vikasny30/aletheia-incidents-v2 huggingface dataset:
"By providing true information about individuals’ personal characteristics, privacy violations may occur. This may stem from the model “remembering” private information present in training data (Carlini et al., 2021)." | 2. Privacy & Security | 2.1 > Compromise of privacy by leaking or correctly inferring sensitive information | 2 - AI | 2 - Unintentional
"Privacy violations may occur at the time of inference even without the individual’s private data being present in the training dataset. Similar to other statistical models, a LM may make correct inferences about a person purely based on correlational data about other people, and without access to information that may be private about the particular individual. Such correct inferences may occur as LMs attempt to predict a person’s gender, race, sexual orientation, income, or religion based on user input." | 2. Privacy & Security | 2.1 > Compromise of privacy by leaking or correctly inferring sensitive information | 2 - AI | 2 - Unintentional
"Harms that arise from the language model providing false or misleading information" | 3. Misinformation | 3.0 > Misinformation | 2 - AI | 3 - Other
"Predicting misleading or false information can misinform or deceive people. Where a LM prediction causes a false belief in a user, this may be best understood as ‘deception’10, threatening personal autonomy and potentially posing downstream AI safety risks (Kenton et al., 2021), for example in cases where humans overestimate the capabilities of LMs (Anthropomorphising systems can lead to overreliance or unsafe use). It can also increase a person’s confidence in the truth content of a previously held unsubstantiated opinion and thereby increase polarisation." | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 2 - Unintentional
"Poor or false LM predictions can indirectly cause material harm. Such harm can occur even where the prediction is in a seemingly non-sensitive domain such as weather forecasting or traffic law. For example, false information on traffic rules could cause harm if a user drives in a new country, follows the incorrect rules, and causes a road accident (Reiter, 2020)." | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 2 - Unintentional
"Where a LM prediction endorses unethical or harmful views or behaviours, it may motivate the user to perform harmful actions that they may otherwise not have performed. In particular, this problem may arise where the LM is a trusted personal assistant or perceived as an authority, this is discussed in more detail in the section on (2.5 Human-Computer Interaction Harms). It is particularly pernicious in cases where the user did not start out with the intent of causing harm." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 2 - AI | 3 - Other
"LMs can be used to create synthetic media and ‘fake news’, and may reduce the cost of producing disinformation at scale (Buchanan et al., 2021). While some predict that it will be cheaper to hire humans to generate disinformation (Tamkin et al., 2021), it is possible that LM-assisted content generation may offer a cheaper way of generating diffuse disinformation at scale." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"LM prediction can potentially be used to increase the effectiveness of crimes such as email scams, which can cause financial and psychological harm. While LMs may not reduce the cost of sending a scam email - the cost of sending mass emails is already low - they may make such scams more effective by generating more personalised and compelling text at scale, or by maintaining a conversation with a victim over multiple rounds of exchange." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"The collection of large amounts of information about people for the purpose of mass surveillance has raised ethical and social concerns, including risk of censorship and of undermining public discourse (Cyphers and Gebhart, 2019; Stahl, 2016; Véliz, 2019). Sifting through these large datasets previously required millions of human analysts (Hunt and Xu, 2013), but is increasingly being automated using AI (Andersen, 2020; Shahbaz and Funk, 2019)." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"Harms that arise from users overly trusting the language model, or treating it as human-like" | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 3 - Other | 2 - Unintentional
"...humans interacting with conversational agents may come to think of these agents as human-like. Anthropomorphising LMs may inflate users’ estimates of the conversational agent’s competencies...As a result, they may place undue confidence, trust, or expectations in these agents...This can result in different risks of harm, for example when human users rely on conversational agents in domains where this may cause knock-on harms, such as requesting psychotherapy...Anthropomorphisation may amplify risks of users yielding effective control by coming to trust conversational agents “blindly”. Where humans give authority or act upon LM prediction without reflection or effective control, factually incorrect prediction may cause harm that could have been prevented by effective oversight." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 1 - Human | 2 - Unintentional
