Challenges of Generative AI
While AI offers many capabilities, some challenges include regulatory violations, social risks, privacy concerns, toxicity, hallucination, and interpretability. These challenges are important to take into account because a model has the potential to make decisions that are unethical or socially irresponsible. Review the list below:
Regulatory violations
Risk
Generative AI models trained on sensitive data might inadvertently generate an output that violates regulations, such as exposing personally identifiable information (PII).
Mitigation
To minimize the risk of privacy violations, implement strict data anonymization and privacy-preserving techniques during model training. To ensure compliance with privacy regulations, conduct thorough audits and assessment of the data used to train the model.
These challenges address issues related to data quality and bias. By proactively addressing them, customers can experience generative AI full potential while ensuring its responsible across various domains.
You have learned about the capabilities and challenges of generative AI. In the next lesson, you will learn about factors to consider when selecting a generative AI model.