  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. There is a
certain consensus about the need to develop AI applications with a
Human-Centric approach. Human-Centric Machine Learning needs to be developed
based on four main requirements: (i) utility and social good; (ii) privacy and
data ownership; (iii) transparency and accountability; and (iv) fairness in
AI-driven decision-making processes. All these four Human-Centric requirements
are closely related to each other. 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 case study focused on automated recruitment: FairCVtest. We train
automatic recruitment algorithms using a set of multimodal synthetic profiles
including image, text, and structured data, which are consciously scored with
gender and racial biases. FairCVtest shows the capacity of the Artificial
Intelligence (AI) behind automatic recruitment tools built this way (a common
practice in many other application scenarios beyond recruitment) to extract
sensitive information from unstructured data and exploit it in combination to
data biases in undesirable (unfair) ways. We present an overview of recent
works developing techniques capable of removing sensitive information and
biases from the decision-making process of deep learning architectures, as well
as commonly used databases for fairness research in AI. We demonstrate how
learning approaches developed to guarantee privacy in latent spaces can lead to
unbiased and fair automatic decision-making process.

Machine Learning;Artificial Intelligence;Computers and Society