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Ethical Risks in Artificial Intelligence-Based Performance Assessment Within Asian Companies
Abstract
Technology is an inseparable part of a business enterprise since it influences its processes, structure, and strategies. The paradigm shift in terms of cloud computing, robotics, and artificial intelligence (AI) has paved the way for the next wave of digital economy termed as Business 5.0. Apart from business model disruption, artificial intelligence has impacted the most vital of resources – the human resource. The questions of morality and ethics are not new to the digital revolution, be it business processes or web-based social networks. The chapter aims to unearth the ethical challenges inherent in an AI-powered performance management system, with particular reference to Asia. It identifies how data-driven decisions on employee performance may be replete with serious ethical fallout and fail to provide holistic performance measurement. Despite the advantages, a lack of the human element occurs as the manager assumes the role of a moderator.
... Employees might feel that their work is being reduced to data points rather than being appreciated for its intrinsic value. Conversely, if managed ethically, AI can contribute to a more inclusive and supportive workplace by identifying areas for improvement and promoting fair practices [1], [2]. ...
As artificial intelligence (AI) increasingly integrates into Human Resources (HR) practices, it is essential to explore the ethical implications of this technological advancement. This paper examines the impact of AI on job performance and organizational culture, focusing on key ethical considerations. AI-driven tools in HR, such as recruitment algorithms, performance evaluations, and employee monitoring systems, promise efficiency and objectivity but also raise concerns about fairness, transparency, and privacy. This study investigates how these tools affect job performance by potentially reinforcing biases or creating a surveillance-oriented culture. Furthermore, it explores how AI influences organizational culture, addressing issues such as employee trust, job satisfaction, and the role of human judgment in HR decisions. By analyzing case studies and theoretical frameworks, this paper aims to provide a comprehensive overview of the ethical challenges posed by AI in HR. The findings suggest that while AI can enhance operational efficiency, it is crucial for organizations to implement ethical guidelines and safeguard measures to ensure that these technologies support rather than undermine positive job performance and a healthy organizational culture. This research underscores the need for ongoing ethical oversight and balanced approaches to integrate AI in HR practices, aiming to foster both innovation and ethical responsibility.
Abstract
Purpose – To investigate the relationship between artificial intelligence strategy (AIS), creativity-oriented HRM (CHRM), and knowledge-sharing quality (KSQ). At individual and organisational levels, this paper measures also the innovative work behaviour (IIWB) and effective performance (OEP) of international organisations conducting AI-powered business practices in Egypt.
Design/methodology/approach – The authors presented a multilevel-model, after reviewing the relevant literature, and tested it through employing mixed-methods approach. Data were collected from 168 questionnaires answered by AI-experts at IT departments of 20 international AI-powered organisations in Egypt in addition to 25 depth interviews, AI-based focus group and international forum.
Findings – Following PLS-SEM approach, results revealed that AIS affects positively and significantly KSQ and CHRM. CHRM affects positively and significantly KSQ and IIWB. KSQ affects positively and significantly OEP and IIWB. The significant positive direct AIS-OEP relationship was not supported yet the significant positive indirect relationship via KSQ was supported.
Originality/value – Empirically, it is the first research that assessed AIS-CHRM-KSQ relationship and its effect on IIWB and OEP of AI-powered businesses from 7 sectors of an emerging economy. Conceptually, the authors adopted an interdisciplinary approach while reflecting on the literature that studied AIS implementation in different business functions (production, operations and supply-chain management, human resources management, strategic management and marketing).
Practical implications – Strategic leaders and managers of different functional areas can benefit from the empirical findings of this study as well as from the examples of best AI-enhanced practices drawn from the literature.
Keywords – Artificial intelligence strategy, AI-powered business functions, Creativity-oriented HRM, Knowledge-sharing quality, Organisational effective performance, Innovative work behaviour, Operations management, Strategic management, Expert system, Machine learning, Forecasting.
Track – e-Business and e-Government.
