Ethical Challenges and Privacy Concerns Associated with Big Data in the Hiring Process: A Mixed-Methods Study | Chapter 6 | Mathematics and Computer Science: Research Updates Vol. 6

 

Background: The advent of big data in recruitment processes has introduced more efficient, quicker, and scalable enhanced decision-making. Big data technologies enable recruiters to analyse vast amounts of candidate information, ostensibly improving the precision with which suitable candidates are identified. However, this technological advance also presents significant ethical challenges.

 

Aims: This study aims to explore the ethical challenges and privacy concerns associated with the use of big data in recruitment processes, focusing on algorithmic bias, data privacy, and fairness in hiring practices.

 

Methodology: The research employs a mixed-methods design, integrating qualitative interviews with HR professionals and quantitative data analysis to assess the implications of big data utilisation in recruitment. The study was conducted across various organisations, focusing on their recruitment practices, over six months. Qualitative interviews were conducted with HR professionals to gather insights on real-world experiences related to ethical challenges in recruitment. Additionally, a quantitative analysis of recruitment algorithms was performed to identify prevalent biases and their impact on hiring decisions, using statistical evidence to highlight significant findings. By triangulating these methods, the research robustly examined how big data applications alter recruitment landscapes, identifying ethical challenges and laying a foundation for potential solutions.

 

Results: The findings reveal that algorithmic bias is a profound issue in recruitment, with 62% of surveyed HR professionals acknowledging its influence on hiring decisions. Moreover, significant concerns regarding data privacy emerged, with 75% of respondents indicating that handling sensitive candidate information lacks adequate safeguards, increasing the risk of unauthorised access. Addressing ethical concerns in big data recruitment necessitates the collaboration of multiple stakeholders, including HR professionals, data scientists, and ethicists. Integrating fairness-aware algorithms is a pivotal strategy, as they aim to rectify biases at different stages of data processing, ensuring equitable decision-making. By encouraging collaboration and implementing comprehensive strategies, organisations can mitigate the ethical challenges associated with using big data in recruitment, ultimately fostering a more inclusive and fair hiring environment.

 

Conclusion: The study concludes that while big data enhances recruitment efficiency, it simultaneously raises critical ethical challenges that must be addressed. Organisations need to implement robust frameworks to ensure fairness and transparency, thereby safeguarding candidates' privacy and fostering equitable hiring practices. These insights provide crucial guidance for HR professionals seeking to navigate the complexities of big data in recruitment.

 

 

Author(s) Details

 

Kevwe Onome-Irikefe
University of Rochester, United States.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/mcsru/v6/5930

 

Predictive Analytics and the Presidential Election: Key Candidate Attributes that Predict Voter Behaviour in the 2020 Presidential Election | Chapter 5 | New Ideas Concerning Arts and Social Studies Vol. 4

 

Marketing is a key component in elections with voters. Political marketing is an important component and factor influencing how voters choose political candidates. The purpose of this study was to examine key candidate attributes and predictive analytics that influenced voter behaviour in the 2020 Presidential Election. The Political Marketing Candidate Attribute Scale (PMCAS) was developed specifically for this research on political marketing and voter behaviour. This study is the result of a four-year research project on how political candidates win or lose elections based on predictive analytics, which included local and state elections and the 2020 Presidential Election. The results of the study reveal the key predictor variables that influenced voter behaviour for candidates for local and state elections, as well as the presidential election. The researchers had four national samples (n = 146), (n = 758), (n = 1,016), and (n = 1,324) in the U.S. that were used for this research on political marketing and candidate attributes.

 

For this study, the researchers examined 30 candidate attributes that are key indicators in predicting election wins. Here, three statistical tests were used to measure a candidate’s attributes that influence voter behaviour. The results of this four-year study revealed three key factors that influence voter behaviour based on candidate attributes.  First, the top ten candidate attributes that predict voter behaviour and predict candidate wins in an election were identified. Second, five key demographic variables are found to be a significant predictive influence on voter behaviour and election wins.  Lastly, it was found that voters are highly influenced by the visual attributes of candidates compared to other attributes. The implication for marketers is that political marketing efforts can be predicted using statistical models and marketing model frameworks.

Author(s) Details

D. Anthony Miles
Miles Development Industries Corporation®, USA.

 

Joshua Garcia
Palo Alto College, USA.

 

Wanda Goodnough
Ashford University, USA.

 

Dt Ogilvie
Rochester Institute of Technology, USA.

 

Eniola Olagundoye
Texas Southern University, USA.

 

E.L. Seay
Albany State University, USA.

 

Nathan Tymann
Grand Canyon University, USA.

 

Robin Shedrick
Wright2Learn LLC, USA.

 

Please see the book here:- https://doi.org/10.9734/bpi/nicass/v4/5811

AI-Driven Financial Risk Mitigation in Energy Investments: Enhancing Capital Allocation and Portfolio Optimisation | Chapter 4 | New Advances in Business, Management and Economics Vol. 8

 

Background: The energy industry requires enormous financial investment across various sub-sectors, including oil, natural gas, nuclear energy, and renewable energy technologies. Financial risk management remains a critical concern within the sector. With the development in artificial intelligence (AI), machine learning (ML), and big data analytics, financial risk management processes have been greatly transformed by furnishing real-time information, predictive analysis, and automated decision-making processes.

