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Artificial Intelligence Applied: Six Actual Projects in Big Organizations
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Abstract
The purpose of the chapter is to present some real application of the most advanced information technology in complex adaptive systems as for-profit companies and organizations. In particular, we would like to present the application of Machine Learning and Artificial Intelligence to support some of the activities that are strategical for an effective management of Human Resources.
The tools have been applied to analyze the professional profiles (competencies, skills, knowledge and activities), to evaluate the candidates for hiring and selection, to assess the competences in order to obtain a certification or to prove the results of a training course.
For each project, we provide a description of: a) the context, b) the problem, c) the solution implemented, d) an analysis of the advantages and limits of the solution. All these cases offer also quantitative and qualitative data to sustain our view of Artificial Intelligence as a tool that can help humans managing the complexity levels of the so-called Anthropocene era we live in.
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Organizational changes lead Italian financial institutions to review their HR (Human Resources) management model. Therefore, banks need toreorganize themselves in order to rationalize and automate their processes, involving both HR and Training department.
A significant starting point for this development is the implementation of an innovative method to map the skills, which should be also capable of giving a complex and clear “snapshot” of the Human Capital, so to preserve its complexity and its dynamic nature.Currently Italian banks, which do not have an educational mission, analyse skills with methods and instruments which are developed in different institutions and domains of knowledge.These tools have some particular characteristics: they are attributable to paradigms which are often in contrast, they mix “regulatory” (objective) methods with "based on the person" (subjective) methods.
Furthermore, they are mostly built according to a linear measurement system and they are not well calibrated since the organizational context is different.Basing on this approach, banks implement practices which are often ineffective in defining the criteria for the selection, mobility and training processes, since HR manager also have to consider the industrial relationships in their decision-making.As an alternative to this view, we propose a new perspective both on the skills and the method to analysethem. According to this theoretical framework, skills are a) nonlinear, b) reticular and c) dynamic entities. More specifically, we define them as: a) "emergent properties" of a series of observable behaviors, which b) are placed and connected among them,according to a distributive logic c) in a defined time period.Moreover, we believe there are two more levels of “explanatory emergency”: 1) any professional profile is the non-linear combination of multiple skills; 2) human capital is the result of a topological distribution of cognitive-behavioral assets which are associated with professional profiles.In order to manage this complexity, we have developed a software tool allowing us to represent human capital in a two-dimensional space within which the different features are placed: these elements occupy a specific area depending on the “intensity” levels of the distinctive skills of their role.We have tested this hypothesis and the tool in analysing the Italian Banking System, using our software to generate "smart pictures" of the human capital considered as a whole: behavioral cognitive assets were placed inside different areas of the map, depending on their properties.The software integrates a Self-Organizing Map of Kohonen –an Artificial Neural Network -trained by the administration of the FBA Fund data describing each professional-type profile, the so-called "best performers".In our opinion, the tool has two potential and interesting applications:
1. It could support the HR department during the whole recruitment process: designing training plans, reorganizing educational activities and certificating new professional profiles;
2. It could process the data resulting from the assessment procedures currently used, focusing on the set of knowledge/ability to locate the position of a candidate within the map
Nature writ large is a mess. Yet, underlying unities pervade the long and storied, albeit meandering, path from the early universe to civilization on Earth. Evolution is one of those unifiers, incorporating physical, biological, and cultural changes within a broad and inclusive cosmic-evolutionary scenario. Complexity is another such unifier, delineating the growth of structure, function, and diversity within and among galaxies, stars, planets, life, and society throughout natural history. This chapter summarizes a research agenda now underway not only to search for unity in Nature but also, potentially and more fundamentally, to quantify both unceasing evolution and increasing complexity by modeling energy, whose flows through non-equilibrium systems arguably grant opportunities for evolution to create even more complexity. COSMIC EVOLUTION Truth be told, I am a phenomenologist – neither a theorist studying Nature from first principles (I’m not smart enough) nor an experimentalist actually measuring things (although I used to). My current philosophy of approach aims to observe and characterize Nature thermodynamically, seeking to explicate a scientific worldview that chronicles systematically and sequentially the many varied changes that have occurred from the big bang to humankind on Earth. I call that epic worldview cosmic evolution.
A robbery is without doubt a complex event that bursts unexpectedly into the "working scenario" of the bank employee, assigning very precise roles to robber and victim. This impacts on the system of standards and rules of conduct laid down by the bank for employees and on the structural variables of the security systems. In addition, it involves a strong emotional reaction on the part of the robber and of the victim. In this perspective, the ABI (Italian Banking Association) has drawn up specific guidelines for the evaluation of robberies with the aim of prevention and employee protection. In addition, the banks have undertaken specific commitments, signing the Protocol of Understanding for the Prevention of Crime together with the Prefectures. In the context described, training becomes the instrument of choice for strengthening skills associated with evaluating and managing the robbery event, and for promoting increased awareness and ability among potential victims regarding the management of possible risks and the means of protection that can be used for their own and others' safety. ABIFormazione, in collaboration with ABI, has developed a learning system aimed at responding to the complexity of the problem and its variables. The core of the learning system is the "Bank Robberies" simulation. The purpose of this feature is to promote the development of skills associated with the management of a complex operational situation such as a robbery. Within this "situation", users have the option to perform the "soft" version of the elements that characterize certain aspects of their own professional or daily life through continuous interaction with the actors and objects within the context. 2.
Organizational Learning (OL) is a scientific field characterized by a long history and growing interest, so to be considered a core and promising concept in theory and practice related to the management and organization of work. This paper addresses the two main issues that have been dominating scientific debate in the area: the terminological confusion and the paucity and limitedness of the relevant empirical studies. The perceived need for systematization is not just a call for a unified vocabulary, but mainly a call for epistemological advances on topics like knowledge, learning, organizations, and innovation. These problems may be generalized to the claim that the social sciences are strong on theory, but relatively weak on practice, and that organizational learning appears to be no exception. The aim of this paper is to discuss some theoretical proposals on those topics, casting them within the complex systems framework, and to outline empirical research relevant to the issues discussed.
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5. This fact raises the question of whether a classifier with less restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence. Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that characterize naive Bayes. We experimentally tested these approaches, using problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper methods for feature selection.
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