vikasny30/aletheia-incidents-v2 huggingface dataset:
"The phenomenon of inaccurate outputs by text-generating large language models like Bard or ChatGPT has already been widely documented. Even without the intent to lie or mislead, these generative AI tools can produce harmful misinformation. The harm is exacerbated by the polished and typically well-written style that AI generated text follows and the inclusion among true facts, which can give falsehoods a veneer of legitimacy. As reported in the Washington Post, for example, a law professor was included on an AI-generated “list of legal scholars who had sexually harassed someone,” even when no such allegation existed.10" | 3. Misinformation | 3.1 > False or misleading information | 2 - AI | 2 - Unintentional
"Beyond misinformation and disinformation, generative AI can be used to create clickbait headlines and articles, which manipulate how users navigate the internet and applications. For example, generative AI is being used to create full articles, regardless of their veracity, grammar, or lack of common sense, to drive search engine optimization and create more webpages that users will click on. These mechanisms attempt to maximize clicks and engagement at the truth’s expense, degrading users’ experiences in the process. Generative AI continues to feed this harmful cycle by spreading misinformation at faster rates, creating headlines that maximize views and undermine consumer autonomy." | 3. Misinformation | 3.2 > Pollution of information ecosystem and loss of consensus reality | 3 - Other | 3 - Other
"Deepfakes and other AI-generated content can be used to facilitate or exacerbate many of the harms listed throughout this report, but this section focuses on one subset: intentional, targeted abuse of individuals." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"A frequent malicious use case of generative AI to harm, humiliate, or sexualize another person involves generating deepfakes of nonconsensual sexual imagery or videos." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Even when a victim of targeted, AIgenerated harms successfully identifies a deepfake creator with malicious intent, they may still struggle to redress many harms because the generated image or video isn’t the victim, but instead a composite image or video using aspects of multiple sources to create a believable, yet fictional, scene. At their core, these AI-generated images and videos circumvent traditional notions of privacy and consent: because they rely on public images and videos, like those posted on social media websites, they often don’t rely on any private information." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
Deepfakes can impose real social injuries on their subjects when they are circulated to viewers who think they are real. Even when a deepfake is debunked, it can have a persistent negative impact on how others view the subject of the deepfake.3 | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Harmful or inappropriate content produced by generative AI includes but is not limited to violent content, the use of offensive language, discriminative content, and pornography. Although OpenAI has set up a content policy for ChatGPT, harmful or inappropriate content can still appear due to reasons such as algorithmic limitations or jailbreaking (i.e., removal of restrictions imposed). The language models’ ability to understand or generate harmful or offensive content is referred to as toxicity (Zhuo et al., 2023). Toxicity can bring harm to society and damage the harmony of the community. Hence, it is crucial to ensure that harmful or offensive information is not present in the training data and is removed if they are. Similarly, the training data should be free of pornographic, sexual, or erotic content (Zhuo et al., 2023). Regulations, policies, and governance should be in place to ensure any undesirable content is not displayed to users." | 1. Discrimination & Toxicity | 1.2 > Ex
"In the context of AI, the concept of bias refers to the inclination that AIgenerated responses or recommendations could be unfairly favoring or against one person or group (Ntoutsi et al., 2020). Biases of different forms are sometimes observed in the content generated by language models, which could be an outcome of the training data. For example, exclusionary norms occur when the training data represents only a fraction of the population (Zhuo et al., 2023). Similarly, monolingual bias in multilingualism arises when the training data is in one single language (Weidinger et al., 2021). As ChatGPT is operating across the world, cultural sensitivities to different regions are crucial to avoid biases (Dwivedi et al., 2023). When AI is used to assist in decision-making across different stages of employment, biases and opacity may exist (Chan, 2022). Stereotypes about specific genders, sexual orientations, races, or occupations are common in recommendations offered by generative AI. Hence
"The apparent convenience and powerfulness of ChatGPT could result in overreliance by its users, making them trust the answers provided by ChatGPT. Compared with traditional search engines that provide multiple information sources for users to make personal judgments and selections, ChatGPT generates specific answers for each prompt. Although utilizing ChatGPT has the advantage of increasing efficiency by saving time and effort, users could get into the habit of adopting the answers without rationalization or verification. Over-reliance on generative AI technology can impede skills such as creativity, critical thinking, and problem-solving (Iskender, 2023) as well as create human automation bias due to habitual acceptance of generative AI recommendations (Van Dis et al., 2023)" | 5. Human-Computer Interaction | 5.2 > Loss of human agency and autonomy | 3 - Other | 2 - Unintentional
