Browse AI risks

14 entries

1. 30.03.01 · Risk Sub-Category

   Fairness

   Injustice

   In the context of LLM outputs, we want to make sure the suggested or completed texts are indistinguishable in nature for two involved individuals (in the prompt) with the same relevant profiles but might come from different groups (where the group attribute is regarded as being irrelevant in this context)

   From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

   Domain 1 1.1 AI Unintentional Post-deployment

2. 30.03.02 · Risk Sub-Category

   Fairness

   Stereotype Bias

   LLMs must not exhibit or highlight any stereotypes in the generated text. Pretrained LLMs tend to pick up stereotype biases persisting in crowdsourced data and further amplify them

   From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

   Domain 1 1.1 AI Unintentional Post-deployment

3. 30.03.03 · Risk Sub-Category

   Fairness

   Preference Bias

   LLMs are exposed to vast groups of people, and their political biases may pose a risk of manipulation of socio-political processes

   From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

   Domain 1 1.1 AI Other Post-deployment

4. 30.07.03 · Risk Sub-Category

   Robustness

   Interventional Effect

   existing disparities in data among different user groups might create differentiated experiences when users interact with an algorithmic system (e.g. a recommendation system), which will further reinforce the bias

   From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

   Domain 1 1.1 AI Unintentional Post-deployment

5. 30.02.00 · Risk Category

   Safety

   Avoiding unsafe and illegal outputs, and leaking private information

   From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

   Domain 1 1.2 AI Other Post-deployment

6. 30.02.01 · Risk Sub-Category

   Safety

   Violence

   LLMs are found to generate answers that contain violent content or generate content that responds to questions that solicit information about violent behaviors

   From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

   Domain 1 1.2 AI Intentional Post-deployment

7. 30.02.02 · Risk Sub-Category

   Safety

   Unlawful Conduct

   LLMs have been shown to be a convenient tool for soliciting advice on accessing, purchasing (illegally), and creating illegal substances, as well as for dangerous use of them

   From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

   Domain 1 1.2 AI Intentional Post-deployment

8. 30.02.03 · Risk Sub-Category

   Safety

   Harms to Minor

   LLMs can be leveraged to solicit answers that contain harmful content to children and youth

   From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

   Domain 1 1.2 AI Intentional Post-deployment

9. 30.02.04 · Risk Sub-Category

   Safety

   Adult Content

   LLMs have the capability to generate sex-explicit conversations, and erotic texts, and to recommend websites with sexual content

   From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

   Domain 1 1.2 AI Intentional Post-deployment

10. 30.06.00 · Risk Category

    Social Norm

    LLMs are expected to reflect social values by avoiding the use of offensive language toward specific groups of users, being sensitive to topics that can create instability, as well as being sympathetic when users are seeking emotional support

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

    Domain 1 1.2 AI Other Post-deployment

11. 30.06.01 · Risk Sub-Category

    Social Norm

    Toxicity

    language being rude, disrespectful, threatening, or identity-attacking toward certain groups of the user population (culture, race, and gender etc)

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

    Domain 1 1.2 AI Other Post-deployment

12. 30.06.03 · Risk Sub-Category

    Social Norm

    Cultural Insensitivity

    it is important to build high-quality locally collected datasets that reflect views from local users to align a model’s value system

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

    Domain 1 1.2 Human Unintentional Pre-deployment

13. 30.03.00 · Risk Category

    Fairness

    Avoiding bias and ensuring no disparate performance

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

    Domain 1 1.3 AI Unintentional Other

14. 30.03.04 · Risk Sub-Category

    Fairness

    Disparate Performance

    The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups. The fact-checking abilities can differ for different tasks and languages

    From Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)

    Domain 1 1.3 AI Unintentional Other

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