scholarly journals “Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

2019 ◽  
Vol 5 (3) ◽  
Author(s):  
Manuel Gonzalez ◽  
John Capman ◽  
Frederick Oswald ◽  
Evan Theys ◽  
David Tomczak
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Adverse Reactions ◽  
Talent Management ◽  
Job Applicants ◽  
Selection Procedures ◽  
Organizational Contexts ◽  
Current State ◽  
Computer Scientists ◽  
Professional Disciplines

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; and in addressing the issue of fairness, we present experimental evidence regarding the potential for AI/ML to evoke adverse reactions from job applicants during selection procedures. We close by emphasizing increased collaboration among I-O psychologists, computer scientists, legal scholars, and members of other professional disciplines in developing, implementing, and evaluating AI/ML applications in organizational contexts.

scholarly journals Artificial Intelligence and Machine Learning

2021 ◽  
Vol 43 (1) ◽  
pp. 8-13
Author(s):  
Bonnie Lawlor
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Professional Disciplines

Abstract The uses of Artificial Intelligence (AI) and Machine Learning (ML) are topics of presentations at most conferences today across diverse professional disciplines. Why? The following quote says it all:

scholarly journals Educing AI-Thinking in Science, Technology, Engineering, Arts, and Mathematics (STEAM) Education

2019 ◽  
Vol 9 (3) ◽  
pp. 184 ◽  
Author(s):  
Meng-Leong How ◽  
Wei Loong David Hung
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Thinking Skills ◽  
Supervised Machine Learning ◽  
Controlled Experiments ◽  
Machine Learn ◽  
Science Technology ◽  
Steam Education ◽  
Computer Scientists ◽  
And Mathematics

In science, technology, engineering, arts, and mathematics (STEAM) education, artificial intelligence (AI) analytics are useful as educational scaffolds to educe (draw out) the students’ AI-Thinking skills in the form of AI-assisted human-centric reasoning for the development of knowledge and competencies. This paper demonstrates how STEAM learners, rather than computer scientists, can use AI to predictively simulate how concrete mixture inputs might affect the output of compressive strength under different conditions (e.g., lack of water and/or cement, or different concrete compressive strengths required for art creations). To help STEAM learners envision how AI can assist them in human-centric reasoning, two AI-based approaches will be illustrated: first, a Naïve Bayes approach for supervised machine-learning of the dataset, which assumes no direct relations between the mixture components; and second, a semi-supervised Bayesian approach to machine-learn the same dataset for possible relations between the mixture components. These AI-based approaches enable controlled experiments to be conducted in-silico, where selected parameters could be held constant, while others could be changed to simulate hypothetical “what-if” scenarios. In applying AI to think discursively, AI-Thinking can be educed from the STEAM learners, thereby improving their AI literacy, which in turn enables them to ask better questions to solve problems.

ALGORITHMS, MACHINE LEARNING, AND COLLUSION

2018 ◽  
Vol 14 (4) ◽  
pp. 568-607 ◽  
Author(s):  
Ulrich Schwalbe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Experimental Economics ◽  
Competition Law ◽  
Price Setting ◽  
Legal Problems ◽  
Pricing Behavior ◽  
Computer Scientists ◽  
Market Communication ◽  
Self Learning

Abstract This paper discusses whether self-learning price-setting algorithms can coordinate their pricing behavior to achieve a collusive outcome that maximizes the joint profits of the firms using them. Although legal scholars have generally assumed that algorithmic collusion is not only possible but also exceptionally easy, computer scientists examining cooperation between algorithms as well as economists investigating collusion in experimental oligopolies have countered that coordinated, tacitly collusive behavior is not as rapid, easy, or even inevitable as often suggested. Research in experimental economics has shown that the exchange of information is vital to collusion when more than two firms operate within a given market. Communication between algorithms is also a topic in research on artificial intelligence, in which some scholars have recently indicated that algorithms can learn to communicate, albeit in somewhat limited ways. Taken together, algorithmic collusion currently seems far more difficult to achieve than legal scholars have often assumed and is thus not a particularly relevant competitive concern at present. Moreover, there are several legal problems associated with algorithmic collusion, including questions of liability, of auditing and monitoring algorithms, and of enforcing competition law.

scholarly journals Redistribution and Rekognition

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Sarah Myers West
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Community ◽  
Statistical Methods ◽  
Gender Discrimination ◽  
Human Life ◽  
Human Interaction ◽  
The People ◽  
Computer Scientists ◽  
Global Population

Computer scientists, and artificial intelligence researchers in particular, have a predisposition for adopting precise, fixed definitions to serve as classifiers (Agre, 1997; Broussard, 2018). But classification is an enactment of power; it orders human interaction in ways that produce advantage or suffering (Bowker & Star, 1999). In so doing, it obscures the messiness of human life, masking the work of the people involved in training machine learning systems, and hiding the uneven distribution of its impacts on communities (Taylor, 2018; Gray, 2019; Roberts, 2019). Feminist scholars, and particularly feminist scholars of color, have made powerful critiques of the ways in which artificial intelligence systems formalize, classify, and amplify historical forms of discrimination and act to reify and amplify existing forms of social inequality (Eubanks, 2017; Benjamin, 2019; Noble, 2018). In response, the machine learning community has begun to address claims of algorithmic bias under the rubric of fairness, accountability, and transparency. But in doing so, it has largely dealt with these issues in familiar terms, using statistical methods aimed at achieving parity and deploying fairness ‘toolkits’. Yet actually existing inequality is reflected and amplified in algorithmic systems in ways that exceed the capacity of statistical methods alone. This article outlines a feminist critique of extant methods of dealing with algorithmic discrimination. I outline the ways in which gender discrimination and erasure are built into the field of AI at a foundational level; the product of a community that largely represents a small, privileged, and male segment of the global population (Author, 2019). In so doing, I illustrate how a situated mode of inquiry enables us to more closely examine a feedback loop between discriminatory workplaces and discriminatory systems.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

scholarly journals Overview of current state of research on the application of artificial intelligence techniques for COVID-19

2021 ◽  
Vol 7 ◽  
pp. e564
Author(s):  
Vijay Kumar ◽  
Dilbag Singh ◽  
Manjit Kaur ◽  
Robertas Damaševičius
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Interaction ◽  
Future Research ◽  
Rapid Testing ◽  
Learning Models ◽  
Open Area ◽  
Current State ◽  
State Of Research ◽  
Novel Drug

Background Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. Methodology This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. Results In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. Conclusions The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2019 ◽  
pp. 1-38
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

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