and their uncle" have been proposing supervisory organizations meant to oversee and control the development and the integration of AI in human activities. While he notes that those efforts were, in the beginning, ineffective due to their grass-root nature and their limited reach and impact, he admits in a post-scriptum that today, more organized and institutionalized entities are getting established, which have a chance to be able to influence AI developments and steer us more effectively away from unwanted, harmful situations. We certainly agree that such organizations are essential in our effort to mitigate or avert most potential dangers. Yet, we suggest that they may be insufficient to thwart the biggest ones, which may be out of reach of governance bodies. For example, we may consider the threat of AI-eased authoritarian involution: regimes will likely not collaborate with those organizations if they value the opportunity to get more robust control and power. Instead, they will likely feign their cooperation and diligence while secretly pursuing developments to reinforce control over society. A similar effect may happen with risk F in Table 1, the development of AI weapons. A likely result will be that countries that limit their technological developments by abiding by the rules set by intergovernmental organizations will have no means to gauge what happens worldwide and will be at a disadvantage compared to others.

Let us consider the light red and dark red boxes in Fig. 13, which correspond in our assessment to the biggest threats to humankind from AI development and deployment; not surprisingly, those threats are also the hardest to control and reduce. Also, not surprisingly, the risk that gets the highest score from our assessment is the development of a misaligned or malignant AGI; this is true regardless of what temporal horizon we consider. In other words, despite its very low likelihood, we are most concerned by that development as it is potentially the most harmful and destructive. This is due to its

far-reaching consequences and the absence of possible countermeasures *ex-post*. We can only hope that the strategies that have started to be implemented today [6], and the considerate action of individuals at the driver's seat of companies who lead development efforts of AGI systems, will help reduce that risk.

A word must also be spent on three other risks that reach in our assessment a level of light or dark orange (respectively, level 7 and 8) in the short term (i.e., within the next ten years). These are risk A (Acceleration of nocuous impact on the environment), risk F (Formation of AI weapons), and J (Job automation effects on society).

As far as the AI-development-induced nocuous impact on the environment is concerned, this is an effect that compounds the dire situation we are already facing in the world today, with soaring CO₂ emissions in the atmosphere, deforestation, and pollution of the environment. We believe that this risk has already become a reality. Although it is, in principle, never too late to fight the current situation, e.g., by embracing the idea of de-growth [414], [415]—a controlled downscaling of production and consumption in the wealthiest countries—, we observe that the 180-degree turn in the way the world's leading economies work that would be required is practically impossible to achieve.

The development and accumulation of AI-powered weapons is also a danger that is already clear and present. As discussed in Sec. V, weapons development already includes AI among its technological ingredients. In connection to risk C (the use of AI for creating ultra-authoritarian regimes), this is a significant threat that is hard to mitigate because of the lack of instruments and the opacity of the involved processes. At the time of writing, given the very uncertain geopolitical situation we are currently facing, it is one of our most serious concerns.

Finally, concerning the risk of social disruptions due to the automation of an increasing number of jobs, this is a situation that rich countries can indeed effectively act upon by foreseeing social amortizers, universal income, and other measures. It is a problem that can be solved by throwing money at it, but this requires careful planning and a favorable political situation to be implemented. So here is an AI-related threat to our society that is very concrete, quite likely to manifest itself in the short term, and one we can defuse if we make it more manifest and if we clarify what options are possible –the kind of objectives we are trying to aim at with the present text. On the other hand, there is ground to be pessimistic in this case, as there have already been situations where job automation caused significant disruption, e.g., at Amazon warehouses or McDonald's restaurants [413] –job loss, devaluation of human skills, worsening of working conditions– without government interventions to mitigate those effects.