Suggested Citation: Younis, R.A.A. and Adel, H.M. (2020), “Artificial intelligence strategy, creativity-oriented HRM and knowledge-sharing quality: Empirical analysis of individual and organisational performance of AI-powered businesses”, The Annual International Conference of The British Academy of Management (BAM) 2020: Innovating for a Sustainable Future, London, United Kingdom, 2-4 September. http://dx.doi.org/10.2139/ssrn.4127128
Social trends and information technology are creating pressure, resulting in organizations being forced to update and recreate themselves. Against this backdrop there is an increasing trend to adopt artificial intelligence technologies. This study aims to explore and investigate the effect of Artificial Intelligence (AI) on Human Resources (HR) practices in UAE companies with the help of mixed-method design. A mixed-method design was applied to explore and test the research questions. Semi-structured interviews and survey were conducted, respectively for both the study designs. AI and HR personnel were approached as sample participants for this study. Thematic analysis and PLS-SEM (Partial least squares path modeling) were used to analyze the data, respectively. Positive and significant effects of planned training and development process (0.231→0.021), tactical performance appraisal and integration of AI (0.719→0.000), and integration of AI and efficient HR practices (0.204→0.131) were shown. The moderating role of ease of use on integration of AI and efficient HR practices was negative and insignificant. AI plays an essential role in shifting the HR functions to enter the digitization era. The types of businesses and abilities require some activities such as training and development due to the increased use of technology.
Companies are increasingly using artificial intelligence (AI) to provide performance feedback to employees, by tracking employee behavior at work, automating performance evaluations, and recommending job improvements. However, this application of AI has provoked much debate. On the one hand, powerful AI data analytics increase the quality of feedback, which may enhance employee productivity (“deployment effect”). On the other hand, employees may develop a negative perception of AI feedback once it is disclosed to them, thus harming their productivity (“disclosure effect”). We examine these two effects theoretically and test them empirically using data from a field experiment. We find strong evidence that both effects coexist, and that the adverse disclosure effect is mitigated by employees’ tenure in the firm. These findings offer pivotal implications for management theory, practice, and public policies.
Managerial abstract
Artificial Intelligence (AI) technologies are bound to transform how companies manage employees. We examine the use of AI to generate performance feedback for employees. We demonstrate that AI significantly increases the accuracy and consistency of the analyses of information collected, and the relevance of feedback to each employee. These advantages of AI help employees achieve greater job performance at scale, and thus create value for companies. However, our study also alerts companies to the negative effect of disclosing using AI to employee that results from employees’ negative perceptions about the deployment of AI, which offsets the business value created by AI. To alleviate value-destroying disclosure effect, we suggest that companies be more proactive in communicating with their employees about the objectives, benefits, and scope of AI applications in order to assuage their concerns. Moreover, the result of the allayed negative AI disclosure effect among employees with a longer tenure in the company suggests that companies may consider deploying AI in a tiered instead of a uniform fashion, i.e., using AI to provide performance feedback to veteran employees but using human managers to provide performance feedback to novices.
Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world.
This research addressed the management awareness about the ethical and moral aspects of artificial intelligence (AI). It is a general trend to speak about AI, and many start-ups and established companies are communicating about the development and implementation of AI solutions. Therefore, it is important to consider different perspectives besides the technology and data as the key elements for AI systems. The way in which societies are interacting and organising themselves will change. Such transformations require diverse perspectives from the society and particularly from AI system developers for shaping the humanity of the future. This research aimed to overcome this barrier with the answers for the question: What kind of awareness does the management of AI companies have about the social impact of its AI product or service? The central research question was divided into five sub-questions that were answered by a fundamental literature review and an empirical research study. This covered the management understanding of the terms moral, ethics, and artificial intelligence; the internal company prioritization of moral and ethics; and the involved stakeholders in the AI product or service development. It analysed the known and used ethical AI guidelines and principles. In the end, the social responsibility of the management regarding AI systems was analysed and compared.
In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence (AI), entitled ‘New Generation Artificial Intelligence Development Plan’ (新一代人工智能发展规划). This strategy outlined China’s aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan (ca. 150 billion dollars) industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents.
Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest profile sets of ethical principles for AI. We assess whether these principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts.
Purpose
This article aims to establish the need for professionals and industry to get together on a common platform to engage in discussions, identify innovations and set standards. The movement of employees from unorganized to organized segment is accelerating and subsequently the cost of managing employees is also going to increase manifold for Indian organizations. The HR Tech landscape is pegged at $400m annually and expected to grow significantly.
Design/methodology/approach
The paper looks at the HR Tech landscape and posits how HR technologies can help companies save significant costs, and also bring in advanced techniques such as automation and analytics which will improve work efficiency.
Findings
While big enterprises are also working in the area of HR technology, startups are showing rapid progress, owing to the fact that they do not have to cope with legacy data/technology/operations. Challenges still remain such as failure to lay groundwork for adoption by employees.
