West Science Social and Humanities Studies Vol.
No.
March 2026, pp.
The Influence of AI Ethics in HR and Algorithm Transparency on Employee Trust in the Manufacturing Industry Supiah Ningsih1.
Danil Syahputra2.
Nazifah Husainah3.
Adityo Ari Wibowo4.
Cikita Berlian Hakim5 Universitas Muhammadiyah Asahan Universitas Muhammadiyah Jakarta 4 Universitas Muhammadiyah Prof.
Dr.
Hamka - Jakarta
5 Universitas Muhammadiyah Kudus
Article Info
ABSTRACT
Article history:
This study examines the influence of artificial intelligence (AI) ethics in human resources and algorithm transparency on employee trust in the manufacturing industry.
The rapid adoption of AI in organizational management has transformed HR practices, including recruitment, performance evaluation, and workforce management, by enabling more efficient and data-driven decision-making.
However, the use of AI also raises concerns regarding ethical governance and the transparency of algorithmic decision-making processes.
This research employs a quantitative approach using a survey method involving 150 employees working in manufacturing companies.
Data were collected through structured questionnaires measured on a five-point Likert scale and analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS .
The results indicate that AI ethics in HR has a positive and significant effect on employee trust, suggesting that the application of ethical principles in AI-based HR practices enhances employeesAo perceptions of fairness and organizational responsibility.
In addition, algorithm transparency also shows a positive and significant influence on employee trust, demonstrating that clear explanations of AI decision-making processes can increase employeesAo confidence in technology-driven HR systems.
The structural model reveals that both variables explain 63% of the variance in employee trust (RA = 0.
Overall, these findings emphasize the importance of responsible AI governance and transparent algorithmic systems in strengthening employee trust and supporting the sustainable adoption of AI technologies within organizations.
Received Mar, 2026 Revised Mar, 2026 Accepted Mar, 2026 Keywords:
Artificial Intelligence Ethics Algorithm Transparency Human Resource Management Employee Trust Manufacturing Industry This is an open access article under the CC BY-SA license.
Corresponding Author:
Name: Supiah Ningsih Institution: Universitas Muhammadiyah Asahan Email: supiahningsih26@gmail.
INTRODUCTION
The rapid advancement of digital technologies has significantly transformed organizational practices across various Among developments, artificial intelligence (AI) has emerged as one of the most influential innovations shaping modern organizational Journal homepage: https://wsj.
westscience-press.
com/index.
php/wsshs West Science Social and Humanities Studies management .
, .
AI technologies are increasingly integrated into human resource (HR) functions such as recruitment screening, employee performance evaluation, workforce planning, and talent management.
Through the use of machine learning algorithms and decision-making organizations are able to process large volumes of employee data efficiently and support faster and more strategic managerial decisions .
, .
As manufacturing companies increasingly adopt Industry 4.
practices, the integration of AI within HR management is expected to expand further, enabling organizations to optimize workforce performance and enhance operational Despite the substantial benefits associated with AI-based HR systems, the adoption of such technologies also raises One of the primary challenges involves the ethical implications of AI in HR decision-making processes.
AI systems rely on algorithms trained using historical organizational data, which may contain embedded biases related to gender, ethnicity, age, or other demographic factors .
, .
these biases are not carefully addressed during algorithm design and implementation.
AI systems may unintentionally reinforce discriminatory practices in recruitment, promotion, or performance evaluation.
Consequently, ensuring ethical governance in the use of AI has become a critical priority for organizations seeking to implement AI technologies responsibly and maintain fairness in workplace decision-making.
Another significant issue associated with AI adoption in HR management relates to the transparency of algorithmic decisionmaking processes.
Many AI systems function as complex Aublack boxAy models in which the underlying logic behind algorithmic decisions is not easily interpretable by users or stakeholders .
In organizational contexts.
HR-related employees may feel uncertain or skeptical when important employment outcomes are determined by automated systems that lack clear explanations.
This lack of transparency A 317 may reduce employeesAo perceptions of fairness, accountability, and procedural justice within the organization .
, .
As a result, algorithm transparency defined as the extent to which AI systems provide understandable explanations regarding how decisions are generatedAihas become an increasingly important factor in shaping employeesAo perceptions toward the adoption of AI technologies.
