  Generative AI systems (ChatGPT, DALL-E, etc) are expanding into multiple
areas of our lives, from art Rombach et al. [2021] to mental health Rob Morris
and Kareem Kouddous [2022]; their rapidly growing societal impact opens new
opportunities, but also raises ethical concerns. The emerging field of AI
alignment aims to make AI systems reflect human values. This paper focuses on
evaluating the ethics of multimodal AI systems involving both text and images -
a relatively under-explored area, as most alignment work is currently focused
on language models. We first create a multimodal ethical database from human
feedback on ethicality. Then, using this database, we develop algorithms,
including a RoBERTa-large classifier and a multilayer perceptron, to
automatically assess the ethicality of system responses.
