AI Ethics : An Empirical Study on the Views of Practitioners and Lawmakers

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ AI Ethics : An Empirical Study on the Views of Practitioners and Lawmakers © Authors 2023 Published version Khan Arif, Ali; Akbar, Muhammad Azeem; Fahmideh, Mahdi; Liang, Peng; Waseem, Muhammad; Ahmad, Aakash; Niazi, Mahmood; Abrahamsson, Pekka Khan Arif, A., Akbar, M. A., Fahmideh, M., Liang, P., Waseem, M., Ahmad, A., Niazi, M., & Abrahamsson, P. (2023). AI Ethics : An Empirical Study on the Views of Practitioners and Lawmakers. IEEE Transactions on Computational Social Systems, 10(6), 2971-2984. https://doi.org/10.1109/tcss.2023.3251729 2023 IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 1 AI Ethics: An Empirical Study on the Views of Practitioners and Lawmakers Arif Ali Khan , Member, IEEE, Muhammad Azeem Akbar , Member, IEEE, Mahdi Fahmideh , Member, IEEE, Peng Liang , Member, IEEE, Muhammad Waseem , Member, IEEE, Aakash Ahmad , Member, IEEE, Mahmood Niazi , and Pekka Abrahamsson Abstract— Artificial intelligence (AI) solutions and technologies are being increasingly adopted in smart systems contexts; however, such technologies are concerned with ethical uncertainties. Various guidelines, principles, and regulatory frameworks are designed to ensure that AI technologies adhere to ethical well-being. However, the implications of AI ethics principles and guidelines are still being debated. To further explore the significance of AI ethics principles and relevant challenges, we conducted a survey of 99 randomly selected representative AI practitioners and lawmakers (e.g., AI engineers and lawyers) from 20 countries across five continents. To the best of our knowledge, this is the first empirical study that unveils the perceptions of two different types of population (AI practitioners and lawmakers) and the study findings confirm that transparency, accountability, and privacy are the most critical AI ethics principles. On the other hand, lack of ethical knowledge, no legal frameworks, and lacking monitoring bodies are found to be the most common AI ethics challenges. The impact analysis of the challenges across principles reveals that conflict in practice is a highly severe challenge. Moreover, the perceptions of practitioners and lawmakers are statistically correlated with significant differences for particular principles (e.g. fairness and freedom) and challenges (e.g. lacking monitoring bodies and machine distortion). Our findings stimulate further research, particularly empowering existing capability maturity models to support ethics-aware AI systems’ development and quality assessment. Manuscript received 11 November 2022; revised 27 January 2023; accepted 22 February 2023. (Corresponding author: Arif Ali Khan.) This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the University of Jyväskylä, Finland. Arif Ali Khan is with the M3S Empirical Software Engineering Research Unit, University of Oulu, 90014 Oulu, Finland (e-mail: [email protected]). Muhammad Azeem Akbar is with the Software Engineering Department, Lappeenranta-Lahti University of Technology, 15210 Lappeenranta, Finland (e-mail: [email protected]). Mahdi Fahmideh is with the School of Business, University of Southern Queensland, Toowoomba, QLD 4350, Australia (e-mail: mahdi.fahmideh@ usq.edu.au). Peng Liang is with the School of Computer Science, Wuhan University, Wuhan 430072, China (e-mail: [email protected]). Muhammad Waseem is with the Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland (e-mail: muhammad.m. [email protected]). Aakash Ahmad is with the School of Computing and Communications, Lancaster University Leipzig, 04109 Leipzig, Germany (e-mail: [email protected]). Mahmood Niazi is with the Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia (e-mail: [email protected]). Pekka Abrahamsson is with the Faculty of Information Technology and Communication Sciences, Tampere University, 33014 Tampere, Finland (e-mail: [email protected]). Digital Object Identifier 10.1109/TCSS.2023.3251729 Index Terms— Accountable artificial intelligence, Artificial intelligence (AI), AI ethics, AI ethics principles, challenges, machine ethics. I. INTRODUCTION ARTIFICIAL Intelligence (AI) is expanded across a vast array of industries, including health, manufacturing, agriculture, and banking [1]. AI technologies have the potential to substantially transform society and offer various technical and societal benefits, which are expected to happen from high-level productivity and efficiency. In line with this, the ethical guidelines presented by the independent high-level expert group on artificial intelligence (AI HLEG) highlight the following [2]. “AI is not an end in itself, but rather a promising means to increase human flourishing, thereby enhancing individual and societal well-being and the common good, as well as bringing progress and innovation.” However, the promising advantages of AI technologies have been considered with worries that the complex and opaque systems might bring more social harms than benefits [1]. People