  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. In fact, many
relevant automated systems have been shown to make decisions based on sensitive
information or discriminate certain social groups (e.g. certain biometric
systems for person recognition). With the aim of studying how current
multimodal algorithms based on heterogeneous sources of information are
affected by sensitive elements and inner biases in the data, we propose a
fictitious automated recruitment testbed: FairCVtest. We train automatic
recruitment algorithms using a set of multimodal synthetic profiles consciously
scored with gender and racial biases. FairCVtest shows the capacity of the
Artificial Intelligence (AI) behind such recruitment tool to extract sensitive
information from unstructured data, and exploit it in combination to data
biases in undesirable (unfair) ways. Finally, we present a list of recent works
developing techniques capable of removing sensitive information from the
decision-making process of deep learning architectures. We have used one of
these algorithms (SensitiveNets) to experiment discrimination-aware learning
for the elimination of sensitive information in our multimodal AI framework.
Our methodology and results show how to generate fairer AI-based tools in
general, and in particular fairer automated recruitment systems.