### 1) An optimistic vision on the incoming revolution

We believe it is appropriate to conclude this long review on a positive note by echoing some of the points in the
previous sections about the enormous benefits that artificial intelligence systems and tools have brought to our modern society. Indeed, AI has profoundly revolutionized our lives within a mere decade, and the process is accelerating. It has significantly increased the living standards of human beings worldwide and across almost all income categories (although, unfortunately, this has come at least in part at the expense of most other animal species on this planet). For example, it is estimated that 85% of the world population today owns a smartphone, and an even more significant fraction can freely access a computer connected to the web. This provides humans with powerful AI-powered functionalities, such as finding one's way regardless of where one is on the planet, instantly translating text, generating computer code, constructing images or videos, solving problems with the help of LLMs, and learning from a vast database of documents and videos. Compiling a list of the benefits we have been reaping from AI technology indeed feels silly, no less than cavemen extolling the virtues of fire.

Within the boundaries of scientific research, some analysis of the present and foreseen future benefits of AI systems has been offered throughout Sec. IV. To summarize that discussion here, we note that in the scientific research areas we discussed in this document, as well as in others we did not comment on, the advantages brought by deeper analysis, more accessible and faster achievement of results, improved performance, higher precision that AI methods can provide over previous standards are so vast that it is hard even to start putting something on the other arm of the scale. This does not even consider all those situations where AI-powered technologies have generated genuine breakthroughs and paradigm-changing advancements that defy the definition of a pre-AI comparison point. We mention the following three:

1) AlphaFold and the Protein Folding Prediction, announced in December 2020 by DeepMind [416], is probably the most glaring example. Protein folding is a critical problem in biology, as proteins' enormously complex three-dimensional structure largely determines their function. AlphaFold demonstrated high precision in predicting the structure of specific protein structures, vastly outperforming traditional methods. This advancement bears profound implications for drug discovery, understanding diseases, and designing novel therapies.

2) Image recognition and computer vision have been thoroughly revolutionized by deep learning methods. Convolutional neural networks have achieved unprecedented accuracy in tasks ranging from object recognition to image classification and segmentation. These advancements have brought a wealth of benefits, e.g., to clinical medicine, improving the precision of the diagnosis of patients, satellite imagery analysis, space exploration, autonomous driving, and pure research in several areas.

3) NLP tools and large language models such as the series of OpenAI's generative pre-trained transformers have wholly transformed language processing tasks. They found application to a wide area of research tasks in many different fields. For example, in biomedical research, NLP techniques have been applied to the analysis of large volumes of biomedical literature, electronic health records, and clinical notes, extracting valuable information from unstructured text, enabling researchers to identify disease patterns, predict outcomes, and improve clinical decision [417], [418].

The above examples should suffice to make the point that in scientific research, the development of AI technologies has produced massively positive developments across the board, and it is likely to continue to do so at an increasing pace. Suppose we broaden our field of view to consider the impact of those research enhancements on our society. In that case, we see distinctly positive outcomes already at the consumer end, notably those connected to better, more effective medicine. In the future, we can only expect a broader impact, with a speed-up of several procedures and bottlenecks of current research and development in all areas of scientific investigation carrying over their effect to an improved quality of human life.

Human progress is hard to define from within the time scale of the human lifetime; its precise appraisal also requires the vantage point of *ex-post* considerations. Yet we entertain no doubts that artificial intelligence is a necessary, unavoidable evolutionary step in the march of humankind toward its future. It is a future that may await us with dystopian or idyllic features. We thus feel we are today at a critical juncture when that future can still be –at least in part– shaped by our decisions and policies. As the introduction notes, shaping may have unimaginable and far-reaching long-term consequences in this area of the universe. For those reasons, artificial intelligence and its development must be kept on the discussion table in all human occupations and at all levels for the years to come. As scientists, we feel we have the responsibility to inform those discussions and keep them rational and pragmatic. Still, we also need to ensure a place in the decision-making procedures to defend science-driven reasons and their impact on those decisions from the attack of irrational arguments. We are therefore happy to see that several recent spontaneous initiatives and organizations worldwide are starting to work in that direction together with governments and inter-governmental bodies. This document is our small contribution to inform those discussions.