start thinking beyond the operational capabilities of AI technologies and investigating the ethical aspects of developing strong and potentially life consequential technologies. For example, the U.S. government and many private companies do not use the virtual implications of decision-making systems in health, criminal justice, employment, and creditworthiness without ensuring that these systems are not coded intentionally or unintentionally with structural biases [1]. Concomitant with advances in AI systems, we witness the ethical failure scenarios. For example, a high rate of unsuccessful job applications that were processed by the Amazon recruitment system was later found biased in analysis of the selection criteria against women applicants and triggered discriminatory issues [3]. Since decisions and recommendations made by AI systems may undergone people lives, the need for developing pertinent policies and principles addressing the ethical aspects of AI systems is crucial. Otherwise, the harms caused by AI systems will jeopardize the control, safety, livelihood, and rights of people. AI systems are not only concerned with technical efforts but also need to incorporate the social, political, legal, and intellectual aspects. However, AI’s current state of ethics is broadly unknown to the public, practitioners, policy, and lawmakers [4], [5]. Extensively, the ethically aligned AI system should meet the following three components through the entire life cycle [2]: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 2 IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 1) compliance with all the applicable laws and regulations; 2) adherence to ethical principles and values; and 3) technical and social robustness. To the best of our knowledge, there is a dearth of empirical study to uncover the above core components in the view of industrial practitioners and lawmakers. For instance, as will be elaborated in Section VII, Vakkuri et al. [4] conducted a survey study to determine industrial perceptions based only on four AI ethics principles. Lu et al. [6] conducted interviews with researchers and practitioners to understand the AI ethics principles implications and the motivation for rooting these principles in the design practices. Similarly, Leikas et al. [7] mainly focused on AI ethics guidelines. Given the lack of empirical studies exploring principles and challenges associated with AI ethics, we strive to answer the following research questions: RQ1: What are the practitioners and lawmakers’ insights on AI ethics principles and challenges? Rationale: RQ1 aims to digest the perceptions of practitioners and lawmakers to empirically evaluate the systematic literature review (SLR) study-based identified AI ethics principles and challenges [8]. The answer to RQ1 provides a better understanding of the most common AI ethics principles and challenges with respect to practitioners and lawmakers’ point of views. RQ2: What would be the severity impacts of identified challenges across the AI ethics principles? Rationale: RQ2 aims to measure the severity impacts of challenging factors across AI ethics principles. The answer to RQ2 would inform practitioners for the most severe challenges before initiating the AI ethics activities. RQ3: How these challenges and principles are differently perceived by practitioners and lawmakers? Rationale: The empirical data were collected from two types of populations (practitioners and lawmakers). The answer to RQ3 would portray a better understanding of significant differences between the opinion of targeted populations for AI ethics principles and challenges. To address these RQs, we conducted a survey study by encapsulating the views and opinions of practitioners and lawmakers regarding AI ethics principles and challenges by collecting data from 99 respondents across 20 different countries. The rest of this article is structured as follows. Section II details the study background, and Section III presents the research methodology. Results and discussions are reported in Section IV. The key findings, along with the research and practical implications, are contextualized in Section V. Threats to the study findings are discussed in Section VI, and the related work review is provided in Section VII. Finally, Section VIII draws conclusions and potential future avenues. II. BACKGROUND Generally, AI ethics is classified under the umbrella of applied ethics, which mainly concerns with ethical issues associated with developing and using AI systems. It focuses on linking how an AI system could raise worries related to human autonomy, freedom in a democratic society, and quality of life. Ethical reflection across AI technologies could serve in achieving multiple societal purposes [2]. For instance, it can stimulate focusing on innovations that aim to foster ethical values and bring collective well-being. Ethically aligned or trustworthy AI technologies can flourish sustainable well-being in society by bringing prosperity, wealth maximization, and value creation [2]. It is vital to understand the