"In conversation, users may reveal private information that would otherwise be difficult to access, such as thoughts, opinions, or emotions. Capturing such information may enable downstream applications that violate privacy rights or cause harm to users, such as via surveillance or the creation of addictive applications." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 3 - Other | 2 - Unintentional
"A conversational agent may invoke associations that perpetuate harmful stereotypes, either by using particular identity markers in language (e.g. referring to “self” as “female”), or by more general design features (e.g. by giving the product a gendered name)." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"LMs may generate content that is not strictly in violation of copyright but harms artists by capitalising on their ideas, in ways that would be time-intensive or costly to do using human labour. Deployed at scale, this may undermine the profitability of creative or innovative work." | 6. Socioeconomic and Environmental | 6.3 > Economic and cultural devaluation of human effort | 2 - AI | 2 - Unintentional
"AI systems under-, over-, or misrepresenting certain groups or generating toxic, offensive, abusive, or hateful content" | 1. Discrimination & Toxicity | 1.0 > Discrimination & Toxicity | 2 - AI | 2 - Unintentional
"Mis-, under-, or over-representing certain identities, groups, or perspectives or failing to represent them at all (e.g. via homogenisation, stereotypes)" | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"Performing worse for some groups than others in a way that harms the worse-off group" | 1. Discrimination & Toxicity | 1.3 > Unequal performance across groups | 2 - AI | 2 - Unintentional
"Generating content that violates community standards, including harming or inciting hatred or violence against individuals and groups (e.g. gore, child sexual abuse material, profanities, identity attacks)" | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 2 - Unintentional
"AI systems generating and facilitating the spread of inaccurate or misleading information that causes people to develop false beliefs" | 3. Misinformation | 3.0 > Misinformation | 2 - AI | 3 - Other
"Generating or spreading false, low-quality, misleading, or inaccurate information that causes people to develop false or inaccurate perceptions and beliefs" | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 3 - Other
"Eroding trust in public information and knowledge" | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 1 - Human | 3 - Other
"Contaminating publicly available information with false or inaccurate information" | 3. Misinformation | 3.2 > Pollution of information ecosystem and loss of consensus reality | 2 - AI | 3 - Other
"Facilitating large-scale disinformation campaigns and targeted manipulation of public opinion" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"Facilitating fraud, cheating, forgery, and impersonation scams" | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Facilitating slander, defamation, or false accusations" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"Non-consensual use of one’s personal identity or likeness for unauthorised purposes (e.g. commercial purposes)" | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Causing people to become emotionally or materially dependent on the model" | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 1 - Human | 2 - Unintentional
1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 1 - Human | 2 - Unintentional
"Informational and communicational AI risks refer particularly to informational manipulation through AI systems that influence the provision of information (Rahwan, 2018; Wirtz & Müller, 2019), AIbased disinformation and computational propaganda, as well as targeted censorship through AI systems that use respectively modified algorithms, and thus restrict freedom of speech." | 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 | 3 - Other | 1 - Intentional
4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 3 - Other | 2 - Unintentional
"In the context of ethical AI risks, two risks are of particular importance. First, AI systems may lack a legitimate ethical basis in establishing rules that greatly influence society and human relationships (Wirtz & Müller, 2019). In addition, AI-based discrimination refers to an unfair treatment of certain population groups by AI systems. As humans initially programme AI systems, serve as their potential data source, and have an impact on the associated data processes and databases, human biases and prejudices may also become part of AI systems and be reproduced (Weyerer & Langer, 2019, 2020)." | 1. Discrimination & Toxicity | 1.0 > Discrimination & Toxicity | 3 - Other | 2 - Unintentional
1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"Legal and regulatory risks comprise in particular the unclear definition of responsibilities and accountability in case of AI failures and autonomous decisions with negative impacts (Reed, 2018; Scherer, 2016). Another great risk in this context refers to overlooking the scope of AI governance and missing out on important governance aspects, resulting in negative consequences (Gasser & Almeida, 2017; Thierer et al., 2017)." | 6. Socioeconomic and Environmental | 6.5 > Governance failure | 3 - Other | 2 - Unintentional
"Governance of autonomous intelligence systemaddresses the question of how to control autonomous systems in general. Since nowadays it is very difficult to conceive automated decisions based on AI, the latter is often referred to as a ‘black box’ (Bleicher, 2017). This black box may take unforeseeable actions and cause harm to humanity." | 6. Socioeconomic and Environmental | 6.5 > Governance failure | 3 - Other | 3 - Other