Employee trust represents another critical element in determining the success of AI implementation within organizations.
Trust reflects employeesAo confidence that organizational systems, technologies, and responsibly, and in accordance with organizational values .
, .
When employees perceive AI systems as ethical and transparent, they are more likely to accept and support the integration of these technologies into HR processes.
In contrast, when AI systems are perceived as opaque or ethically questionable, employees may develop skepticism or resistance toward technologydriven decision-making .
, .
Such perceptions can undermine trust not only in the AI systems themselves but also in the organizations that deploy them.
In the manufacturing industry, where operational efficiency and effective workforce management are essential for maintaining maintaining employee trust is particularly Manufacturing organizations increasingly rely on digital technologies, automation, and data-driven systems to optimize both production processes and human resource management.
As AI becomes more deeply embedded within HR functions, employees must perceive these systems as fair, transparent, and ethically governed in Ethical governance and transparent algorithmic processes may therefore serve as key mechanisms for strengthening employee trust Several previous studies have emphasized the importance of ethical Vol.
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West Science Social and Humanities Studies considerations and transparency in the adoption of AI within organizational Research suggests that the implementation of ethical AI frameworks can enhance organizational legitimacy and increase employee acceptance of emerging Likewise, algorithmic systems have been shown to improve perceptions of fairness and accountability in automated decision-making However, empirical investigations examining the relationship between AI ethics, algorithm transparency, and employee trustAiparticularly within the context of the manufacturing sectorAiremain relatively Therefore, this study aims to analyze the influence of artificial intelligence ethics in human resources and algorithm transparency on employee trust in the manufacturing industry using a quantitative research approach involving 150 manufacturing By examining employeesAo perceptions of ethical AI practices and contributes to the growing literature on responsible AI governance and provides practical insights for organizations seeking to implement AI technologies in ways that strengthen employee trust and support sustainable digital transformation.
LITERATURE REVIEW
1 Artificial Intelligence in Human Resource Management Artificial intelligence (AI) has become an important technological tool in modern particularly in human resource management (HRM).
AI refers to computer systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, decisionmaking, and pattern recognition.
In HR practices.
AI is widely used to improve efficiency in processes such as recruitment performance evaluation, talent
A 318
analytics, workforce planning, monitoring .
The adoption of AI allows organizations to analyze large datasets quickly and make more data-driven For example.
AI-based numbers of job applications, while AI-driven performance evaluation tools can analyze productivity patterns to support managerial assessments .
, .
However, implementation of AI in HR also raises challenges, particularly the risk of algorithmic bias that may reproduce discriminatory patterns in hiring, promotion, or Therefore, organizations must ensure that AI systems in HR management follow ethical fairness, accountability, and 2 AI Ethics in Human Resources AI ethics refers to the set of moral principles and guidelines implementation of artificial intelligence technologies.
In the context of human resource emphasizes that AI systems should operate in ways that nondiscrimination, accountability, and human dignity, ensuring affecting employees remain transparent and aligned with organizational values .
, .
The importance of AI ethics has increasingly rely on automated systems in HR processes such as monitoring, and performance Vol.
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West Science Social and Humanities Studies Without proper systems may generate biased perceptions of fairness and organizational justice .
, .
Therefore, emphasize key principles such as transparency, and responsibility to ensure that algorithmic decisions remain unbiased and When organizations apply these ethical principles in HR management, employees are more likely to perceive the organization as responsible and trustworthy, thereby strengthening trust employees and the organization.
3 Algorithm Transparency Algorithm refers to the extent to which the processes and logic behind algorithmic decision-making are understandable and accessible to transparency involves providing clear explanations of how data are processed, how decisions are generated, and which factors influence algorithmic outputs .
, .
Many AI systems operate as complex Aublack boxAy mechanisms are difficult for users to interpret, which can skepticism among employeesAi especially when important HR decisions such as promotions, performance evaluations, or When employees do not understand how algorithmic decisions are made, they may question the fairness and legitimacy of these outcomes A 319 Therefore, transparency plays a crucial role in building confidence in AI explaining how AI systems function and how decisions are produced, organizations can enhance employeesAo perceptions of fairness and trust in automated processes.