development, deployment, and use of AI technologies to ensure that everyone can build a better future and live a thriving life in the AIbased world. However, the increasing popularity of AI systems has raised concerns such as reliability and impartiality of decision-making scenarios [2]. We need to make sure that decision-making support of AI technologies must have an accountable process to ensure that their actions are ethically aligned with human values that should not be compromised [2]. In this regard, different organizations and technology giants developed committees to draft the AI ethics guidelines. Google and SAP presented the guidelines and policies to develop ethically aligned AI systems [9]. Similarly, the Association of Computing Machinery (ACM), Access Now, and Amnesty International jointly proposed the principles and guidelines to develop an ethically mature AI system [9]. In Europe, the AI HLEG guidelines are developed for promoting trustworthy AI [2]. The ethically aligned design (EAD) guidelines are presented by IEEE, consisting of a set of principles and recommendations that focus on the technical and ethical values of AI systems [10]. In addition, the joint ISO/IEC international standard committee proposed the ISO/IEC JTC 1/SC 42 standard, which covers the entire AI ecosystem, including trustworthiness, computational approach, governance, standardization, and social concerns [11]. However, some researchers claim that the extant AI ethics guidelines and principles are not effectively adopted in industrial settings. McNamara et al. [12] conducted an empirical study to understand the influence of the ACM code of ethics in the software engineering decision-making process. Surprisingly, the study findings reveal that no evidence has been found that the ACM code of ethics regulates decision-making activities. Vakkuri et al. [13] conducted multiple interviews to know the status of ethical practices in the domain of the AI industry. The study findings uncover the fact that various guidelines are available; however, their deployment in industrial domains is far from being mature. The gap between AI ethics research and practice remains an ongoing challenge. To bridge this gap, we previously conducted an SLR study to provide a comprehensive and state-of-the-art overview of AI ethics principles and challenges [8]. A total of 27 primary studies are identified, and the systematic overview of the identified studies reveals 22 AI ethics principles and This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. KHAN et al.: AI ETHICS: AN EMPIRICAL STUDY ON THE VIEWS OF PRACTITIONERS AND LAWMAKERS 3 Fig. 1. Overview of the research methodology. 15 challenges. The frequently cited principles and challenges are thoroughly discussed and thematically mapped across different core categories. This study is extended based on the SLR findings [8] to provide empirical insights to know the significance of AI ethics principles, challenges, and their impact by encapsulating the views of AI practitioners and lawmakers. III. METHODOLOGY We deemed two groups of research participants that would be relevant to this survey—AI practitioners and lawmakers. On one hand, practitioners often make the design decisions and have higher ethical responsibilities compared to others. Practitioners often make the design decisions of complex autonomous systems with less ethical knowledge. The magnitude of risks in AI systems makes practitioners responsible for understanding ethical attributes. To achieve reliable outcomes, it is essential to know the practitioners understanding of AI ethics principles and challenges. On the other hand, law resolves everyday conflicts and sustains order in social life. People consider law an information source as it impacts social norms and values [14]. The aim of considering this type of population (lawmakers) is to understand the application of the law to AI ethics. The data collected from legislation personnel will uncover the question, of whether standing AI ethics principles are sufficient, or is there a need for innovative standards [14]? We used industrial collaboration contacts to search the AI practitioners and sent a formal invitation to participate in this survey. Moreover, various law forums across the world were sought, contacted, and requested to participate in this study. The targeted populations were approached using social media networks, including LinkedIn, WeChat, ResearchGate, Facebook, and personal email addresses. The overview of research methodology is shown in Fig. 1. The survey instrument consisted of four core sections: 1) demographics; 2) AI ethics principles; 3) challenges; and 4) challenges impact on principles. The survey questionnaire also includes open-ended questions to know the novel principles and challenges that were not identified in the SLR study [8]. The Likert scale is used to evaluate the significance of each principle and challenge and assess the severity level of the challenging factors. The survey instrument is structured both in English and Chinese language. The software industry in China is flourishing like never before, where AI is taking the front seat and is home to some of the leading technology giants in the world, such as Huawei, Alibaba, Baidu, Tencent, and Xiaomi. China is emerging as the global leader in the AI industry. The hype in the U.S.