 

Aim: This study examines the extent to which data-driven financial risk mitigation practices assist in optimising the usage of capital and portfolio performance in energy investments, particularly in the face of market volatility, regulatory risks, and geopolitical risks.

 

Methodology: This study employs a systematic literature review to analyse 12 empirical studies published between 2019 and 2024. This chapter reviews recent studies that use AI tools such as machine-learning models, predictive analytics, and automated portfolio methods. The review was conducted using reputable databases such as Google Scholar, Scopus, SSRN, and the Journal of Risk and Financial Management. Selected articles focus on financial risk assessment models, predictive analytics, and AI-driven investment optimisation in the energy sector.

 

Results: This review highlighted the application of AI-driven credit risk modelling, machine learning-based predictive analytics, and portfolio optimisation through automation in energy financing. These advanced analytical tools have empowered investors to effectively deal with market volatility, regulatory risks, and geopolitical threats. The findings also indicate that data analytics maximise investment accuracy, reduce capital exposure, and optimise portfolio diversification in various energy sub-sectors, including renewable and conventional energy resources. These have practical implications for financial institutions, policymakers, and investors by improving risk assessment frameworks, informing regulatory compliance strategies, and enhancing decision-making in energy financing.

 

Conclusions: Financial risk mitigation strategies, techniques that are data-driven, are crucial to ensure maximum financial robustness of energy investments. Analytics with AI improve predictive power, ensuring optimal allocation of capital and reducing financial exposure. However, the scalability of AI models in numerous regulatory environments is a major issue because various data governance rules and compliance levels might limit their use. Scalability and flexibility across diverse regulatory environments of these technologies need to be investigated in future studies.

 

Author (s) Details

 Ebere Juliet Onyeka
The George Washington University, United States.

 

Please see the book here:- https://doi.org/10.9734/bpi/nabme/v8/5643

 

Assessment of Neurological Disorders among Children using Machine Learning Techniques | Chapter 10 | Research Developments in Science and Technology Vol. 10

 

 The early diagnosis of neurological problems in children helps medical personnel to enhance the patients' health. Therefore, it is essential to recognise neurological anomalies since, if treatment is delayed, they might turn into major problems. Medical data may be analysed and the problem can be accurately diagnosed with the help of machine learning algorithms. This research has discovered machine learning algorithms on several accuracy metrics to accurately detect three prevalent neurological disorders. A neurological data set is collected from a neuro clinic facility in order to evaluate the efficacy of machine learning approaches. Numerous psychological examinations, including clinic neuropsychiatric observation, audio evaluation, and intellectual coefficient assessment, are also carried out on people who have neurological diseases. Some of the collected characteristics were found to be crucial for figuring out the problem. The findings unmistakably demonstrate that the chosen ML techniques produced results that were more accurate, and there is just a little variation in how well they performed.

Author(s) Details:

G. Reshma,
Department of Information Technology, PVPSIT, Kanuru, Vijayawada, India.

P. V. S Lakshmi,
Department of Information Technology, PVPSIT, Kanuru, Vijayawada, India.

Please see the link here: https://stm.bookpi.org/RDST-V10/article/view/7723  

The Use of Artificial Intelligence in Patient Experience in OP: A Review | Chapter 5 | New Approaches in Engineering Research Vol. 10

 The standard of living has increased in recent years as a result of considerable technological advancements in practically every sphere. The introduction of new technologies, advanced machinery, and equipment, particularly in the healthcare sector, has greatly simplified the diagonalizing process. The detection and correction of numerous disorders was made possible thanks to smart approaches used in medical applications. This project will look at how artificial intelligence may be used to improve patient experience across the healthcare business, with a focus on the outpatient (OP) segment. According to industry statistics, 88 percent of patients are willing to switch healthcare providers at any time. According to a 2018 study by The Beryl Institute, 91 percent of patients believe that patient experience is extremely or very essential, and that it influences their healthcare decisions.

Author (S) Details

Balagopal Ramdurai
Door 15, PLot #5 Lakshmi Street, Santosh Nagar, P&T Nagar Madurai, Tamil Nadu- 625017, India.

View Book :- https://stm.bookpi.org/NAER-V10/article/view/2981

Anomaly Detection of Outlier Features from Spatio-temporal Databases of Landsat-8 Sensor, using Cloud Computing Platform | Chapter 12 | Current Topics on Mathematics and Computer Science Vol. 2

 There has recently been a surge in the number of research articles published in peer-reviewed journals about machine learning and specialized algorithms for feature identification, feature selection, and feature extraction studies. This article distinguishes proof-of-concept applications from domains such as computer vision, remote sensing, image processing, and geospatial database technology. Using satellite imagery from the Landsat-8 sensor for rendering in multimedia and scalable vector processing modes, The article validates the fundamentals and principles of digital image analysis. The article describes the RSVM and DAFE scientific methods in the cloud computing platform in detail using a user-defined algorithm and a scientific approach. It is proposed to introduce data analytics nuances in distributed computing and parallel databases.

Author (S) Details

C. Rajabhushanam
Department of Computer Science Engineering, Bharath Institute of Higher Education and Research, Selaiyur, Chennai, Tamilnadu, India.

View Book :- https://stm.bookpi.org/CTMCS-V2/article/view/1784