Practical implications
While there are diverse products and technologies in the market, the core challenge is to find the right product-market fit. HR buyers have to ensure that the solutions are future proof.
Social implications
Automation is going to affect a lot of jobs, certain profiles more so than others. At a larger scale, integrating automation in work processes will have a substantial adverse impact on employees in these job profiles.
Originality/value
The HR Tech landscape is inundated with technology-first startups. There is a dearth of academic literature. There is also a cogent need for professionals and industry to get together on a common platform to engage in discussions, identify innovations, and set standards.
Despite its benefits, information technology (IT) use is associated with serious negative effects on individuals.
For example, technostress and IT addiction can harm IT users’ organizational performance
and everyday well-being. Such dark side phenomena have become more evident since IT has transmuted
into a major component of humans’ job routines and private lives. However, since current information
systems (IS) research on the dark side is in an early and fragmented stage, there is a need
for a synthesis. To address this need, we conducted a literature review of 37 articles published in the
IS basket journals between 1995 and 2015. We detected four key phenomena: technostress, information
overload, IT addiction, and IT anxiety. Within the analysis of the articles, we paid special attention
to the antecedents, consequences, and mitigation mechanisms of each phenomenon. Based on
our findings, this article contributes to IS research by pointing out unmapped territories in the IS basket
and suggesting five areas for future research: (1) Technostress mitigation mechanisms in everyday
use of IT, (2) IT addiction in organizational use of IT, (3) Information overload in everyday use of IT,
(4) IT anxiety in everyday use of IT, and (5) Personal IT actions as mitigation mechanisms.
The purpose of this research was to develop a taxonomy of adaptive job performance and examine the implications of this taxonomy for understanding, predicting, and training adaptive behavior in work settings. Two studies were conducted to address this issue. In Study 1, over 1,000 critical incidents from 21 different jobs were content analyzed to identify an 8-dimension taxonomy of adaptive performance. Study 2 reports the development and administration of an instrument, the Job Adaptability Inventory, that was used to empirically examine the proposed taxonomy in 24 different jobs. Exploratory factor analyses using data from 1,619 respondents supported the proposed 8-dimension taxonomy from Study 1. Subsequent confirmatory factor analyses on the remainder of the sample (n = 1,715) indicated a good fit for the 8-factor model. Results and implications are discussed.
Purpose – This narrative review presents a multi-stakeholder ethical framework for AI-augmented HRM, based on extant research in the domains of ethical HRM and ethical AI. More specifically, we identify critical ethical issues pertaining to AI-augmented HRM functions and suggest ethical principles to address these issues by identifying the relevant stakeholders based on the responsibility ethics approach.
Design/methodology/approach – This paper follows a narrative review approach by first identifying various ethical/codes/issues/dilemmas discussed in HRM and AI. We next discuss ethical issues concerning AI-augmented HRM, drawing from recent literature. Finally, we propose ethical principles for AI-augmented HRM and stakeholders responsible for managing those issues.
Findings – The paper summarises key findings of extant research in the ethical HRM and AI domain and provides a multi-stakeholder ethical framework for AI-augmented HRM functions.
Originality/value – This research’s value lies in conceptualising a multi-stakeholder ethical framework for AI-augmented HRM functions comprising eleven ethical principles. The research also identifies the class of stakeholders responsible for identified ethical principles. The research also presents future research directions based on the proposed model.
The purpose of this study is to critically review current studies on employees’ adaptive performance and to integrate the findings to provide insights on how to develop employees’ adaptability. It also aims to identify gaps in the research to guide future studies on adaptive performance in the field of human resource development. By reviewing 34 empirical studies on adaptive performance published between 1999 and 2016, we identified 22 antecedents of adaptive performance at the individual (nine), job (six), group (three), and organizational (four) levels. Our analyses reveal that a significant amount of research has focused more on individual characteristics than on other aspects affecting employees’ high adaptive performance. Discussion, implications, and recommendations for future research are elaborated.
Purpose
Artificial intelligence (AI) and robotics are already here, influencing almost every single industry in one way or another. Organisational scientists at Qlearsite use AI and machine learning to unleash the business potential languishing in workforce data – something previously unusable because of its format. This paper aims to track the initial implementation of AI up to present day, based on Qlearsite’s corporate experience.
Design/methodology/approach
Using actual experiences, Qlearsite were tasked with demonstrating how businesses can better understand their workforce, identify performance hurdles and develop strategies to clear them, ultimately improving productivity.