–China AI rivalry is an alarm over China’s rapid growth overtaking USA in the AI industry a decade ahead [15]. By 2025, China plans to achieve major breakthroughs in AI research and become a world-leading producer of critical AI applications. The country also plans to grow the AI industry by over 400 billion yuan (U.S. $61.4 billion) and aims to develop and upgrade ethical standards for AI in law [15]. However, in this study, it would be challenging to collect data from the Chinese industry because of the language barriers. Mandarin is the national and official language in China, unlike USA or India, where English is commonly used for official purposes. Therefore, the Chinese version of the survey instrument is developed to cover the major portion of the targeted population. Both English and Chinese versions of the survey instrument are available online for replication [16]. The piloting of the questionnaire is performed by inviting three external subject/domain experts. The experts’ suggestions were mainly related to the overall design and understandability of the survey questions. The suggested changes were incorporated, and the survey instrument was finalized based on the authors’ consensus (see Fig. 1). The final survey instrument was online deployed using Google forms (English version) and Tencent questionnaire (Chinese version). The first two authors engaged with the data collection process, while the next coauthors frequently monitored/screened the participants’ responses. The data collection process was started in September 2021 and ended up in April 2022 with initial 107 total responses. It should be noted that we provided the consensus details in the information sheet of the survey questionnaire [16] and only considered the agreed responses for further analysis. The manual screening revealed that eight responses were incomplete and we only considered 99 responses for the final data analysis. The third author mainly extracted and analyzed the survey data. The descriptive data were analyzed using the frequency analysis approach. The frequency-based tables and charts are created for the identified AI ethics principles and challenges (see Section IV). Frequency analysis is more suitable for analyzing a group of variables and for both numeric and ordinal types of data [17]. The significance of identified AI ethics principles and challenges is evaluated based on the level of agreement between the two types of populations (AI practitioners and lawmakers) (see Section IV-D). The same data analysis approach has been used in different other This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 4 IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS Fig. 2. Geographical distribution of survey participants. similar nature of studies [18], [19], [20]. We received several responses to the open-ended survey questions. However, the data were not strong enough to analyze formally; therefore, we provided it as quotes to support the study results (see Section IV-B). Finally, various Zoom consent meetings were called and invited all the authors to overview the study results and provide feedback. The study replication package is provided in [16]. IV. RESULTS AND DISCUSSION We now present the final results and discussions of the survey findings based on the final agreement of all the authors: 1) demography details of survey participants; 2) survey participants’ perceptions of AI ethics principles and challenges; 3) severity impact of identified challenges across the AI ethics principles; and 4) statistically significant differences between opinion of both types of populations (practitioners and lawmakers) for the identified principles and challenges. A. Demographic Details Frequency analysis was performed to organize the descriptive data. We noticed that 99 respondents from 20 countries across five continents with nine roles and ten different backgrounds participated in the survey study (see Figs. 2and 3). The organizational size (number of employees) of survey participants mostly ranges from 50 to 249, which is 28% of the total responses [see Fig. 3(a)]. We mapped the respondents’ roles across nine different categories using thematic mapping [see Fig. 3(b)]. The final results show that most of the respondents (29%) are classified across the law practitioner category. Similarly, the working domains of the participants’ organizations are conceptually framed in ten-core categories and the results revealed that most (19%) of the organizations are working on smart systems [see Fig. 3(c)]. Participants were asked to explain their opinions to perceive the significance of AI ethics in their respective organizations. The majority of the participants positively agreed. For instance, 77% mentioned that their organizations consider ethical aspects in AI processes or develop policies for AI Fig. 3. Organization size, professional roles, and product type. (a) Size of Organisation. (b) Professional Roles. (c) Types of Software/Systems. Fig. 4. Organization considering AI ethics and survey participants experience. (a) AI Ethics. (b) Years of Experience. projects, 12% answered negatively, and 10% were not sure about it [see Fig. 4(a)]. Of all the responses, majority (48%) have 3–5 years of experience working with AI focused projects as practitioners or lawmakers [see Fig. 4(b)]. