Browse AI risks

4 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

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