Moreover, algorithm transparency also accountability, as transparent systems enable organizations to identify potential biases or errors and continuously improve the fairness and reliability of their AI-based decision-making 4 Employee Trust Employee trust refers to the level of confidence employees have in their organization, its leadership, and the systems used to manage workplace processes.
Trust organizational relationships and plays an important role in shaping employee attitudes, commitment, and performance.
In the context of technological innovation, particularly the adoption of AI-based systems in increasingly important because such technologies may alter processes and create uncertainty regarding job security, privacy, fairness, or the accuracy of automated decisions .
, .
Several employee trust in technologydriven organizations, including perceived fairness, transparency.
When employees perceive technological systems Vol.
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West Science Social and Humanities Studies transparently, they are more likely to accept their use and trust the decisions generated by .
, .
Conversely, perceptions of bias or opacity in AI systems may reduce trust in both the technology and the organization implementing it.
Ultimately, strong employee trust can organizational commitment, and overall performance, while low levels of trust may lead to resistance toward technological employee morale.
5 The Relationship between AI Ethics.
Algorithm Transparency, and Employee Trust The ethical governance of artificial intelligence (AI) and the transparency of algorithmic systems are closely related to the development of employee trust within organizations.
When organizations implement ethical AI practices, employees are more likely to perceive that decisionmaking processes are guided by fairness, accountability, and respect for employee well-being.
Ethical AI governance signals committed to minimizing the risks associated with automated decision-making and ensuring Similarly, algorithm transparency can strengthen employee trust by reducing When understand how AI systems operate and how decisions are generated, they are more likely to believe that these systems function objectively and without Transparent employees to evaluate whether decisions are based on relevant and fair criteria.
Therefore, the combination of ethical AI algorithmic systems provides an responsible AI adoption, helping organizations strengthen trust relationships with employees while supporting the effective management practices.
Based on discussed above, this study H1: Artificial Intelligence Ethics in Human Resources has a positive and significant effect on manufacturing industry.
H2:
Algorithm Transparency has a positive and significant effect on employee trust in the manufacturing METHODS 1 Research Design This study employed a quantitative research approach to examine the influence of Artificial Intelligence (AI) ethics in human resources and algorithm transparency on employee trust in the manufacturing A quantitative design was selected because it enables researchers to measure relationships between variables objectively using statistical techniques.
The research adopted a cross-sectional survey method in which data were collected from respondents at a single point in time through structured questionnaires, making it appropriate for analyzing employeesAo perceptions and attitudes toward AI-based HR practices within their organizations.
To analyze the relationships among variables, this study used Structural Equation Modeling with the Partial Least Squares approach (SEM-PLS).
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West Science Social and Humanities Studies SEM-PLS was chosen because it is suitable for predictive analysis, can handle relatively small sample sizes, and is effective in examining complex relationships between latent variables.
The analysis was conducted using SmartPLS version 3.
2 Population and Sample The population of this study consisted of employees working in manufacturing companies that have begun technologies in their organizational or human resource management processes, including the use of digital systems in recruitment, employee evaluation, workforce monitoring, and other HR-related decision-making The sampling technique used was to ensure respondents met criteria relevant to the research objectives.
The selected respondents manufacturing companies, were familiar with or affected by AI-based systems in HR practices, and had worked in their organizations for at least one year.
Based on these criteria, a total of 150 respondents were selected as the research sample.
This sample size is considered adequate for SEM-PLS analysis, which typically requires a minimum of around 100 observations to produce reliable statistical results.
3 Data Collection Technique The data used in this study were primary data collected through the distribution of structured questionnaires to employees working in manufacturing The distributed both online and directly to respondents, who were asked to provide their perceptions regarding AI ethics in human transparency, and employee trust within their All measurement items were assessed using a five-point Likert scale ranging from 1 (Strongly Disagre.
, 2 (Disagre.
, 3 (Neutra.
, 4 (Agre.
, to 5 (Strongly Agre.
This Likert scale was used to effectively capture respondentsAo attitudes and perceptions toward the variables examined in the study 4 Research Variables Operational Definitions This study includes two independent variables and one dependent variable.
The first independent variable is Artificial Intelligence Ethics in Human Resources (X.
