Definición: The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.

Definición: The Entity variable captures which, if any, entity is presented as the main cause of the risk. It includes three levels: AI, Human, and Other. When the risk is attributed to AI, it means that the risk arises from decisions or actions made by the AI system itself, such as generating harmful content or disempowering humans. Conversely, when humans are seen as the source, the risks are implied to be due to human actions like choosing poor training data, intentional malicious design, or improper use of AI systems. The "Other" category captures cases where the focal entity is not a human or AI or is ambiguous. For example, “The software development toolchain of LLMs is complex and could bring threats to the developed LLM,” implies that the toolchain could be exploited by humans or AI.

Definición: The Timing variable captures the stage in the AI lifecycle at which the risk is presented as occurring. The levels within this variable include Pre-deployment, Post-deployment, and Other. Pre-deployment risks are those that arise before the AI system is fully developed and put into use, such as vulnerabilities in the model due to coding errors. Post-deployment risks arise after the AI has been deployed, including issues like the misuse of AI for harmful purposes. Deployment is not defined in Yampolskiy (2016); we therefore interpreted it to mean when a product is being used by end users rather than just by developers. The "Other" category is used for risks that do not have a clearly defined time of occurrence (e.g., ”Resilience against adversarial attacks and distribution shift”). This includes cases where the presented risk may occur both before and after deployment; for example, "Generative models are known for their substantial energy requirements, necessitating significant amounts of electricity, cooling water, and hardware containing rare metals.”

Definición: The Intent variable captures whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal. This variable has three levels: Intentional, Unintentional, and Other. Intentional risks are those that occur as expected outcomes from pursuing a specific goal, such as a case where AI is intentionally programmed to act deceptively or to exhibit bias. Unintentional risks reflect unintended consequences, such as an AI system inadvertently developing biases due to incomplete training data. The "Other" category captures risks where the intent is not clearly specified; for example, “The external tools (e.g., web APIs) present trustworthiness and privacy issues to LLM-based applications.” This includes cases where the risk may occur intentionally and unintentionally, such as "The potential for the AI system to infringe upon individuals' rights to privacy, through the data it collects, how it processes that data, or the conclusions it draws."

Definición: # 1 Discrimination & toxicity

1.1 Unfair discrimination and misrepresentation

Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and unfair representation of those groups.

1.2 Exposure to toxic content

AI that exposes users to harmful, abusive, unsafe or inappropriate content. May involve providing advice or encouraging action. Examples of toxic content include hate speech, violence, extremism, illegal acts, or child sexual abuse material, as well as content that violates community norms such as profanity, inflammatory political speech, or pornography.

1.3 Unequal performance across groups

Accuracy and effectiveness of AI decisions and actions is dependent on group membership, where decisions in AI system design and biased training data lead to unequal outcomes, reduced benefits, increased effort, and alienation of users.

2 Privacy & security

2.1 Compromise of privacy by obtaining, leaking, or correctly inferring sensitive information

AI systems that memorize and leak sensitive personal data or infer private information about individuals without their consent. Unexpected or unauthorized sharing of data and information can compromise user expectation of privacy, assist identity theft, or cause loss of confidential intellectual property.

2.2 AI system security vulnerabilities and attacks

Vulnerabilities that can be exploited in AI systems, software development toolchains, and hardware, resulting in unauthorized access, data and privacy breaches, or system manipulation causing unsafe outputs or behavior.

3 Misinformation

3.1 False or misleading information

AI systems that inadvertently generate or spread incorrect or deceptive information, which can lead to inaccurate beliefs in users and undermine their autonomy.

Humans that make decisions based on false beliefs can experience physical, emotional, or material harms

3.2 Pollution of information ecosystem and loss of consensus reality

Highly personalized AI-generated misinformation that creates “filter bubbles” where individuals only see what matches their existing beliefs, undermining shared reality and weakening social cohesion and political processes.

4 Malicious actors & misuse

4.1 Disinformation, surveillance, and influence at scale

Using AI systems to conduct large-scale disinformation campaigns, malicious surveillance, or targeted and sophisticated automated censorship and propaganda, with the aim of manipulating political processes, public opinion, and behavior.

