Utilizing machine learning models to identify implicit bias

A device, comprising:

• one or more memories;
• and
• one or more processors, coupled to the one or more memories, configured to:
• receive multimodal content;
• process the multimodal content, with a binary classifier model, to determine whether the multimodal content is biased or unbiased;
• process the multimodal content, with a multilabel classifier model and based on the multimodal content being biased, to determine an implicit bias in the multimodal content;
• and
• perform one or more actions based on the implicit bias.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive multimodal content that includes two or more of text data, image data, or video data;
• process the two or more of the text data, the image data, or the video data, with a binary classifier model, to determine whether the two or more of the text data, the image data, or the video data is biased or unbiased,
• wherein the binary classifier model includes a bidirectional encoder representations from transformers model;
• process the two or more of the text data, the image data, or the video data, with a multilabel classifier model and based on the two or more of the text data, the image data, or the video data being biased, to determine an implicit bias in at least one of the two or more of the text data, the image data, or the video data,
• wherein the multilabel classifier model includes a bidirectional auto-regressive transformers model;
• and
• perform one or more actions based on the implicit bias.