scholarly journals Scientific, Legal, and Ethical Concerns About AI-Based Personnel Selection Tools: A Call to Action

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Nancy Tippins ◽  
Frederick Oswald ◽  
S. Morton McPhail
Keyword(s):  
Machine Learning ◽  
Personnel Selection ◽  
Machine Learning Algorithms ◽  
Call To Action ◽  
Ethical Concerns ◽  
Test Taking ◽  
Future Success ◽  
Job Candidates ◽  
Many Sources ◽  
Selection Tools

Organizations are increasingly turning toward personnel selection tools that rely on artificial intelligence (AI) technologies and machine learning algorithms that, together, intend to predict the future success of employees better than traditional tools. These new forms of assessment include online games, video-based interviews, and big data pulled from many sources, including test responses, test-taking behavior, applications, resumes, and social media. Speedy processing, lower costs, convenient access, and applicant engagement are often and rightfully cited as the practical advantages for using these selection tools. At the same time, however, these tools raise serious concerns about their effectiveness in terms their conceptual relevance to the job, their basis in a job analysis to ensure job relevancy, their measurement characteristics (reliability and stability), their validity in predicting employee-relevant outcomes, their evidence and normative information being updated appropriately, and the associated ethical concerns around what information is being represented to employers and told to job candidates. This paper explores these concerns, concluding with an urgent call to industrial and organizational psychologists to extend existing professional standards for employment testing to these new AI and machine learning based forms of testing, including standards and requirements for their documentation.

scholarly journals Scientific, Legal, and Ethical Concerns about AI-Based Personnel Selection Tools: A Call to Action

2021 ◽  
Author(s):  
Nancy T. Tippins ◽  
Frederick Louis Oswald ◽  
S. Morton McPhail
Keyword(s):  
Machine Learning ◽  
Personnel Selection ◽  
Machine Learning Algorithms ◽  
Call To Action ◽  
Ethical Concerns ◽  
Test Taking ◽  
Future Success ◽  
Job Candidates ◽  
Many Sources ◽  
Selection Tools

Organizations are increasingly turning toward personnel selection tools that rely on artificial intelligence (AI) technologies and machine learning algorithms that, together, intend to predict the future success of employees better than traditional tools. These new forms of assessment include online games, video-based interviews, and the use of large amounts of big data pulled from many sources, including test responses, test-taking behavior, applications, resumes, and social media. Speedy processing, lower costs, convenient access, and applicant engagement are often and rightfully cited as the practical advantages for using these selection tools. At the same time, however, these tools raise serious concerns about their effectiveness in terms their conceptual relevance to the job; their basis in a job analysis to ensure job relevancy; their measurement characteristics (reliability and stability); their validity in predicting employee-relevant outcomes; their evidence and normative information being updated appropriately; and the associated ethical concerns around what information is being represented to employers and told to job candidates. This paper explores these concerns, concluding with an urgent call to industrial and organizational psychologists to extend existing professional standards for employment testing to these new AI and machine learning based forms of testing, including standards and requirements for their documentation.

Artificial Intelligence for Decision Makers

2019 ◽  
pp. 0000-0000
Author(s):  
Viktor Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools

We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns.

Artificial Intelligence for Decision-Makers

2019 ◽  
Vol 17 (1) ◽  
pp. 51-55 ◽  
Author(s):  
Viktor H. Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools ◽  
Jel Classifications

ABSTRACT We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns. JEL Classifications: A29; C44; C45; D81; M41.

Machine Learning and Its Application in Monitoring Diabetes Mellitus

2021 ◽  
pp. 228-288
Author(s):  
Vandana Kalra ◽  
Indu Kashyap ◽  
Harmeet Kaur
Keyword(s):  
Machine Learning ◽  
Chronic Disease ◽  
Data Science ◽  
Prediction Models ◽  
Simulation Models ◽  
Machine Learning Algorithms ◽  
Common Chronic Disease ◽  
Future Success ◽  
Automatic Pattern Recognition ◽  
Knowledge Exploration

Data science is a fast-growing area that deals with data from its origin to the knowledge exploration. It comprises of two main subdomains, data analytics for preparing data, and machine learning to probe into this data for hidden patterns. Machine learning (ML) endows powerful algorithms for the automatic pattern recognition and producing prediction models for the structured and unstructured data. The available historical data has patterns having high predictive value used for the future success of an industry. These algorithms also help to obtain accurate prediction, classification, and simulation models by eliminating insignificant and faulty patterns. Machine learning provides major advancement in the healthcare industry by assisting doctors to diagnose chronic diseases correctly. Diabetes is one of the most common chronic disease that occurs when the pancreas cells are damaged and do not secrete sufficient amount of insulin required by the human body. Machine learning algorithms can help in early diagnosis of this chronic disease by studying its predictor parameter values.

Test-Taking Motivation and Personality Test Validity

2010 ◽  
Vol 9 (3) ◽  
pp. 117-125 ◽  
Author(s):  
Thomas A. O’Neill ◽  
Richard D. Goffin ◽  
Ian R. Gellatly
Keyword(s):  
Personality Assessment ◽  
Personnel Selection ◽  
Test Validity ◽  
Personality Testing ◽  
Personality Test ◽  
Field Sample ◽  
Moderation Effect ◽  
Test Taking ◽  
Low Levels ◽  
Valid Criterion

In this study we assessed whether the predictive validity of personality scores is stronger when respondent test-taking motivation (TTM) is higher rather than lower. Results from a field sample comprising 269 employees provided evidence for this moderation effect for one trait, Steadfastness. However, for Conscientiousness, valid criterion prediction was only obtained at low levels of TTM. Thus, it appears that TTM relates to the criterion validity of personality testing differently depending on the personality trait assessed. Overall, these and additional findings regarding the nomological net of TTM suggest that it is a unique construct that may have significant implications when personality assessment is used in personnel selection.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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