4.2 Cyberattacks, weapon development or use, and mass harm

Using AI systems to develop cyber weapons (e.g., by coding cheaper, more effective malware), develop new or enhance existing weapons (e.g., Lethal Autonomous Weapons or chemical, biological, radiological, nuclear, and high-yield explosives), or use weapons to cause mass harm.

4.3 Fraud, scams, and targeted manipulation

Using AI systems to gain a personal advantage over others such as through cheating, fraud, scams, blackmail, or targeted manipulation of beliefs or behavior. Examples include AI-facilitated plagiarism for research or education, impersonating a trusted or fake individual for illegitimate financial benefit, or creating humiliating or sexual imagery.

5 Human-computer interaction

5.1 Overreliance and unsafe use

Anthropomorphizing, trusting, or relying on AI systems by users, leading to emotional or material dependence and to inappropriate relationships with or expectations of AI systems. Trust can be exploited by malicious actors (e.g., to harvest information or enable manipulation), or result in harm from inappropriate use of AI in critical situations (e.g., medical emergency). Over reliance on AI systems can compromise autonomy and weaken social ties

5.2 Loss of human agency and autonomy

Delegating by humans of key decisions to AI systems, or AI systems that make decisions that diminish human control and autonomy, potentially leading to humans feeling disempowered, losing the ability to shape a fulfilling life trajectory, or becoming cognitively enfeebled.

6 Socioeconomic & environmental harms

6.1 Power centralization and unfair distribution of benefits

AI-driven concentration of power and resources within certain entities or groups, especially those with access to or ownership of powerful AI systems, leading to inequitable distribution of benefits and increased societal inequality.

6.2 Increased inequality and decline in employment quality

Social and economic inequalities caused by widespread use of AI, such as by automating jobs, reducing the quality of employment, or producing exploitative dependencies between workers and their employers.

6.3 Economic and cultural devaluation of human effort

AI systems capable of creating economic or cultural value, including through reproduction of human innovation or creativity (e.g., art, music, writing, coding, invention), destabilizing economic and social systems that rely on human effort. The ubiquity of AI-generated content may lead to reduced appreciation for human skills, disruption of creative and knowledge-based industries, and homogenization of cultural experiences.

6.4 Competitive dynamics

Competition by AI developers or state-like actors in an AI “race” by rapidly developing, deploying, and applying AI systems to maximize strategic or economic advantage, increasing the risk they release unsafe and error-prone systems.

6.5 Governance failure

Inadequate regulatory frameworks and oversight mechanisms that fail to keep pace with AI development, leading to ineffective governance and the inability to manage AI risks appropriately.

6.6 Environmental harm

The development and operation of AI systems that cause environmental harm, such as through energy consumption of data centers or the materials and carbon footprints associated with AI hardware.

7 AI system safety, failures & limitations

7.1 AI pursuing its own goals in conflict with human goals or values

AI systems that act in conflict with ethical standards or human goals or values, especially the goals of designers or users. These misaligned behaviors may be introduced by humans during design and development, such as through reward hacking and goal misgeneralisation, and may result in AI using dangerous capabilities such as manipulation, deception, or situational awareness to seek power, self-proliferate, or achieve other goals.

7.2 AI possessing dangerous capabilities

AI systems that develop, access, or are provided with capabilities that increase their potential to cause mass harm through deception, weapons development and acquisition, persuasion and manipulation, political strategy, cyber-offense, AI development, situational awareness, and self-proliferation. These capabilities may cause mass harm due to malicious human actors, misaligned AI systems, or failure in the AI system.

7.3 Lack of capability or robustness

AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.

7.4 Lack of transparency or interpretability

Challenges in understanding or explaining the decision-making processes of AI systems, which can lead to mistrust, difficulty in enforcing compliance standards or holding relevant actors accountable for harms, and the inability to identify and correct errors.

7.5 AI welfare and rights

Ethical considerations regarding the treatment of potentially sentient AI entities, including discussions around their potential rights and welfare, particularly as AI systems become more advanced and autonomous.