Bias and Fairness
Generative AI models are trained on large datasets of text and images, which can contain biases. This can lead to the models generating biased or unfair content. It is important to be aware of these biases and to take steps to mitigate them.
Misinformation and Disinformation
Generative AI can be used to create realistic but fake images, videos, and text, which can be used to spread misinformation and disinformation. It is important to develop tools and techniques for detecting and flagging fake content.
Intellectual Property
Generative AI models are trained on large datasets of copyrighted material. This raises questions about who owns the intellectual property of the content generated by these models. We need to develop new legal frameworks to address these issues.
Accountability and Transparency
When a generative AI model makes a mistake, who is responsible? It is important to develop clear lines of accountability for the actions of generative AI models. We also need to make the models more transparent so that we can understand how they make their decisions.
GenAI Labs
AI product and agentic systems engineering
