AWS GenAI Ethical Considerations

GenAI Ethical Considerations

Responsible AI refers to practices and principles that help you create AI systems that are transparent and trustworthy while mitigating potential risks and negative outcomes.

You should consider these responsible standards throughout the entire lifecycle of an AI application.

This includes the initial design, development, deployment, monitoring, and evaluation phases.

Challenges of generative AI

Generative AI has its unique set of benefits, and it also has a unique set of challenges.

Some of these challenges include toxicity, hallucinations, intellectual property, plagiarism, and cheating.

Toxicity

Toxicity refers to the possibility of generating content that might be offensive, disturbing, or otherwise inappropriate.

Hallucinations

Hallucinations are assertions or claims that sound plausible but are factually incorrect.

Intellectual property

LLMs can produce text or code passages that are verbatim copies of parts of their training data, resulting in privacy and other concerns.

Plagiarism and cheating

The creative capabilities of generative AI create challenges because it can plagiarize works and help with other forms of cheating or illicit copying.

Amplifying bias

Generative AI can amplify existing bias.

When bias exists in the data used for training LLMs, it transfers to the model outputs.

Privacy concerns

The information that you share with your model can include personal information and might violate privacy laws.