, which refers to the ethical principles guiding the use of artificial intelligence systems in human resource management processes and measures employeesAo perceptions of fairness, responsibility, and ethical considerations in AI-driven HR decision-making.
The second Algorithm Transparency (X.
, defined as the extent to which employees understand how AI systems process data and generate decisions, reflecting the clarity, explainability, and openness of algorithm-based HR systems.
The dependent variable is Employee Trust (Y), which refers to the level of confidence employees have in the organization and its use of AI-based systems in HR management, employeesAo organizational systems operate fairly, responsibly, and in their best interest.
5 Measurement Indicators The constructs used in this study were measured using several indicators adapted from previous studies related to AI governance, organizational transparency, and employee trust.
Table 1.
Measurement Variable Variable Artificial Intelligence Ethics in HR (X.
Code
AIHR1
AIHR2
Measurement Item The use of AI in HR decisions in my company follows ethical AI systems used by the company treat employees fairly.
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West Science Social and Humanities Studies AIHR3
AIHR4
Algorithm Transparency (X.
AT1
AT2
AT3
AT4
Employee Trust (Y) ET1
ET2
ET3
ET4
The organization ensures that AI decisions do not discriminate against employees.
The company monitors AI systems to ensure responsible use.
Employees are informed about the use of AI systems in HR The organization explains how AI systems make decisions.
AI-based HR decisions are understandable to employees.
Employees can obtain explanations for decisions made by AI I trust the HR systems used by my organization.
I believe AI-based HR decisions are fair.
I feel confident about the organizationAos use of AI technology.
The use of AI in my organization increases my trust in 6 Data Analysis Technique The data analysis in this study was Structural Equation Modeling with the Partial Least Squares approach (SEM-PLS) through SmartPLS version 3, which involves two main stages:
outer model evaluation and inner model The outer model evaluation was performed to assess the validity and reliability of the measurement model, including convergent validity measured by factor loading values where indicators above 0.
are considered valid.
Average Variance Extracted (AVE) values above 0.
50 indicating that constructs explain more than half of the variance of their indicators.
Composite Reliability (CR) values exceeding 0.
70 to demonstrate internal consistency, and CronbachAos Alpha values above 0.
70 to confirm construct reliability.
In addition, discriminant validity was evaluated using the Fornell-Larcker Criterion and the HeterotraitMonotrait Ratio (HTMT).
Meanwhile, the inner model evaluation was conducted to examine the structural relationships between variables, including the Coefficient of Determination (RA) explanatory power of independent variables on the dependent variable, path coefficients to determine the strength and direction of relationships between constructs, and hypothesis testing using the bootstrapping procedure in SmartPLS.
A relationship is considered statistically significant if the tstatistic value is greater than 1.
96 and the pvalue is less than 0.
05 at a 5% significance RESULTS AND DISCUSSION 1 Respondent Profile This study involved 150 employees working in manufacturing companies that have implemented or are familiar with artificial intelligence (AI) technologies in The respondents were selected using purposive sampling to ensure that participants had sufficient understanding of AI-based HR practices within their organizations.
The respondents include gender, age, education level, and work experience, which provide an overview of the background of the participants involved in the study.
The distribution of respondents based on these demographic characteristics is presented in Table 1.
Table 1.
Demographic Characteristics of Respondents .
= .
Characteristic Gender Age Category Male Female 20Ae30 years 31Ae40 years 41Ae50 years Frequency Percentage (%) Vol.
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West Science Social and Humanities Studies Education Work Experience >50 years Diploma Bachelor Master 1Ae5 years 6Ae10 years >10 years Table 1 presents the demographic characteristics of the 150 respondents involved in this study.
Based on gender distribution, the majority of respondents were male .
3%), while female respondents accounted for 38.
In terms of age, most respondents were between 31Ae40 years old .
7%), followed by those aged 20Ae30 years .
7%), 41Ae50 years .
7%), and above 50 .
0%).
Regarding background, the largest proportion of respondents held a bachelorAos degree .
3%),
followed by diploma holders .
7%) and those with a masterAos degree .
0%).
In terms of work experience, most respondents had worked for 6Ae10 years .
7%), followed by those with 1Ae5 years of experience .