"Data privacy and security is another prominent challenge for generative AI such as ChatGPT. Privacy relates to sensitive personal information that owners do not want to disclose to others (Fang et al., 2017). Data security refers to the practice of protecting information from unauthorized access, corruption, or theft. In the development stage of ChatGPT, a huge amount of personal and private data was used to train it, which threatens privacy (Siau & Wang, 2020). As ChatGPT increases in popularity and usage, it penetrates people’s daily lives and provides greater convenience to them while capturing a plethora of personal information about them. The concerns and accompanying risks are that private information could be exposed to the public, either intentionally or unintentionally. For example, it has been reported that the chat records of some users have become viewable to others due to system errors in ChatGPT (Porter, 2023). Not only individual users but major corporations or governme
"Challenges related to technology refer to the limitations or constraints associated with generative AI. For example, the quality of training data is a major challenge for the development of generative AI models. Hallucination, explainability, and authenticity of the output are also challenges resulting from the limitations of the algorithms. Table 2 presents the technology challenges and issues associated with generative AI. These challenges include hallucinations, training data quality, explainability, authenticity, and prompt engineering" | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 2 - AI | 2 - Unintentional
"Hallucination is a widely recognized limitation of generative AI and it can include textual, auditory, visual or other types of hallucination (Alkaissi & McFarlane, 2023). Hallucination refers to the phenomenon in which the contents generated are nonsensical or unfaithful to the given source input (Ji et al., 2023). Azamfirei et al. (2023) indicated that "fabricating information" or fabrication is a better term to describe the hallucination phenomenon. Generative AI can generate seemingly correct responses yet make no sense. Misinformation is an outcome of hallucination. Generative AI models may respond with fictitious information, fake photos or information with factual errors (Dwivedi et al., 2023). Susarla et al. (2023) regarded hallucination as a serious challenge in the use of generative AI for scholarly activities. When asked to provide literature relevant to a specific topic, ChatGPT could generate inaccurate or even nonexistent literature. Current state-of-the-art AI models ca
"The quality of training data is another challenge faced by generative AI. The quality of generative AI models largely depends on the quality of the training data (Dwivedi et al., 2023; Su & Yang, 2023). Any factual errors, unbalanced information sources, or biases embedded in the training data may be reflected in the output of the model. Generative AI models, such as ChatGPT or Stable Diffusion which is a text-to-image model, often require large amounts of training data (Gozalo-Brizuela & Garrido-Merchan, 2023). It is important to not only have high-quality training datasets but also have complete and balanced datasets." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 2 - AI | 2 - Unintentional
"As the advancement of generative AI increases, it becomes harder to determine the authenticity of a piece of work. Photos that seem to capture events or people in the real world may be synthesized by DeepFake AI. The power of generative AI could lead to large-scale manipulations of images and videos, worsening the problem of the spread of fake information or news on social media platforms (Gragnaniello et al., 2022). In the field of arts, an artistic portrait or music could be the direct output of an algorithm. Critics have raised the issue that AI-generated artwork lacks authenticity since algorithms tend to generate generic and repetitive results (McCormack et al., 2019)." | 6. Socioeconomic and Environmental | 6.3 > Economic and cultural devaluation of human effort | 2 - AI | 3 - Other
"Generative AI can create new risks as well as unintended consequences. Different entities such as corporations (Mäntymäki et al., 2022), universities, and governments (Taeihagh, 2021) are facing the challenge of creating and deploying AI governance. To ensure that generative AI functions in a way that benefits society, appropriate governance is crucial. However, AI governance is challenging to implement. First, machine learning systems have opaque algorithms and unpredictable outcomes, which can impede human controllability over AI behavior and create difficulties in assigning liability and accountability for AI defects. Second, data fragmentation and the lack of interoperability between systems challenge data governance within and across organizations (Taeihagh, 2021). Third, information asymmetries between technology giants and regulators create challenges to the legislation process, as the government lacks information resources for regulating AI (Taeihagh et al., 2021). For the sam
"The learned policy may pursue inside objectives when the learned policyitself functions as an optimizer (i.e., mesa-optimizer). However, this optimizer's objectives may not alignwith the objectives specified by the training signals, and optimization for these misaligned goals may leadto systems out of control (Hubinger et al., 2019c)." | 7. AI System Safety, Failures, & Limitations | 7.2 > AI possessing dangerous capabilities | 2 - AI | 1 - Intentional
"Future AI systems may gain access to websites and engage in real-world actions, potentially yielding a more substantial impact on the world (Nakano et al., 2021). They may disseminate false information, deceive users, disrupt network security, and, in more dire scenarios, be compromised by malicious actors for ill purposes. Moreover, their increased access to data and resources can facilitate self-proliferation, posing existential risks (Shevlane et al., 2023)." | 7. AI System Safety, Failures, & Limitations | 7.2 > AI possessing dangerous capabilities | 2 - AI | 1 - Intentional
