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ABSTRACT
An industry or a business or a firm requires quality personnel for accomplishment of objectives framed by them in order to survive in this competitive era. They all are in the beginning of fourth industrial revolution. To remain competitive in this digital world all search for bright, potential and dynamic employees. Organizations with an effective recruitment strategy will be able to employ suitable individual in order to manage the digital world and developing business environment. So the recruitment strategy is the prime factor for every organization in hiring skilled employees who could be more efficient and effective in accomplishing the job objectives. The recruitment strategy as it is a major function of organization apparently takes help of data analysis for decision making process. The data analysis is known as “Artificial Intelligence” which plays a crucial role in recruitment decision. Artificial intelligence in a most basic terminology and is a human develop intelligent machines. AI will work and react like human and its ultimate goal is to facilitate computers to carry out the work as normally done by people. AI leads with an incredible speed and accuracy. The major objective of this paper is to study how Artificial Intelligence influences the recruitment strategy.
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.
