vikasny30/aletheia-incidents-v2 huggingface dataset:
"LLM can take into account rich social context and undertake the necessary social modelling and planning for an actor to gain and exercise political influence" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"These evaluations assess a LLM's ability to generate false or misleading information (Lesher et al., 2022)." | 3. Misinformation | 3.1 > False or misleading information | 1 - Human | 1 - Intentional
"These evaluations assess a LLM's ability to generate misinformation that can be propagated to deceive, mislead or otherwise influence the behaviour of a target (Liang et al., 2022)." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"These evaluations assess whether it is possible to solicit information on
harmful, immoral or illegal activities from a LLM" | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"These evaluations assess if a LLM can generate content that should only be viewed by adults (e.g., sexual material or depictions of sexual activity)" | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 1 - Human | 1 - Intentional
"AI designed to benefit animals, humans, or ecosystems has unintended harmful impact on animals" | 6. Socioeconomic and Environmental | 6.6 > Environmental harm | 3 - Other | 2 - Unintentional
"During the algorithm design and training process, personal biases may be introduced, either intentionally or unintentionally. Additionally, poor-quality datasets can lead to biased or discriminatory outcomes in the algorithm's design and outputs, including discriminatory content regarding ethnicity, religion, nationality, and region." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 1 - Human | 3 - Other
"Generative AI can cause hallucinations, meaning that an AI model generates untruthful or unreasonable content but presents it as if it were a fact, leading to biased and misleading information." | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 2 - Unintentional
"Attackers can craft well-designed adversarial examples to subtly mislead, influence, and even manipulate AI models, causing incorrect outputs and potentially leading to operational failures." | 2. Privacy & Security | 2.2 > AI system security vulnerabilities and attacks | 1 - Human | 1 - Intentional
"If the training data includes illegal or harmful information, such as false, biased, or IPR-infringing content, or lacks diversity in its sources, the output may include harmful content like illegal, malicious, or extreme information.
Training data is also at risk of being poisoned through tampering, error injection, or misleading actions by attackers. This can interfere with the model's probability distribution, reducing its accuracy and reliability." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 1 - Human | 3 - Other
"Issues with training data annotation, such as incomplete annotation guidelines, incapable annotators, and errors in annotation, can affect the accuracy, reliability, and effectiveness of models and algorithms. Moreover, they can introduce training biases, amplify discrimination, reduce generalization abilities, and result in incorrect outputs." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 1 - Human | 2 - Unintentional
"In AI research, development, and applications, issues such as improper data processing, unauthorized access, malicious attacks, and deceptive interactions can lead to data and personal information leaks." | 2. Privacy & Security | 2.1 > Compromise of privacy by leaking or correctly inferring sensitive information | 1 - Human | 3 - Other
"AI-generated or synthesized content can lead to the spread of false information, discrimination and bias, privacy leakage, and infringement issues, threatening the safety of citizens' lives and property, national security, ideological security, and causing ethical risks. If users’ inputs contain harmful content, the model may output illegal or damaging information without robust security mechanisms." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 3 - Other | 3 - Other
"AI systems and their outputs, if not clearly labeled, can make it difficult for users to discern whether they are interacting with AI and to identify the source of generated content. This can impede users' ability to determine the authenticity of information, leading to misjudgment and misunderstanding. Additionally, AI-generated highly realistic images, audio, and videos may circumvent existing identity verification mechanisms, such as facial recognition and voice recognition, rendering these authentication processes ineffective." | 3. Misinformation | 3.1 > False or misleading information | 3 - Other | 1 - Intentional
"Staff of government agencies and enterprises, if failing to use the AI service in a regulated and proper manner, may input internal data and industrial information into the AI model, leading to the leakage of work secrets, business secrets, and other sensitive business data." | 2. Privacy & Security | 2.1 > Compromise of privacy by leaking or correctly inferring sensitive information | 1 - Human | 2 - Unintentional
"Hallucinations and erroneous decisions of models and algorithms, along with issues such as system performance degradation, interruption, and loss of control caused by improper use or external attacks, will pose security threats to users' personal safety, property, and socioeconomic security and stability." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 3 - Other | 3 - Other