## 2) Limitations and potential biases of this study

As a position paper, this work may not be as unbiased and objective as a typical research work. It represents the viewpoint of a group of experienced scholars who have collectively published numerous scientific works across various disciplines over the past forty years. We are expressing our perspectives on the impacts that artificial intelligence is having on our research fields. It is important to note that our backgrounds
may influence our viewpoints. However, our collective views could be valuable and interesting to the community. This aspect is both a limitation and a valuable asset of our work.

## ACKNOWLEDGMENT

A. Ustyuzhanin was supported by the Ministry of Education, Singapore, under its funding for the Research Centre of Excellence Institute for Functional Intelligence Materials and by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG3-RP-2022-028).

C. Casonato's research is supported in part by the activities of the MUR PNRR project FAIR - Future AI Research (PE00000013) funded by the Next Generation EU.

G. Olivato's contribution is realized with co-financing from the European Union - FSE REACT- EU, PON Ricerca e Innovazione 2014-2020.

Pietro Vischia's work was supported by the "Ramón y Cajal" program under Project No. RYC2021-033305-I funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.

The research of J.J. Nieto was supported by the Agencia Estatal de Investigacion (AEI) of Spain Grant PID2020-113275GB-100 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe", by the "European Union" and Xunta de Galicia, grant ED431C 2023/12 for Competitive Reference Research Groups (2023-2026).

The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.

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**TOMMASO DORIGO** was born in Venice, Italy in 1966. He received his Laurea in physics in 1995 and a Ph.D. in physics in 1999, both from the University of Padova.

From 1999 to 2000, he was a postdoctoral fellow at Harvard University and later a research grantee with the University of Padova and an INFN researcher. Since 2019 he is a First Researcher at the Padova section of INFN (Italian National Institute for Nuclear Physics), and since 2024 he is also a

WASP Guest Researcher at Luleå University of Technology (Sweden).

Dr. Dorigo is currently serving as President of the USERN organization (https://usern.org). He is also the founder of the MODE Collaboration (https://mode-collaboration.github.io). He was a member of the CDF experiment at Fermilab from 1996 to 2012 and is a member of the CMS experiment at CERN since 2001. He is an author of over 1700 scientific publications in peer-reviewed journals and has an H-index of 257, according to Google Scholar.

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**ARTEMI CERDÀ** is a Full Professor in Physical Geography at the University of València. Head of the Soil Erosion and Degradation Research Group.

He developed his Ph.D. in the soil hydrology of Mediterranean Soils (1989-1993). During his post-doctorate studies in The Netherlands, Israel, Bolivia, and the UK from 1994 to 1996, he researched (i) soil hydrology in arid land, (ii) the impact of climate on geomorphological processes, and (iii) fire and agriculture impact on soil sustainability.

His position as a researcher in the Desertification Research Centre shed light on the interaction of seeds, plants, and soil erosion in road embankments.

Since 2002, Prof. Cerdá has worked as an associate professor, got his accreditation as a Full Professor in 2009, and has worked since 2010 at the University of Valencia as a full professor. He developed the Soil Erosion and Degradation Research Group (SEDER) and the Soil Erosion Research Stations of Montesa and El Teularet. These studies focused mainly on soil erosion processes in agriculture and forest land.

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**GARY D. BROWN** is an associate professor of practice and faculty lead for the cyber policy concentration at the Bush School of Government and Public Service, Texas A&M University.

Previously, he taught cyber policy and law at the US National Defense University and Marine Corps University. He served for 24 years as an officer in the US Air Force, retiring as a colonel. After his military career, he spent three years with the International Committee of the Red Cross Washington

Delegation.

Colonel Brown was the first senior legal counsel for US Cyber Command, has authored a number of cyber policy and law articles, and was the on-camera legal expert for the documentary film Zero Days (2016). He has a law degree from the University of Nebraska and an LL.M. in international law from Cambridge University.