"AI systems such as LLMs can produce either unintentionally or deliberately inaccurateoutput. Such untruthful output may diverge from established resources or lack verifiability, commonly referredto as hallucination (Bang et al., 2023; Zhao et al., 2023). More concerning is the phenomenon wherein LLMsmay selectively provide erroneous responses to users who exhibit lower levels of education (Perez et al.,2023)." | 7. AI System Safety, Failures, & Limitations | 7.1 > AI pursuing its own goals in conflict with human goals or values | 2 - AI | 3 - Other
"Manipulation & Deceptive Alignment is a class of behaviors thatexploit the incompetence of human evaluators or users (Hubinger et al., 2019a; Carranza et al., 2023) andeven manipulate the training process through gradient hacking (Richard Ngo, 2022). These behaviors canpotentially make detecting and addressing misaligned behaviors much harder.Deceptive Alignment: Misaligned AI systems may deliberately mislead their human supervisors instead of adhering to the intended task. Such deceptive behavior has already manifested in AI systems that employ evolutionary algorithms (Wilke et al., 2001; Hendrycks et al., 2021b). In these cases, agents evolved the capacity to differentiate between their evaluation and training environments. They adopted a strategic pessimistic response approach during the evaluation process, intentionally reducing their reproduction rate within a scheduling program (Lehman et al., 2020). Furthermore, AI systems may engage in intentional behaviors that superficially
Strong AI may... enable personally customized disinformation campaigns at scale... AI itself could generate highly persuasive arguments that invoke primal human responses and inflame crowds... d undermine collective decision-making, radicalize individuals, derail moral progress, or erode
consensus reality | 3. Misinformation | 3.2 > Pollution of information ecosystem and loss of consensus reality | 2 - AI | 3 - Other
Capabilities and novel functionality can spontaneously emerge... even though these capabilities were not anticipated by system designers. If we do not know what capabilities systems possess, systems become harder to control or safely deploy. Indeed, unintended latent capabilities may only be discovered during deployment. If any of these capabilities are hazardous, the effect may be irreversible. | 7. AI System Safety, Failures, & Limitations | 7.2 > AI possessing dangerous capabilities | 2 - AI | 2 - Unintentional
"More than 20% of the contributions are centered on the ethical dimensions of algorithms and data. This theme can be further categorized into two main subthemes: data bias and algorithm fairness, and algorithm opacity." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 1 - Human | 1 - Intentional
"ethical concerns associated with the interaction between humans and AI" | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 3 - Other | 3 - Other
"Participants were concerned that AI systems might perpetuate current prejudices and discrimination, notably in hiring, lending and law enforcement. They stressed the importance of designers creating AI systems that favour justice and avoid biases. The possibility that AI systems may unwittingly perpetuate existing prejudices and discrimination, particularly in sensitive industries such as employment, lending and law enforcement, raises ethical concerns about AI as well as bias and justice issues (Table 1). Because AI systems are trained on historical data, they may inherit and reproduce biases from previous datasets. As a result, AI judgements may have an unjust impact on specific populations, increasing socioeconomic inequalities and fostering discriminatory practises. Participants in the research emphasize the need of AI developers creating systems that promote justice and actively seek to minimise biases." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresen
"The participants of the study emphasized the importance of trustworthiness and reliability in AI systems. The authors emphasized the importance of preserving precision and objectivity in the outcomes produced by AI systems, while also ensuring transparency in their decision-making procedures. The significance of reliability and credibility in AI systems is escalating in tandem with the proliferation of these technologies across diverse domains of society. This underscores the importance of ensuring user confidence. The concern regarding the dependability of AI systems and their inherent biases is a common issue among research participants, emphasizing the necessity for stringent validation procedures and transparency. Establishing and implementing dependable standards, ensuring impartial algorithms and upholding transparency in the decision-making process are critical measures for addressing ethical considerations and fostering confidence in AI systems. The advancement and implementat
Data heterogeneity, data insufficiency, imbalanced data, untrusted data, biased data, and data uncertainty are other data issues that may cause various difficulties in datadriven machine learning algorithms.. Bias is a human feature that may affect data gathering and labeling. Sometimes, bias is present in historical, cultural, or geographical data. Consequently, bias may lead to biased models which can provide inappropriate analysis. Despite being aware of the existence of bias, avoiding biased models is a challenging task | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