Findings
While initial implementation strategies may have faced challenges by senior human resources (HR) members across industries, the multiple benefits are evident and measurable against corporate success and productivity levels today.
Originality/value
Where HR leaders and senior decision-makers struggle to solve ongoing issues, such as absenteeism, an acute problem for the financial services industry. This paper demonstrates the true value of AI, with complex issues solved by data analysis – a monumental and expensive task for any employee to carry out.
Executives know that a company's measurement systems strongly affect employee behaviors. But the traditional financial performance measures that worked for the industrial era are out of sync with the skills organizations are trying to master. Frustrated by these inadequacies, some managers have abandoned financial measures like return on equity and earnings per share. "Make operational improvements, and the numbers will follow,"the argument goes. But managers want a balanced presentation of measures that will allow them to view the company from several perspectives at once. In this classic article from 1992, authors Robert Kaplan and David Norton propose an innovative solution. During a yearlong research project with 12 companies at the leading edge of performance management, the authors developed a "balanced scorecard;" a new performance measurement system that gives top managers a fast but comprehensive view of their business. The balanced scorecard includes financial measures that tell the results of actions already taken. And it complements those financial measures with three sets of operational measures related to customer satisfaction, internal processes, and the organization's ability to learn and improve-the activities that drive future financial performance. The balanced scorecard helps managers look at their businesses from four essential perspectives and answer Some important questions. First, How do customers see us? Second, What must we excel at? Third, Can we continue to improve and create value? And fourth, How do we appear to shareholders? By looking at all of these parameters, managers can determine whether improvements in one area have come at the expense of another. Armed with that knowledge, the authors say, executives can glean a complete picture of where the company stands-and where it's headed.
Modern work is frequently characterized by jobs where adaptive performance (AP) is crucial for employees to succeed in light of new or altered task demands. This recognition has fueled growing interest in AP as a dimension of workplace performance. To this point, however, research on AP has evolved from disparate perspectives and methods, resulting in fragmentation and a less than coherent knowledge base. This paper presents a comprehensive review of research studies regarding the nomological network of individual AP. In doing so, we synthesize the current knowledge base surrounding correlates of AP, elucidate current ambiguities, and suggest directions for future research efforts. We conclude that although the extant AP literature has amassed a critical body of studies linking various predictors to successful AP outcomes, much remains unknown, most critically regarding the implications of different methods of assessing AP, the effects of different types of changes in the task environment, the process of AP, and the steps organizations can take to foster AP among their employees. We hope that our synthesis and analysis paves the way for efforts to address these important questions. Copyright © 2014 John Wiley & Sons, Ltd.
International observers and critics often attack China's Internet policy on the basis of liberal values. If China's Internet is designed and built on Confucian values that are distinct from, and sometimes incompatible to, liberal values, then the liberalist critique ought to be reconsidered. In this respect, Mary Bockover's “Confucian Values and the Internet: A Potential Conflict” appears to be the most direct attempt to address this issue. Yet, in light of developments since its publication in 2003, it is time to re-examine this issue. In this paper, I revisit Bockover’s argument and show why it fails. Using social media as an example, I offer an alternative argument to show why the Internet remains largely incompatible with Confucian values. I end this paper by suggesting how to recontextualise the Confucian way of life and to redesign social media in accordance to Confucian values in the information society.
For the most part, managers looking to cure their organizational ills rely on obsolete knowledge they picked up in school, long-standing but never proven traditions, patterns gleaned from experience, methods they happen to be skilled in applying, and information from vendors. They could learn a thing or two from practitioners of evidence-based medicine, a movement that has taken the medical establishment by storm over the past decade. A growing number of physicians are eschewing the usual, flawed resources and are instead identifying, disseminating, and applying research that is soundly conducted and clinically relevant. It's time for managers to do the same. The challenge is, quite simply, to ground decisions in the latest and best knowledge of what actually works. In some ways, that's more difficult to do in business than in medicine. The evidence is weaker in business; almost anyone can (and many people do) claim to be a management expert; and a motley crew of sources--Shakespeare, Billy Graham,Jack Welch, Attila the Hunare used to generate management advice. Still, it makes sense that when managers act on better logic and strong evidence, their companies will beat the competition. Like medicine, management is learned through practice and experience. Yet managers (like doctors) can practice their craft more effectively if they relentlessly seek new knowledge and insight, from both inside and outside their companies, so they can keep updating their assumptions, skills, and knowledge.
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