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**JOSEPH CIARROCHI**, PhD, is a professor at the Institute for Positive Psychology and Education at Australian Catholic University.

He has published more than 179 scientific journal articles and many books, including the widely acclaimed Emotional Intelligence in Every-day Life and The Weight Escape. His latest book is What makes you stronger: How to thrive in the face of uncertainty using Acceptance and Commitment Therapy.

Prof. Ciarrochi has served as the President of the Association for Contextual Behavior Science and was the first editor of the Journal of Contextual Behavioral Science. Prof. Ciarrochi has been honored with more than four million dollars in research funding. His work has been discussed on TV and radio, and in magazines and newspaper articles. He is ranked in the top 1% of scientists in the world across all disciplines.

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**CARLO CASONATO** is a Full Professor of comparative constitutional law at the Faculty of Law of the University of Trento.

After receiving a Fellowship from the Council of Europe (1993), he completed a PhD in Fundamental Rights and Freedoms in Comparative Law (1996). Visiting professor at the Chicago-Kent College of Law (IIT, 2003: course on Law and Bioethics), he has held teaching appointments or research periods at the Universities of Oxford

(2016, 2013, and 2012), Harvard (2009), Berkeley (2006), Toronto (2000), Lancaster (1999), Montréal (1995) and San Sebastian (1993). He is the Principal Investigator for numerous national and European research projects, and he is the author or editor of over 160 publications, including more than 20 books.

Prof. Casonato holds the Jean Monnet Chair in AI EU Law (T4F). He is the founder and chief editor of the BioLaw Journal, Director of the BioLaw Laboratory, and serves as the delegate of the rector and vice-president of the Ethics Committee for Research at the University of Trento. He is also a member of the Commission for Ethics and Integrity in Research at the CNR (Italian National Research Council).

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**MAURO DA LIO** is a Full Professor of mechanical systems at the University of Trento, Italy.

His earlier research activity was modeling, simulation, and optimal control of mechanical multi-body systems, particularly vehicle and spacecraft dynamics. More recently, his focus shifted to modeling human sensory-motor control with applications in health, robotics, and, mostly, intelligent vehicles. He was involved in several EU framework program 6 and 7 projects (PREVENT, SAFERIDER, INTERACTIVE, VERITAS, ADAPTIVE, NO-TREMOR, and SUN-RISE).

Prof. Da Lio was the coordinator of the EU Horizon 2020 Dreams4Cars Research and Innovation Action: a collaborative project in the robotics domain that aimed at increasing the cognition abilities of artificial driving agents using offline simulation mechanisms broadly inspired by the human dream state (learning of forward models and offline synthesis of inverse ones).
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**NICOLE D'SOUZA** is a researcher at the Systems Neural Engineering Laboratory at the University of California, Riverside. She is pursuing a Bachelor of Science in Neuroscience while also minoring in Data Science. Her academic journey is marked by a commitment to bridging the gap between neuroscience and data analytics.

She is the recipient of the prestigious Chancel-
lor's Research Fellowship, funding her research
between the Department of Bioengineering, UCR

and Stanford University. Currently, she is heading research at the Palo Alto Veterans Affair Medical Center and Stanford in applying machine learning algorithms to enhance diagnostic tools and treatment strategies for neurological disorders such as Alzheimer's Disease.

Ms. D'Souza serves as an editor in UCR's Undergraduate Research Journal, where she contributes to the dissemination of innovative research findings, and is a previous intern at the Society of Brain Mapping and Therapeutics, where she organized the Alzheimer's Disease and Dementia conference track.

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**NICOLAS R. GAUGER** is Full Professor and Chairholder for Scientific Computing and Director of the Computing Center (RHRZ) at University of Kaiserslautern-Landau (RPTU) as well as Principal Investigator of the SIVERT research training group (https://sivert.info) dealing with the algorithmic part of the proton Computed Tomography (pCT) project of the Bergen pCT Collaboration (https://indico.cern.ch/category/13882/).