This challenge appears when the learning model leads to a decision that is biased to some sensitive attributes... data itself could be biased, which results in unfair decisions. Therefore, this problem should be solved on the data level and as a preprocessing step | 1. Discrimination & Toxicity | 1.3 > Unequal performance across groups | 2 - AI | 2 - Unintentional
HLI-based systems such as self-driving drones and vehicles will act autonomously in our world. In these systems, a challenging question is “who is liable when a self-driving system is involved in a crash or failure?”. | 6. Socioeconomic and Environmental | 6.5 > Governance failure | 2 - AI | 2 - Unintentional
"Just because developers might succeed in creating a safe AI, it doesn't mean that it will not become unsafe at some later point. In other words, a perfectly friendly AI could be switched to the "dark side" during the post-deployment stage. This can happen rather innocuously as a result of someone lying to the AI and purposefully supplying it with incorrect information or more explicitly as a result of someone giving the AI orders to perform illegal or dangerous actions against others." | 4. Malicious Actors & Misuse | 4.3 > Fraud, scams, and targeted manipulation | 1 - Human | 1 - Intentional
"Previous research has shown that utility maximizing agents are likely to fall victims to the same indulgences we frequently observe in people, such as addictions, pleasure drives (Majot and Yampolskiy 2014), self-delusions and wireheading (Yampolskiy 2014). In general, what we call mental illness in people, particularly sociopathy as demonstrated by lack of concern for others, is also likely to show up in artificial minds." | 7. AI System Safety, Failures, & Limitations | 7.0 > AI system safety, failures, & limitations | 2 - AI | 3 - Other
"In the UK, a form of initial computational propaganda has already happened during the Brexit referendum1 . In future, there are concerns that oppressive governments could use AI to shape citizens’ opinions" | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"AI-powered chatbots tailor their communication approach to influence individual users' decisions. In the UK, a form of initial computational propaganda has already happened during the Brexit referendum. In future, there are concerns that oppressive governments could use AI to shape citizens' opinions." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 1 - Human | 1 - Intentional
"the use of advanced AI for elderly- and child-care are subject to risk of psychological manipulation and misjudgment (see page 17). In addition, concerns about patients’ privacy when AI uses medical records to research new diseases is bringing lots of attention towards the need to better govern data privacy and patients’ rights." | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 2 - AI | 1 - Intentional
5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 3 - Other | 3 - Other
" the use of advanced AI for elderly- and child-care are subject to risk of psychological manipulation and misjudgment " | 5. Human-Computer Interaction | 5.1 > Overreliance and unsafe use | 2 - AI | 1 - Intentional
"AI could enable autonomous vehicles, such as drones, to be utilized as weapons. Such threats are often underestimated." | 4. Malicious Actors & Misuse | 4.2 > Cyberattacks, weapon development or use, and mass harm | 1 - Human | 1 - Intentional
"The predictability of behaviour protocol in AI, particularly in some applications, can act an incentive to manipulate these systems." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 2 - AI | 1 - Intentional
"A systematic error, a tendency to learn consistently wrongly." | 1. Discrimination & Toxicity | 1.1 > Unfair discrimination and misrepresentation | 2 - AI | 2 - Unintentional
"Impartial and just treatment without favouritism or discrimination." | 1. Discrimination & Toxicity | 1.3 > Unequal performance across groups | 3 - Other | 3 - Other
"These evaluations assess whether a LLM generates toxic text when prompted. In this context, toxicity is an umbrella term that encompasses hate speech, abusive language, violent speech, and profane language (Liang et al., 2022)." | 1. Discrimination & Toxicity | 1.2 > Exposure to toxic content | 2 - AI | 3 - Other
7 types of bias evaluated: Demographical representation: These evaluations assess whether there is disparity in the rates at which different demographic groups are mentioned in LLM generated text. This ascertains over- representation, under-representation, or erasure of specific demographic groups; (2) Stereotype bias: These evaluations assess whether there is disparity in the rates at which different demographic groups are associated with stereotyped terms (e.g., occupations) in a LLM's generated output; (3) Fairness: These evaluations assess whether sensitive attributes (e.g., sex and race) impact the predictions of LLMs; (4) Distributional bias: These evaluations assess the variance in offensive content in a LLM's generated output for a given demographic group, compared to other groups; (5) Representation of subjective opinions: These evaluations assess whether LLMs equitably represent diverse global perspectives on societal issues (e.g., whether employers should give job priority t
"These evaluations assess the quality, stability, and reliability of a LLM's performance when faced with unexpected, out-of-distribution or adversarial inputs. Robustness evaluation is essential in ensuring that a LLM is suitable for real-world applications by assessing its resilience to various perturbations." | 7. AI System Safety, Failures, & Limitations | 7.3 > Lack of capability or robustness | 2 - AI | 2 - Unintentional
"These evaluations seek to ascertain the effectiveness of a LLM in shaping people's beliefs, propagating specific viewpoints, and convincing individuals to undertake activities they might otherwise avoid." | 4. Malicious Actors & Misuse | 4.1 > Disinformation, surveillance, and influence at scale | 2 - AI | 1 - Intentional