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  • Writer's pictureSarah Ruivivar

Meta's FACET dataset: Tackling cultural bias in AI

Updated: Oct 31, 2023

Image from Meta AI

In the ever-evolving world of artificial intelligence, ensuring fair representation and eliminating bias is a crucial challenge.

Meta, the tech giant, is taking a significant step towards this goal with the release of its new FACET dataset.

FACET, which stands for FAirness in Computer Vision EvaluaTion, is a human-labelled dataset comprising 32k images. The dataset covers a wide range of demographic attributes, including gender, skin tone, hairstyle, and more. The aim is to help AI developers incorporate these elements into their models, thereby ensuring better representation of historically marginalised communities.

Meta's move is a response to the challenge of benchmarking fairness in computer vision. The risk of mislabelling is real, and the user experience of AI systems can vary based on their demographics, rather than the complexity of the task at hand. By including a broader set of demographic qualifiers, Meta hopes to address this issue, ensuring a more diverse audience group within the results.

Preliminary studies using FACET have shown that state-of-the-art models often exhibit performance disparities across demographic groups. For instance, they may struggle to detect people in images with darker skin tones, a challenge that can be exacerbated for people with coily rather than straight hair.

The release of FACET aims to enable researchers and practitioners to better understand the disparities present in their own models and monitor the impact of mitigations put in place to address fairness concerns. Although FACET is for research evaluation purposes only and cannot be used for training, Meta hopes it will become a standard fairness evaluation benchmark for computer vision models.

In conclusion, the launch of Meta's FACET dataset marks a significant step towards ensuring greater representation and fairness in AI models. By providing a more inclusive set of demographic attributes, it paves the way for the development of AI tools that are not only more robust but also more equitable.

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