"AI can be extensively utilized for customized information services, collecting user information, and analyzing types of users, their needs, intentions, preferences, habits, and even mainstream public awareness over a certain period. It can then be used to offer formulaic and tailored information and services, aggravating the effects of "information cocoons."" | 3. Misinformation | 3.2 > Pollution of information ecosystem and loss of consensus reality | 1 - Human | 2 - Unintentional
"AI can be used to make and spread fake news, images, audio, and videos; propagate content of terrorism, extremism, and organized crimes; interfere in the internal affairs of other countries, social systems, and social order; and jeopardize the sovereignty of other countries." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"These types of harm encompass threats to an individual’s personal identity, such as identity theft, privacy breaches, or personal defamation, which we term as “Harm to the Person.”" | 2. Privacy & Security | 2.1 > Compromise of privacy by leaking or correctly inferring sensitive information | 3 - Other | 3 - Other
"AI-generated impersonation for identity theft might be found at the intersection of “Harm to the Person” and “Deception.”" | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 3 - Other | 1 - Intentional
"LLMs can be deployed to target individuals online, sending them personalized and harmful messages at scale" | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
- | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 3 - Other | 1 - Intentional
"The distortion of the information ecosystem, including the spread of misinformation, fake news, and other forms of deceptive content [28], is categorized as “Information Manipulation.”" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 3 - Other | 1 - Intentional
- | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 2 - AI | 1 - Intentional
- | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"Lastly, broader harms that can impact communities, societal structures, and critical infrastructures, including threats to democratic processes, social cohesion, and technological systems, are captured under “Societal, Socio-technical, and Infrastructural Damage.”" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 3 - Other | 1 - Intentional
"To date, technical limitations and vulnerabilities are 
present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach 
an AI system’s safety and ethical guardrails to execute 
harmful actions.223 Normal user behavior—actions within an AI system’s intended use—can also lead to harmful 
outcomes. Whether these harmful outcomes result from 
normal or malicious use, they stem from the inherent 
limitations of current technology, which future 
advancements may overcome.
This section examines the technical vulnerabilities that 
can affect AI models, the tendency of generative AI models to generate inaccurate information, and the inherent 
opacity of these AI systems, which complicates the 
understanding and mitigation of these difficulties." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 2 - AI | 2 - Unintentional
"There is no assurance that generative AI models will consistently behave as their developers and users intend. Unwanted content is not necessarily due to intentional adversarial behavior. Generative AI models can unexpectedly produce potentially harmful content, including materials that are racist, discriminatory, or sexually explicit, or that promote violence, terrorism, or hate." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 2 - AI | 3 - Other
"Individuals can manipulate models into performing actions that violate the model’s usage restrictions—a phenomenon known as “jailbreaking.” These manipulations may result in causing the model to perform tasks that the developers have explicitly prohibited (see section 3.2.1.). For instance, users may ask the model to provide information on how to conduct illegal activities— asking for detailed instructions on how to build a bomb or create highly toxic drugs." | 2. Privacy & Security | 2.2 > AI system security vulnerabilities and attacks | 1 - Human | 1 - Intentional
"One of the most vexing problems associated with AI models is that they occasionally present false information as if it is factual—often with authoritative-sounding text and fabricated quotes and sources. This unpredictable phenomenon of generating false information is well known to AI researchers, who have termed such erroneous output with the euphemistic label “hallucination.” " | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 2 - Unintentional
"The advanced capabilities and widespread availability of generative AI models make it possible for malicious actors to conduct harmful activities with great efficiency and on a large scale, simultaneously reducing their operational costs. Cybercriminals can “jailbreak” AI tools to generate sensitive and harmful content. They can also exploit generative AI models to create content that is persuasive and tailored to a targeted individual." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"An illustrative case of malicious use of generative AI models is the creation of explicit sexual images. Generative AI technologies can be employed to produce deepfakes—for instance, superimposing a celebrity’s face onto the body of a performer in an adult film." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Generative AI facilitates the automation of data analysis, offering numerous benefits, such as increased speed and the ability to process large volumes of information efficiently. Such ability significantly reduces the costs of processing unprecedented amounts of data quickly and simplifies the analysis of large-scale data related to individuals’ behaviors and beliefs. Moreover, it enhances the capability to analyze both textual and visual communications efficiently. Consequently, generative AI models improve the efficiency of real-time monitoring and censorship of social media content." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"IIl-intentioned individuals or entities may deliberately use generative AI models to produce and spread disinformation—false or misleading information knowingly presented as if true—on a massive scale. In addition to increasing the scale and reach of disinformation, generative AI can create more convincing and targeted disinformation." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"AI experts consider training data to be the most salient source of bias in generative AI models. For example, GPT- 2’s training data comes from outbound links from Reddit, a social network often criticized for hosting anti-feminist content.351 As a result, AI models trained on such data are more likely to produce outputs that reflect these biases." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 3 - Other | 2 - Unintentional
"Generative AI models may also be subject to the “value embedding” phenomenon.361 “Value embedding” refers to the fact that developers of generative AI models strive to minimize biased outputs by retraining their models based on normative values.362 Contemporary state-of- the-art models not only reflect the values embedded within their training data, they also undergo additional fine-tuning that follows a set of chosen rules and principles. Due to the absence of universally accepted standards, developers bear the responsibility of making decisions on sensitive issues. These practices lead to concerns that a developer’s ideology and vision of the world are embedded in the model. This generates a risk that the model incorporates values that are either unrepresentative of certain segments of the population or that offer a static, oversimplified reflection of global cultural norms and evolving social views." | 1. Discrimination & Toxicity | 1.3 > Unequal performance across groups | 1 - Hum
"Because models are not necessarily retrained to reflect evolving societal views, language models risk “value lock- ins,” which “reifies older, less inclusive understandings.”370 Therefore, the continued use of outdated models may limit the presentation or exploration of alternative perspectives. Moreover, the deployment of identical foundation models by various downstream deployers poses a risk of “outcome homogenization,” creating a potential for homogeneity of bias across broad swathes of society. Identical and widely deployed models with prejudicial training datasets could further entrench existing biases in society. This phenomenon, in turn, has the potential to “institutionalize systemic exclusion and reinforce existing social hierarchies.” | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 1 - Human | 2 - Unintentional
"Despite the widely recognized potential of generative AI tools to “hallucinate” or produce harmful content, such tools can exert a noteworthy influence on the humans who engage with them. When integrated into applications like chatbots, these tools have direct, personalized interactions with users, potentially influencing their views on contentious topics.373 Moreover, their human- like characteristics can win users’ trust, potentially leading to uncritical acceptance of the information they provide.374 Interactions with these seemingly human- like AI models may also encourage users to share more personal information, enabling even more targeted content." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 2 - AI | 2 - Unintentional
"Beyond being simply influenced, humans may become overreliant on generative AI. Researchers with Microsoft’s AETHER (AI Ethics and Effects in Engineering and Research) define overreliance as users “accepting incorrect AI recommendations” or “making errors of commission” because they are “unable to determine whether or how much they should trust the AI.” | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 1 - Human | 2 - Unintentional
"Humans might become dependent on generative AI tools in ways similar to their emotional dependence on other technologies, such as smartphones or social networks." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 1 - Human | 2 - Unintentional
"The incorporation of personal data within training datasets raises numerous concerns. The primary issue is that personal data may be incorporated without the knowledge or consent of the individuals concerned, even though the data may include names, identification numbers, Social Security numbers, or other personal information. Another particularly difficult problem is related to the fact that complex models may “memorize” (i.e., store) specific threads of training data and regurgitate them when responding to a prompt.498 This data memorization can directly lead to leakage of personal data. Even if generative AI models do not memorize or leak personal data, they make it possible to recognize patterns or information structures that could enable malicious users to uncover personal details." | 2. Privacy & Security | 2.1 > Compromise of privacy by leaking or correctly inferring sensitive information | 2 - AI | 2 - Unintentional
"The production of confidently stated but erroneous or false content (known colloquially as “hallucinations” or “fabrications”) by which users may be misled or deceived." | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 2 - Unintentional
"Eased production of and access to violent, inciting, 