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. KHAN et al.: AI ETHICS: AN EMPIRICAL STUDY ON THE VIEWS OF PRACTITIONERS AND LAWMAKERS 5 Fig. 5. Survey participants’ perceptions on AI ethics principles. B. AI Ethics Principles and Challenges (RQ1) The survey responses were classified as average agree, neutral, and average disagree (see Figs. 5and 6). We observed that (approximately 65%) of the respondents positively confirmed the AI ethics principles and challenges identified in the SLR study [8]. 1) AI Ethics Principles: The results illustrate that the majority of the survey participants positively agreed (approximately 64%) to consider the identified list of AI ethics principles (see Fig. 5). For instance, one survey participant mentioned the following. דThe listed AI ethics principles are comprehensive and extensive to cover various aspects of ethics in AI.” We noticed that 77.8% of survey respondents thought transparency as the most significant AI ethics principle. This is an interesting observation as transparency is equally confirmed as one of the core seven essential requirements by AI HLEG [2] for realizing the “trustworthy AI.” Transparency provides detailed explanations of logical AI models and decision-making structures understandable to the system stakeholders. Moreover, it deals with the public perceptions and understanding of how AI systems work. Broadly, it is a societal and normative ideal of “openness.” The second most significant principle to the survey participants was accountability (71.7%). It refers to the expectations or requirements that organizations or individuals need to satisfy throughout the lifecycle of an AI system. They should be accountable according to their roles and applicable regulatory frameworks for the system design, development, deployment, and operation by providing documentation on the decision-making process or conducting regular auditing with proper justification. Privacy is the third most frequently occurred principle, supported by 69.7% of the survey participants. It refers to preventing harm, a fundamental right specifically affected by the decision-making system. Privacy compels data governance throughout the system lifecycle, covering data quality, integrity, application domain, access protocols, and capability to process the data in a way that safeguards privacy. It must be ensured that the data collected and manipulated by the AI system shall not be used unlawfully or unfairly discriminate against human beings. For example, one of the respondents mentioned the following. דThe privacy of hosted data used in AI applications and the risk of data breaches must be considered.” In general, the survey findings of AI ethics principles are confirmatory to the widely adopted accountability, responsibility, and transparency (ART) framework [21] and the findings of an industrial empirical study conducted by Vakkuri et al. [13]. Both studies [13], [21] jointly considered transparency and accountability as the core AI ethics principles, which is consistent with the findings in this survey. On the contrary, we noticed that privacy has been ignored in both mentioned studies [13], [21] but is placed as the third most significant principle in this survey. The reason might be that, as more and more AI systems have been placed online, the significance of privacy and data protection is increasingly recognized [22]. Presently, various countries embarked on legislation to ensure the protection of data and privacy. 2) AI Ethics Challenges: Furthermore, the results reveal that the majority of the survey respondents (approx. 66%) confirmed the identified challenging factors [8] (see Fig. 6). Lack of ethical knowledge is considered as the most frequently cited challenge by (81.8%) of the survey participants. It exhibits that knowledge of ethical aspects across AI systems is largely ignored in industrial settings. There is a significant gap between research and practice in AI ethics. Extant guidelines and policies devised by researchers and regulatory bodies discussed different ethical goals for AI systems. However, these goals have not been widely adopted in the industrial domain because of limited knowledge of scaling them in practice. The findings are affirmative to the results of industrial study conducted by Vakkuri et al. [13], concluding that ethical aspects of AI systems are not exclusively considered, and it mainly happened because of a lack of knowledge, awareness, and personal commitment. We noticed that no legal framework (69.7%) is ranked as the second most common challenge for