0%) and more than 10 years of experience .
3%).
Overall, these demographic characteristics indicate that the respondents generally have sufficient educational background and work experience to understand AI-based human resource practices within manufacturing 2 Descriptive Statistics Descriptive statistics were calculated to understand the overall perceptions of respondents regarding the research variables.
Table 2 presents the mean and standard deviation values for each construct.
Table 3.
Descriptive Statistics Variable AI Ethics in HR Algorithm Transparency Employee Trust Table 3 presents the descriptive statistics of the variables examined in this The results indicate that AI Ethics in HR has the highest mean value (Mean = 4.
SD = 0.
, suggesting that respondents generally perceive the implementation of ethical principles in AI-based HR practices within their organizations as relatively strong.
Employee Trust also shows a high mean value (Mean = 4.
SD = 0.
, indicating that employees tend to have a positive level of trust toward their organizations in relation to the use of AI technologies in HR management.
Meanwhile.
Algorithm Transparency has a slightly lower mean value (Mean = 3.
SD =
, although it still reflects a generally Mean Standard Deviation positive perception among employees regarding the clarity and explainability of AIbased HR systems.
3 Measurement Model Evaluation (Outer Mode.
The measurement model evaluation was conducted to assess the reliability and validity of the constructs used in the study.
Convergent Validity Convergent validity was evaluated using factor loadings.
All indicators must have loading values above 0.
70 to be considered valid.
Table 4.
Outer Loadings Variable AI Ethics in HR Indicator AIHR1 Loading Vol.
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West Science Social and Humanities Studies Algorithm Transparency Employee Trust Table 4 presents the outer loading values for each indicator used to measure the constructs in this study.
The results indicate that all indicators have loading values above the recommended threshold of 0.
demonstrating strong convergent validity.
For the AI Ethics in HR variable, the loading values range from 0.
807 to 0.
854, indicating that all four indicators reliably represent the ethical implementation of AI in HR practices.
Similarly, the Algorithm Transparency variable shows loading values between 0.
847, confirming that the indicators effectively capture employeesAo perceptions AIHR2
AIHR3
AIHR4
AT1
AT2
AT3
AT4
ET1
ET2
ET3
ET4
regarding the clarity and explainability of AIbased HR systems.
Meanwhile, the Employee Trust variable exhibits the highest loading values, ranging from 0.
848 to 0.
suggesting that these indicators strongly reflect employeesAo confidence in the organizationAos use of AI technologies.
Reliability and AVE Reliability and convergent validity were further evaluated using CronbachAos Alpha.
Composite Reliability (CR), and Average Variance Extracted (AVE).
Table 5.
Reliability and Validity Test Variable AI Ethics in HR Algorithm Transparency Employee Trust CronbachAos Alpha Table 5 presents the results of the reliability and validity tests for the constructs used in this study.
The findings indicate that all variables meet the recommended reliability criteria, as shown by CronbachAos Alpha values above 0.
70, with AI Ethics in HR .
Algorithm Transparency .
, and Employee Trust .
demonstrating strong internal consistency.
Similarly, the Composite Reliability values for all constructs exceed the recommended threshold of 0.
70, with values 905, 0.
893, and 0.
929, respectively, indicating that the measurement items consistently represent their respective In addition, the Average Variance Extracted (AVE) values for all variables are 50, with AI Ethics in HR .
Composite Reliability AVE Algorithm Transparency .
Employee Trust .
, confirming adequate convergent validity.
Overall, these results demonstrate that the measurement model has satisfactory reliability and validity, indicating that the constructs are appropriate for further structural model analysis.
4 Structural Model Evaluation (Inner Mode.
Coefficient Determination (RA) The coefficient of determination (RA) measures how much variance in the dependent variable is explained by the independent variables.
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West Science Social and Humanities Studies Table 5.
R-Square Variable Employee Trust The RA value of 0.
635 indicates that 5% of the variance in employee trust can be explained by AI ethics in HR and algorithm transparency, while the remaining 37% is influenced by other variables not included in the model.
Hypothesis Testing Hypothesis testing was conducted using the bootstrapping procedure in SmartPLS 3.
The significance level used in this study was 5% .
-statistic > 1.
Table 7.
