Year: 2024
Type: conference-paper
Abstract: The advance of AI, mainly in image and text generative models, has accelerated the AI presence in many applications in our daily lives. However, the access to this tool for a large audience implies rigorous evaluation to avoid output biases, discrimination and other ethical problems. This paper describes the evaluation method focused on generative AI and LLM to identify toxicity of outputs. The case studies showed the importance of model evaluation approach from identification of gender biases in an image generative model, and demonstrated that it’s quite simple to circumvent the guardrails of State of The Art LLMs in order to obtain harmful outputs.
Authors: Cristian Souza, Diogo T. Nascimento, Lucas L. Ramalho, Eliane Collins
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4405935604