This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 6 IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS Fig. 6. Survey participants’ perceptions on AI ethics challenges. considering ethics in the AI domain. The proliferation of AI technologies in high-risk areas starts mounting the pressure of designing ethical and legal standards and frameworks to govern them [23]. It highlights the nuances of the debate on AI law and lays the groundwork for a more inclusive AI governance framework [24]. The framework shall focus on most pertinent ethical issues raised by the AI systems, the use of AI across industry and government organizations, and economic displacement (i.e., the ethical reply to the loss of jobs as a result of AI-based automation). The third most common challenging factor is lacking monitoring bodies, and it was revealed by (68.7%) of the survey participants. Lacking monitoring bodies refers to the lack of regulatory oversight to assess ethics in AI systems [8]. It raises the issue of public bodies’ empowerment to monitor and audit the enforcement of ethical concerns in AI technologies by the domain (e.g., health, transport, and education). One survey respondent mentioned the following. דI believe it shall be mandatory for the industry to get standard approval from monitoring bodies to consider ethics in the development process of AI systems.” Monitoring bodies extensively promote and observe the ethical values in society and evaluate technology development associated with ethical aspects of AI [2]. They would be tasked to advocate and define responsibilities and develop rules, regulations, and practices in a situation where the system takes a decision autonomously. The monitoring group should ensure “ethics by, in and for design” as mentioned in AI HLEG [2] guidelines. In addition, the survey participants elaborated on new challenging factors. For instance, one of the participants mentioned the following. דImplicit biases in AI algorithms such as data discrimination and cognitive biases could impact system transparency.” Similarly, the other respondent reported the following. דBiases in the AI system’s design might bring distress to a group of people or individuals.” Moreover, a survey respondent explicitly considered the lack of tools for ethical transparency and AI biases as significant challenges to AI ethics. We noticed that AI biases are reported as the most common additional challenge. It will be interesting to further explore: 1) the type of biases that might be embedded with the AI algorithms; 2) the causes of these biases; and 3) corresponding countermeasures to minimize the negative impact on AI ethics. C. Severity Impacts of Identified Challenges (RQ2) We selected the most frequently reported seven challenging factors and six principles discussed in our SLR study [8]. The aim is to investigate the severity impact of the seven challenges (i.e., lack of ethical knowledge, vague principles, highly general principles, conflict in practice, interpret principles differently, lack of technical understanding, and extra constraints) across the six AI ethics principles (i.e., transparency, privacy, accountability, fairness, autonomy, and explainability). The survey participants were asked to rate the severity impact using the Likert scale: short term (insignificant, minor, and moderate) and long term (major and catastrophic) (see Fig. 7). The results revealed that most challenges have long-term impacts on the principles (major and catastrophic). For the transparency principle, we noticed that the challenging factor interpret principles differently has significant longterm impacts, and 77% (i.e., 50% major and 27% catastrophic) of the survey participants agreed to it. The interpretation of ethical concepts can change for a group of people and individuals. For instance, the practitioners might perceive transparency differently (more focused on technical aspects) than law and policymakers, who have broad social concerns. Furthermore, lack of ethical knowledge has a short-term impact on the transparency principle, and it is evident from the survey findings supported by 52% (7% insignificant, 25% minor, and 20% moderate) responses. Lack of knowledge could be instantly covered by attaining knowledge, understanding, and awareness of transparency concepts. Conflict in practice is deemed the most significant challenge to the privacy principle. Hence, 74% (i.e., 53% major and 21% catastrophic) survey respondents considered it a long-term severe challenge. Various groups, organizations, and individuals might have opinion conflicts associated with privacy in AI ethics [25]. It is critical to interpret and understand privacy conflicts in practice. We noticed that (82%) of survey participants considered the challenging factor extra constraints as the most severe (long-term) challenge for both accountability and fairness principles. Situational constraints, including organizational politics, lack of information, and management interruption, could possibly interfere with the This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. KHAN