Hypothesis Testing Path AI Ethics in HR Ie Employee Trust Algorithm Transparency Ie Employee Trust Table 7 presents the results of hypothesis testing examining the influence of AI Ethics in HR and Algorithm Transparency on Employee Trust.
The findings indicate that AI Ethics in HR has a positive and significant effect on Employee Trust ( = 0.
478, t = 6.
p = 0.
, supporting H1.
This result suggests that when organizations implement ethical principles in AI-based HR practices, employees are more likely to develop trust in the organization and its decision-making Similarly.
Algorithm Transparency also shows a positive and significant influence on Employee Trust ( = 0.
364, t = 4.
852, p = .
, supporting H2.
This indicates that when employees clearly understand how AI systems process data and generate HR-related decisions, their confidence in organizational systems increases.
Discussion The results of this study indicate that AI ethics in human resources has a positive and significant influence on employee trust in the manufacturing industry.
The path coefficient value of 0.
47 suggests that ethical practices in the implementation of AI systems play a crucial role in strengthening employeesAo confidence in organizational decision-making processes.
When employees perceive that AI systems operate according to accountability, and non-discrimination, they are more likely to trust both the technology and the organization implementing it.
These Path Coefficient T-Statistic P-Value Result Supported Supported findings reinforce the argument that responsible AI governance is essential for maintaining positive relationships between employees and organizations in increasingly digitalized workplaces .
, .
The findings also reveal that influences employee trust, with a path coefficient of 0.
This result indicates that transparency in algorithmic decision-making can reduce uncertainty and enhance employeesAo confidence in AI-based HR When organizations provide clear explanations regarding how AI systems process data and generate decisions, employees are more likely to perceive these systems as legitimate and fair .
, .
Transparency therefore plays an important role in addressing concerns related to Aublack boxAy AI systems, which often create skepticism and resistance among employees due to the lack of clear understanding of how automated decisions are made.
Furthermore, the RA value of 0.
demonstrates that AI ethics and algorithm transparency collectively explain a substantial proportion of employee trust in organizations implementing AI-based HR practices.
This finding suggests that ethical governance and dimensions that organizations must consider when integrating AI technologies into human resource management systems.
By ensuring that AI systems are designed with ethical safeguards and transparent mechanisms.
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West Science Social and Humanities Studies organizations can significantly improve employeesAo perceptions of fairness and trust in digital HR processes.
From a managerial perspective, these findings highlight the importance of developing comprehensive ethical AI policies and implementing transparent algorithmic systems within organizations.
Companies should ensure that AI systems are designed, monitored, and regularly evaluated to prevent bias and discrimination in HR-related In addition, organizations should actively communicate with employees regarding the purpose, functioning, and limitations of AI technologies used in HR Providing such transparency can help employees better understand how organizational systems.
Overall, this study contributes to the growing body of literature on AI governance.
As organizations continue to integrate AI technologies into HR practices, maintaining strong ethical standards and promoting transparency will be essential for technological adoption.
By prioritizing these principles, organizations can foster stronger trust relationships with employees while maximizing the benefits of AI-driven HR systems in the modern workplace.
CONCLUSION
This study aimed to examine the influence of artificial intelligence (AI) ethics in human resources and algorithm transparency on employee trust in the manufacturing Based on the SEM-PLS analysis of data collected from 150 employees, the findings show that both AI ethics and influence employee trust.
Ethical practices in AI implementation contribute to employeesAo perceptions of fairness, responsibility, and organizational accountability, meaning that when organizations ensure AI systems operate according to ethical principles, employees are more likely to trust both the technology and the organizational systems that utilize it.
In addition, algorithm transparency plays an important role in strengthening employee trust, as transparent AI systems that provide clear explanations of decision-making processes help reduce When employees understand how AI-based HR systems function, they tend to perceive these technologies as more legitimate and trustworthy, which also encourages greater acceptance of AI-driven organizational practices.
Overall, these results highlight that organizations adopting AI technologies in HR management must prioritize ethical governance and transparent algorithmic processes to strengthen employee trust and support the sustainable integration of AI technologies in the workplace, while future research may further explore this topic by incorporating additional variables such as perceived fairness, organizational culture, or technological readiness.
REFERENCES