His research interests are numerical optimiza-

tion, high-performance computing, machine learning, and pCT, amongst
other fields of application.

Prof. Gauger is also a member of the MODE Collaboration (https://mode-collaboration.github.io), a collaboration of physicists and computer scientists for the optimized co-design of hardware and software for future experiments in fundamental science.

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**STEVEN C. HAYES** received his Ph.D. in clinical psychology with a minor in experimental psychology from West Virginia University, Morgantown, WV, USA, in 1977, doing his internship at Brown University, Providence, RI in 1975-76.

He was an Assistant and Associate Professor of
Psychology from 1977 to 1986 at the University
of North Carolina at Greensboro, and from 1986
to 2023, a Full Professor of Psychology at the
University of Nevada, Reno. He is now Foundation

Professor Emeritus at UNR and President of the Institute for Better Health in Santa Rosa, CA, a 47-year-old charitable organization dedicated to excellence in mental and behavioral health care. As the author of 47 books and over 700 scientific articles, his career has focused on symbolic learning and how it relates to human suffering.

Dr. Hayes is a Fellow of the American Association for the Advancement of Science and has received lifetime achievement awards from the Association for Behavioral and Cognitive Therapy, and the Association for Psychological Science.

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**STEFAN G. HOFMANN** was born in Germany. He is the Alexander von Humboldt Professor at the Philipps University of Marburg (Germany), where he holds the LOEWE Spitzenprofessur for Translational Clinical Psychology (Germany). Hofmann studied psychology at the University of Marburg, Germany, where he received his B.A., M.S., and Ph.D.

He went to the USA in 1991 and worked at
Boston University from 1996 to 2023. Prior to that,

he was at SUNY Albany and, before that, at Stanford University. Hofmann is a leading expert in anxiety disorders, with a focus on psychotherapy. He has been the editor of Cognitive Therapy and Research and has been named a Highly Cited Researcher every year since 2015.

Prof. Hofmann is a fellow of many professional organizations, including AAAS, and he has received many awards, including the Aaron T. Beck Award for Significant and Enduring Contributions to the Field of Cognitive Therapy, the Lifetime Achievement Award by ABCT, and the Humboldt Research Award. He was an advisor to the DSM-5 and the DSM-5-TR. He has published more than 500 peer-reviewed scientific articles and 20 books. Some of his studies led to insights into the mechanism of treatment change, translating discoveries from neuroscience into clinical applications, emotion regulation, and cultural expressions of psychopathology.

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**ROBERT JOHANSSON** is an Associate Professor in Clinical Psychology in the Department of Psychology at Stockholm University.

He holds a PhD (2013) in Psychology and a PhD (2024) in Computer Science, both from Linköping University. His clinical research has focused on the development and evaluation of digital health interventions. Since 2017, his main focus of research has been in the area of Artificial General Intelligence (AGI), where he has focused on study-

ing the development of human-level cognitive capabilities with a particular AGI system, the Non-Axiomatic Reasoning System (NARS). The research is guided by Relational Frame Theory, a behavioral psychology approach to understanding human language and cognition. He is also a licensed psychologist with a broad range of interests, with a particular interest in emotion-focused psychotherapy models.

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**MARCUS LIWICKI** is chaired professor in Machine Learning and vice-rector for AI at Luleå University of Technology. He received his M.S. degree in Computer Science from the Free University of Berlin, Germany, in 2004, his PhD degree from the University of Bern, Switzerland, in 2007, and his habilitation degree at the Technical University of Kaiserslautern, Germany, in 2011.

His research interests include machine learning, pattern recognition, artificial intelligence, human-computer interaction, digital humanities, knowledge management, ubiquitous intuitive input devices, document analysis, and graph matching.