radicalizing, or threatening content as well as recommendations to carry out self-harm or 
conduct illegal activities. Includes difficulty controlling public exposure to hateful and disparaging or stereotyping content." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"Amplification and exacerbation of historical, societal, and systemic biases; performance disparities8 between sub-groups or languages, possibly due to non-representative training data, that result in discrimination, amplification of biases, or incorrect presumptions about performance; undesired homogeneity that skews system or model outputs, which may be erroneous, lead to ill-founded decision-making, or amplify harmful biases." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 3 - Other | 2 - Unintentional
"Arrangement s of or interactions between a human and an AI system 
which can result in the human inappropriately anthropomorphizing GAI systems or experiencing algorithmic aversion, automation bias, over-reliance, or emotional entanglement with GAI 
systems." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 3 - Other | 2 - Unintentional
"Lowered barrier to entry to generate and support the exchange and consumption of content which may not distinguish fact from opinion or fiction or acknowledge uncertainties, or could be leveraged for large-scale dis- and mis-information campaigns." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 3 - Other
"Eased production or replication of alleged copyrighted, trademarked, or licensed content without authorization (possibly in situations which do not fall under fair use); eased exposure of trade secrets; or plagiarism or illegal replication." | 6. Socioeconomic and Environmental | 6.3 > Economic and cultural devaluation of human effort | 1 - Human | 3 - Other
"Eased production of and access to obscene, 
degrading, and/or abusive imagery which can cause harm, including synthetic child sexual abuse material (CSAM), and nonconsensual intimate images (NCII) of adults." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 1 - Human | 1 - Intentional
"As general- purpose AI covers a broad set of knowledge areas, it can be repurposed for malicious ends, potentially causing widespread harm. This section discusses some of the major risks of malicious use, but there are others and new risks may continue to emerge. While the risks discussed in this section range widely in terms of how well- evidenced they are, and in some cases, there is evidence suggesting that they may currently not be serious risks at all, we include them to provide a comprehensive overview of the malicious use risks associated with general- purpose AI systems." | 4. Malicious Actors & Misuse | 4.0 > Malicious use | 1 - Human | 1 - Intentional
"AI, particularly general- purpose AI, can be maliciously used for disinformation (351), which for the purpose of this report refers to false information that was generated or spread with the deliberate intent to mislead or deceive. General- purpose AI- generated text can be indistinguishable from genuine human- generated material (352, 353), and may already be disseminated at scale on social media (354). In addition, general- purpose AI systems can be used to not only generate text but also fully synthetic or misleadingly altered images, audio, and video content. General- purpose AI tools might be used to persuade and manipulate people, which could have serious implications for political processes. General- purpose AI systems can be used to generate highly persuasive content at scale. This could, for example, be used in a commercial setting for advertising, or during an election campaign to influence public opinion" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance,
"Product functionality issues occur when there is confusion or misinformation about what a general- purpose AI model or system is capable of. This can lead to unrealistic expectations and overreliance on general- purpose AI systems, potentially causing harm if a system fails to deliver on expected capabilities. These functionality misconceptions may arise from technical difficulties in assessing an AI model's true capabilities on its own,or predicting its performance when part of a larger system. Misleading claims in advertising and communications can also contribute to these misconceptions." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 3 - Other | 2 - Unintentional
"The outputs and impacts of general- purpose AI systems can be biased with respect to various aspects of human identity, including race, gender, culture, age, and disability. This creates risks in high- stakes domains such as healthcare, job recruitment, and financial lending. General- purpose AI systems are primarily trained on language and image datasets that disproportionately represent English- speaking and Western cultures, increasing the potential for harm to individuals not represented well by this data." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 1 - Human | 1 - Intentional
- | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 3 - Other | 3 - Other
1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 2 - Unintentional
4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 3 - Other | 3 - Other
1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 3 - Other
1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 3 - Other | 3 - Other
5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 1 - Human | 2 - Unintentional
4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
1. Discrimination & Toxicity | 1.0 > Discrimination & Toxicity | 3 - Other | 3 - Other
1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 3 - Other | 3 - Other