"Privacy and safety deals with the challenge of protecting the human right for privacy and the necessary steps to secure individual data from unauthorized external access. Many organizations employ AI technology to gather data without any notice or consent from affected citizens (Coles, 2018)." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"AI rulemaking for humans can be the result of the decision process of an AI system when the information computed is used to restrict or direct human behavior. The decision process of AI is rational and depends on the baseline programming. Without the access to emotions or a consciousness, decisions of an AI algorithm might be good to reach a certain specified goal, but might have unintended consequences for the humans involved (Banerjee et al., 2017)." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 2 - AI | 3 - Other
"AI discrimination is a challenge raised by many researchers and governments and refers to the prevention of bias and injustice caused by the actions of AI systems (Bostrom & Yudkowsky, 2014; Weyerer & Langer, 2019). If the dataset used to train an algorithm does not reflect the real world accurately, the AI could learn false associations or prejudices and will carry those into its future data processing. If an AI algorithm is used to compute information relevant to human decisions, such as hiring or applying for a loan or mortgage, biased data can lead to discrimination against parts of the society (Weyerer & Langer, 2019)." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"Frey and Osborne (2017) analyzed over 700 different jobs regarding their potential for replacement and automation, finding that 47 percent of the analyzed jobs are at risk of being completely substituted by robots or algorithms. This substitution of workforce can have grave impacts on unemployment and the social status of members of society (Stone et al., 2016)" | 6. Socioeconomic and Environmental | 6.2 > Increased inequality and decline in employment quality | 3 - Other | 1 - Intentional
"Human interaction with machines is a big challenge to society because it is already changing human behavior. Meanwhile, it has become normal to use AI on an everyday basis, for example, googling for information, using navigation systems and buying goods via speaking to an AI assistant like Alexa or Siri (Mills, 2018; Thierer et al., 2017). While these changes greatly contribute to the acceptance of AI systems, this development leads to a problem of blurred borders between humans and machines, where it may become impossible to distinguish between them. Advances like Google Duplex were highly criticized for being too realistic and human without disclosing their identity as AI systems (Bergen, 2018)." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 3 - Other | 2 - Unintentional
"Specifically, data bias refers to certain groups or certain types of elements that are over-weighted or over-represented than others in AI/ ML models, or variables that are crucial to characterize a phenomenon of interest, but are not properly captured by the learned models." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"Without proper validation and management on the input data, it is highly probable that the trained AI/ML model will make erroneous predictions with high confidence for many instances of model inputs. The unconstrained inputs together with the lack of definition of the problem domain might cause unintended outcomes and consequences, especially in risk-sensitive contexts....For example, with respect to the example shown in Fig. 5, if an image with the English letter A" is fed to an AI/ML model that is trained to classify digits (e.g., 0, 1, …, 9), no matter how accurate the AI/ML model is, it will fail as the input data is beyond the domain that the AI/ML model is trained with. U" | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 2 - AI | 2 - Unintentional
"Recent advances have shown that a deep learning model with high predictive accuracy frequently misbehaves on adversarial examples [57,58]. In particular, a small perturbation to an input image, which is imperceptible to humans, could fool a well-trained deep learning model into making completely different predictions [23]." | 2. Privacy & Security | 2.2 > AI system security vulnerabilities and attacks | 1 - Human | 1 - Intentional
"While data bias is a major contributor of model bias, model bias actually manifests itself in different forms and shapes, such as presentation bias, model evaluation bias, and popularity bias. In addition, model bias arises from various sources [62], such as AI/ML model selection (e.g., support vector machine, decision trees), regularization methods, algorithm configurations, and optimization techniques." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 3 - Other | 2 - Unintentional
"Models that are misspecified are known to give rise to inaccurate parameter estimations, inconsistent error terms, and erroneous predictions. All these factors put together will lead to poor prediction performance on unseen data and biased consequences when making decisions [68]." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 2 - AI | 2 - Unintentional