From October 2009 to March 2010, Prof. Liwicki visited Kyushu University (Fukuoka, Japan) as a research fellow (visiting professor), supported by the Japanese Society for the Promotion of Science. In 2015, at the young age of 32, he received the ICDAR Young Investigator Award, a bi-annual award acknowledging outstanding achievements in pattern recognition for researchers up to the age of 40.
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**FABIEN LOTTE** is a research director (DR2) at the Inria Center at the University of Bordeaux and the LaBRI, Talence, France.

He holds an MSc. (INSA Rennes, 2005), a PhD (INSA Rennes, 2008), and a Habilitation to Supervise Research (Univ. Bordeaux, 2016), all in Computer Science. Fabien Lotte is a specialist in Brain-Computer Interfaces (BCI) and Electroencephalography signal processing. He is a member of the editorial boards of several leading journals

on BCI and co-edited two books on the subject in 2016 and 2018.

Dr. Lotte notably coordinated or is coordinating the ANR REBEL project (2016-2019), the ANR Proteus project (2023-2027), the ERC Starting Grant BrainConquest project (2017-2022) or the ERC Proof-of-Concept project SPEARS (2024-2025). He has published more than 200 papers in this field, which have received more than 16 000 citations in total. He also gave more than 110 invited talks in 20 different countries. He is the laureate of the 2022 international USERN (Universal Science and Education Research Network) prize in formal science, the 2023 Lovelace-Babbage prize from the French Academy of Science in collaboration with the French Computer Science Society (SIF), and the Nature Award for Mentoring in Science 2023 (mid-career category).

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**PETER PARNES** was born in Malmö, Sweden, in 1971. He earned his PhD in computer science from Luleå University of Technology (LTU), Luleå, Sweden, in 1999. He holds there a docent title in media technology.

He has been a Full Professor in Pervasive and Mobile Computing at Luleå University of Technology since 2010, leading the ArcTech Learning Lab since 2020. His work experience includes various academic and research positions, including his role

as a researcher and lecturer at LTU before his current position, and several non-academic positions, such as founder and chief scientist of Marratech 1998-2007 and site engineering manager for Google Sweden 2007-2009.

Prof. Parnes has received the Curt Boström award for best PhD thesis (1999) and IT-person of the year (2015) for his ongoing work in attracting more girls into the STEM area and technical studies via his work with Luleå Makerspace. He has served as an expert in various subjects related to digitalization and learning with several Swedish authorities, such as the Swedish National Agency for Education, the Swedish Ethical Review Authority, the Swedish Research Council, and Sweden's innovation agency, VINNOVA.

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**JUAN J. NIETO** is a Full Professor at the Department of Statistics, Mathematical Analysis and Optimization and Academician. He was a Fulbright fellow at the University of Texas (USA), and his research areas include Mathematical Analysis, Differential Equations, Nonlinear Analysis, Biomedical Applications, and Digital Twins.

His most influential contributions to date are in the area of differential equations, and his research interests are in fractional calculus, equations under

uncertainty, and epidemiological models. He is one of the most cited mathematicians in the world according to different databases and has been listed in the Highly Cited Researchers uninterruptedly from 2014 to 2021. He was also in the World's Top 2% Researcher in 2022 by Stanford University. In 2010, he was among the scientists who had the most hot papers.

Dr. Nieto is presently Editor-in-Chief of the journal Fixed Point Theory and Algorithms for Sciences and of Differential Equations and Dynamical Systems.

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**GEORGE PERRY** is a neuroscientist and Professor of Neuroscience, Developmental and Regenerative Biology at the University of Texas at San Antonio. Perry is recognized in the field of Alzheimer's disease research, particularly for his work on oxidative stress.

He received his Bachelor of Arts degree in zoology from the University of California, Santa Barbara. After graduation, he studied at Scripps Institution of Oceanography, Hopkins Marine Station

of Stanford University, and the Marine Biological Laboratory at Woods Hole; he obtained his Ph.D. from the University of California at San Diego in Marine Biology under David Epel in 1979. He then received a postdoctoral fellowship in the Department of Cell Biology in the laboratories of William R. Brinkley, Joseph Bryan, and Anthony R. Means at Baylor College of Medicine, where he laid the foundation for his observations of cytoskeletal abnormalities.

