Browse AI risks

7 entries

1. 04.02.00 · Risk Category

   Unfairness and Discrimination

   Social bias is an unfairly negative attitude towards a social group or individuals based on one-sided or inaccurate information, typically pertaining to widely disseminated negative stereotypes regarding gender, race, religion, etc.

   From Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

   Domain 1 1.1 Other Other Post-deployment

2. 04.01.00 · Risk Category

   Toxicity and Abusive Content

   This typically refers to rude, harmful, or inappropriate expressions.

   From Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

   Domain 1 1.2 Other Other Post-deployment

3. 04.04.00 · Risk Category

   Controversial Opinions

   The controversial views expressed by large models are also a widely discussed concern. Bang et al. (2021) evaluated several large models and found that they occasionally express inappropriate or extremist views when discussing political top-ics. Furthermore, models like ChatGPT (OpenAI, 2022) that claim political neutrality and aim to provide objective information for users have been shown to exhibit notable left-leaning political biases in areas like economics, social policy, foreign affairs, and civil liberties.

   From Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

   Domain 1 1.2 AI Other Post-deployment

4. 04.06.00 · Risk Category

   Privacy and Data Leakage

   Large pre-trained models trained on internet texts might contain private information like phone numbers, email addresses, and residential addresses.

   From Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

   Domain 2 2.1 AI Unintentional Pre-deployment

5. 04.05.00 · Risk Category

   Misleading Information

   Large models are usually susceptible to hallucination problems, sometimes yielding nonsensical or unfaithful data that results in misleading outputs.

   From Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

   Domain 3 3.1 AI Unintentional Post-deployment

6. 04.07.00 · Risk Category

   Malicious Use and Unleashing AI Agents

   LMs, due to their remarkable capabilities, carry the same potential for malice as other technological products. For instance, they may be used in information warfare to generate deceptive information or unlawful content, thereby having a significant impact on individuals and society. As current LMs are increasingly built as agents to accomplish user objectives, they may disregard the moral and safety guidelines if operating without adequate supervision. Instead, they may execute user commands mechanically without considering the potential damage. They might interact unpredictably with humans a

   From Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

   Domain 4 4.0 Other Intentional Post-deployment

7. 04.03.00 · Risk Category

   Ethics and Morality Issues

   LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

   From Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

   Domain 7 7.3 AI Other Post-deployment

Informational only, not legal advice. Verify every claim against the linked official sources and consult qualified counsel before acting.