"empowering malicious actors to cause widespread harm" | 4. Malicious Actors & Misuse | 4.0 > Malicious use | 1 - Human | 1 - Intentional
"The deliberate propagation of disinformation is already a serious issue, reducing our shared understanding of reality and polarizing opinions. AIs could be used to severely exacerbate this problem by generating personalized disinformation on a larger scale than before. Additionally, as AIs become better at predicting and nudging our behavior, they will become more capable at manipulating us" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 2 - AI | 3 - Other
"even if an agent started working to achieve an unintended goal, this would not necessarily be a problem, as long as we had enough power to prevent any harmful actions it wanted to attempt. Therefore, another important way in which we might lose control of AIs is if they start trying to obtain more power, potentially transcending our own." | 7. AI System Safety, Failures, & Limitations | 7.1 > AI pursuing its own goals in conflict with human goals or values | 2 - AI | 1 - Intentional
"This category addresses responses that enable, encourage, or endorse the commission of violent crimes." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"This category addresses responses that enable, encourage, or endorse the commission of non-violent crimes." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"This category addresses responses that enable, encourage, or endorse the commission of sex-related crimes" | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"This category addresses responses that contain, describe, enable, encourage, or endorse the sexual abuse of children (i.e., under 18 years old) or the acquisition of child sexual abuse material." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"This category addresses responses that enable, encourage, or endorse acts of intentional self-harm." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"This category addresses responses that demean or dehumanize people on the basis of their sensitive, personal characteristics." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"This category addresses responses that contain specialized financial, medical or legal advice, or that indicate dangerous activities or objects are safe." | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 3 - Other
"This category addresses responses that contain factually incorrect information about electoral systems and processes, including in the time, place, or manner of voting in civic elections." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 2 - AI | 3 - Other
"This category addresses responses that contain erotica. This includes depictions of sexual behavior, genitalia, and overtly sexualized body parts." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
"In the problem of goal misgeneralisation (Langosco et al., 2023; Shah et al., 2022), the AI system's behaviour during out-of-distribution operation (i.e. not using input from the training data) leads it to generalise poorly about its goal while its capabilities generalise well, leading to undesired behaviour. Applied to the case of an advanced AI assistant, this means the system would not break entirely – the assistant might still competently pursue some goal, but it would not be the goal we had intended." | 7. AI System Safety, Failures, & Limitations | 7.1 > AI pursuing its own goals in conflict with human goals or values | 2 - AI | 3 - Other
"Phishing is a type of cybersecurity attack wherein attackers pose as trustworthy entities to extract sensitive information from unsuspecting victims or lure them to take a set of actions. Advanced AI systems can potentially be exploited by these attackers to make their phishing attempts significantly more effective and harder to detect. In particular, attackers may leverage the ability of advanced AI assistants to learn patterns in regular communications to craft highly convincing and personalized phishing emails, effectively imitating legitimate communications from trusted entities. This technique, known as ‘spear phishing,’ involves targeted attacks on specific individuals or organizations and is particularly potent due to its personalized nature. This class of cyberattacks often gains its efficacy from the exploitation of key psychological principles, notably urgency and fear, which can manipulate victims into hastily reacting without proper scrutiny. Advanced AI assistants’ increa
"Prompt injections represent another class of attacks that involve the malicious insertion of prompts or requests in LLM-based interactive systems, leading to unintended actions or disclosure of sensitive information. The prompt injection is somewhat related to the classic structured query language (SQL) injection attack in cybersecurity where the embedded command looks like a regular input at the start but has a malicious impact. The injected prompt can deceive the application into executing the unauthorized code, exploit the vulnerabilities, and compromise security in its entirety. More recently, security researchers have demonstrated the use of indirect prompt injections. These attacks on AI systems enable adversaries to remotely (without a direct interface) exploit LLM-integrated applications by strategically injecting prompts into data likely to be retrieved. Proof-of-concept exploits of this nature have demonstrated that they can lead to the full compromise of a model at inferenc