FairCVtest Demo: Understanding Bias in Multimodal Learning with a Testbed in Fair Automatic Recruitment
This work addresses fairness and discrimination issues in AI-driven recruitment systems, which is a critical societal problem, but it appears incremental as it builds on existing multimodal learning approaches with a specific focus on bias mitigation.
The paper tackles the problem of bias in multimodal AI algorithms for automated recruitment by introducing FairCVtest, a testbed that demonstrates how AI can extract and exploit sensitive information from unstructured data, leading to unfair outcomes. It also presents SensitiveNets, a new algorithm that eliminates sensitive information in the framework, though no concrete performance numbers are provided.
With the aim of studying how current multimodal AI algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, this demonstrator experiments over an automated recruitment testbed based on Curriculum Vitae: FairCVtest. The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new sources of discrimination are arising. This demo shows the capacity of the Artificial Intelligence (AI) behind a recruitment tool to extract sensitive information from unstructured data, and exploit it in combination to data biases in undesirable (unfair) ways. Aditionally, the demo includes a new algorithm (SensitiveNets) for discrimination-aware learning which eliminates sensitive information in our multimodal AI framework.