Prof. Perry joined in 1982 the faculty of Case Western Reserve University, where he holds an adjunct appointment. He is dean of the College of Sciences and professor of biology at the University of Texas at San Antonio. He is distinguished as one of the top Alzheimer's disease researchers with over 1000 publications, one of the top 100 most-cited scientists in Neuroscience & Behavior, and one of the top 25 scientists in free radical research.

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**GIULIA OLIVATO** is a third-year PhD student at the University of Trento. In 2020, she graduated cum laude in Comparative, European and Transnational Law at the University of Trento.

She was a visiting PhD student at Technische Universität München (Germany) from October 2023 to April 2024. She completed a 6-month internship (November 2022 - June 2023) at a software company, where she experienced first-hand the benefits and drawbacks of implementing some

regulatory provisions of the AI Act.

Ms. Olivato received formal legal training in both Rome and Trento and has been admitted to the bar. She has presented at various conferences and webinars and has published one article and two conference proceedings. Additionally, one more conference proceeding and one book chapter are forthcoming.

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**ALICE PLEBE** received her BSc and MSc in Computer Science from the University of Catania, Italy, in 2014 and 2016, respectively. In 2021, she earned her Ph.D. in Information and Communication Technology from the University of Trento, Italy.

Following her doctorate, she was a postdoctoral fellow at the same institution from 2021 to 2024. As part of the EU Horizon 2020 Dreams4Cars Research and Innovation Action, her research focused

on exploring how the cognitive capabilities of the human brain can inform the design of artificial driving agents with human-like performance.

Dr. Plebe since 2024 has been a research fellow at University College London, working on large language models for trustworthy collaboration in multi-agent systems.
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**IDUPULAPATI M. RAO** was born in Mandadam Village, Amaravathi, Andhra Pradesh, India in 1951.

He received a B.Sc. degree in Chemistry, Botany, and Zoology in 1971 from Andhra University; an M.Sc. degree in Botany in 1973 from Bhopal University; and a Ph.D. degree in Botany (Plant Physiology) in 1978 from Sri Venkateswara University in India.

From 1979 to 1981, Dr. Rao was a Plant Physiologist with ICRISAT, India. From 1981 to 1989, he was a Research Associate/Assistant Specialist at the University of Illinois and the University of California-Berkeley, USA. From 1989 to 2016, he worked as a Plant Nutritionist/Physiologist at the International Center for Tropical Agriculture (CIAT), Cali, Colombia. Since 2017, he has been an International Consultant and Emeritus Scientist of CIAT. He is the author of more than 250 journal articles and more than 70 book chapters. His research interests include abiotic stress tolerance of crops, crop-livestock systems, and mitigation of climate change. He is on the Editorial Board of the journal Farming System. Dr. Rao was a recipient of the Outstanding Principal Staff Award in 2000 from CIAT and the Outstanding Research Publication Awards from CIAT in 1999, 2003, 2009, and 2011.

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**NIMA REZAEI** gained his medical degree (MD) from Tehran University of Medical Sciences and subsequently obtained an MSc in Molecular and Genetic Medicine and a PhD in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also spent a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital.

He is now the Full Professor of Immunology and Vice Dean of Research and Technologies at the School of Medicine, Tehran University of Medical Sciences, and the cofounder and Head of the Research Center for Immunodeficiencies. He is also the Founder of the Universal Scientific Education and Research Network (USERN).

Prof. Rezaei has already been the Director of more than two hundred research projects and has designed and participated in several international collaborative projects. Prof. Rezaei is the editor, editorial assistant, or editorial board member of more than fifty international journals. He has edited more than one hundred international books, has presented more than a thousand lectures/posters at congresses/meetings, and has published more than 1,500 scientific papers in international journals.

