AI use cases and applications
Challenges of Generative AI
A model can make decisions that are unethical or socially irresponsible, so each challenge needs a mitigation.
| Challenge | Risk | Mitigation |
|---|---|---|
| Regulatory violations | Output exposes regulated data such as PII | Anonymize data, use privacy-preserving training, audit training data |
| Social risks | Content damages reputation or harms society | Test outputs, set content policies, monitor use |
| Data security and privacy | Sensitive data leaks through prompts or outputs | Encrypt data, control access, filter inputs and outputs |
| Toxicity | Offensive or inappropriate output | Curate training data, use guardrails to filter content |
| Hallucinations | Plausible but false statements | Ground answers with RAG, verify outputs, keep humans in the loop |
| Interpretability | Hard to explain why the model produced an output | Use explainability tools and document model behavior |
| Nondeterminism | The same input gives different outputs on different runs | Lower the temperature, test repeatedly, validate outputs |
The full notes captured only the first slide of this carousel (regulatory violations). The other rows follow the AWS course.
Nondeterminism is the term to remember when a question describes different outputs from the same input.