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**FREDRIK SANDIN** was born in Sweden in 1977. He is a full professor in machine learning at the Luleå University of Technology (LTU) since 2021.

He was an NFSR postdoctoral fellow at the University of Liège 2008-2009 and received his Ph.D in Physics from LTU and the Swedish Graduate School of Space Technology in 2007, both with a focus on computational physics. He completed his M.Sc. in 2001 with a diploma work in the ATLAS Collaboration at CERN.

His work focuses on brain-inspired machine learning and neuromorphic technologies for resource-efficient AI. He serves as PI for several projects in that area and coordinates the LTU research and education on sustainable AI. He was awarded the Gunnar Öquist Fellowship from the Kempe Foundations in 2014 and received a "New-Talents" award for original work in theoretical physics at the 2004 International School of Subnuclear Physics in Erice.

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**ANDREY USTYUZHANIN** is the Director of AI/ML research at Acronis (Singapore), a visiting research professor at the National University of Singapore, and a PI at the Institute of Functional Intelligent Materials. His research's primary priority is designing new Machine Learning methods and using them to solve tough scientific challenges, thus improving the fundamental understanding of our world.

His current research interest is the development of foundation generative models for the discovery of new dynamic materials. Before joining NUS, he worked with LHCb – one of the LHC experiments at CERN.

Dr. Ustyuzhanin's key projects include the efficiency improvement of online triggers at LHCb, speeding up BDT-based online processing, and developing the algorithm for tracking in scintillators optical fiber detectors, among others.

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**GIORGIO VALLORTIGARA** is a Professor of Neuroscience at the Centre for Mind/Brain Sciences of the University of Trento, Italy.

He has published more than 300 refereed papers that have received more than 29,000 citations (h-index=95 Google Scholar).

Prof. Vallortigara has also contributed to several book chapters and is the author of "Born Knowing" (MIT Press, 2021). He has been the recipient of the Geoffrey de St. Hilaire Prize for Ethology and a

doctorate honoris causa from the University of Ruhr in Germany.

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**PIETRO VISCHIA** was born in Padova, Italy in 1983. After his Bachelor (2006) and Master (2011) in physics from Università degli Studi di Padova, he obtained his Ph.D. in Physics in 2016 from Instituto Superior Técnico, Lisboa.

From 2016 to 2018, he was a postdoctoral fellow at Universidad de Oviedo and from 2018 to 2022 at Université catholique de Louvain, holding several prestigious doctoral and postdoctoral research grants. Since 2023, he is in his current position as a "Ramón y Cajal" Senior Researcher at Universidad de Oviedo and ICTEA and Adjunct Professor at IIT-Madras.

He is the coordinator and one of the cofounders of the MODE Collaboration (mode-collaboration.github.io), a collaboration of physicists and computer scientists for the optimized co-design of hardware and software for future experiments in fundamental science. He is also coordinator of the Machine Learning group of the CMS Collaboration, and he was the co-coordinator of the CERN Interexperimental Machine Learning Working Group from 2020 to 2024. Prof. Vischia has been a member of the CMS Collaboration at CERN since 2009. He is an author of over 1000 scientific publications in peer-reviewed journals and has an H-index of 116, according to Scopus. More on his research can be found at vischia.github.io/.
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**NILOUFAR YAZDANPANAH** is a researcher in immunology, neuroimmunology, and genetics. She received her MD from Tehran University of Medical Sciences.

In 2022, she received the title of “Best Undergraduate Student of the Year” at the Tehran University of Medical Sciences and also received the same award at Children’s Medical Center Hospital; in 2023, she was awarded the First-Rank Razi Medical Award, the most prestigious award

in medical sciences in Iran.

Currently, Dr. Yazdanpanah is a post-doc researcher at the Research Center for Immunodeficiencies, Children’s Medical Center of Tehran. Dr. Yazdanpanah has been the Executive Director of the Universal Scientific Education and Research Network (USERN) since 2022.

...