et al.: AI ETHICS: AN EMPIRICAL STUDY ON THE VIEWS OF PRACTITIONERS AND LAWMAKERS 7 Fig. 7. Severity impacts of identified challenges. accountability and fairness measures [26]. It could negatively impact the employee’s motivation and interest to explicitly consider ethical aspects across the AI activities. Interestingly, (79%) of the survey respondents considered conflict in practice as the most common (long-term) challenge for autonomy and explainability principles. Overall, we could interpret that conflict in practice is the most severe challenge, and its average occurrence is >60% for all the principles. It gives a general understanding to propose specific solutions that focus on tackling the opinion conflict regarding the real-world implication of AI ethics principles. The results further reveal that lack of ethical knowledge has an average (28%) short-term impact across selected AI ethics principles. The lack of knowledge gap could be covered by conducting training sessions, workshops, certification, and encouraging social awareness of AI ethics [8]. Knowledge increases the possibility of AI ethics success and acceptance in the best practice of the domain. D. Statistical Inferences (RQ3) We performed nonparametric statistical analysis [27], [28] to evaluate the significant differences and similarities between the opinion of lawmakers and software practitioners. The same nonparametric statistical analysis is previously performed in different other similar nature of studies [18], [19], [29]. The frequency-based ranking of both datasets is identified for AI ethics principles (see Table I) and challenges (see Table V) to set common measures for nonparametric Spearman’s rankorder correlation coefficient. It gives the linear dependence between a set of variables, ranging from (rs (correlation coefficient) = +1 to −1), where +1 indicates a total linear dependency [27], [28]. 1) Significant Differences for AI Ethics Principles: Spearman’s rank-order correlation test was applied to statistically evaluate the significant differences between the practitioners and lawmakers perceptions on AI ethics principles. We obtained the Spearman’s rank-order correlation coefficient value (rs =0.819), which is statistically significant (p= 0.000) (see Table II). The value (rs =0.819) and the scatter plot given in Fig. 8show the strong correlation between the ranks of both datasets (lawmakers and software practitioners). The identified principles are widely discussed across multiple AI ethics guidelines, and it might be the reason why both practitioners and lawmakers equally agreed with the significance and implications of these principles. For example, transparency is a common AI ethics principle, and practitioners and lawmakers ranked it in the first position. However, we also noticed significant differences (p=0.000) between both types of the population. For instance, lawmakers ranked fairness at position five as the most important principle; however, the software practitioners placed it at position seven. It shows that fairness across AI activities is relatively important based on lawmakers’ perceptions. It is because fairness is a nontechnical and more socially used term. Laws such as EU GDPR impose This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. 8 IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS TABLE I AI ETHICS PRINCIPLES RANKS TABLE II PRACTITIONERS AND LAWMAKERS PERCEPTIONS CORRELATION FOR AI ETHICS PRINCIPLES Fig. 8. Scatter plot of ranks for AI ethics principles. concrete requirements on AI development organizations to safeguard fairness in AI system design, deployment, and data processing [30]. The low-ranked placement of fairness by the practitioners might be because of limited knowledge and understanding of interpreting fairness technically, e.g., fairness in AI by design. In addition to Spearman’s rank order correlation, we also applied the independent t-test to compare the mean differences of the ranks obtained from both types of population (see Tables III and IV). Since Levene’s test is slightly significant (i.e., p=0.051 >0.05), therefore, we assume that the variances are approximately equal. Based on this assumption, the results of t-test (i.e., t=0.942 and p=0.661 > 0.05) show that there are no high-level significant differences between both variables. The results show that the degree of agreement between lawmakers and practitioners concerning AI ethics principles is positive, meaning that both populations Fig. 9. Scatter plot of ranks for AI ethics challenges. (lawmakers and software practitioners) equally consider the importance of AI ethics principles. The group statistics for both variables are given in Table IV. 2) Significant Differences for AI Ethics Challenges: Similar to AI ethics principles, the identified challenges are ranked (see Table V) and applied Spearman’s rank-order correlation coefficient test to measure the significant differences. The correlation coefficient value (rs =0.628) shows a positive and statistically significant (p=0.012) correlation between both types of population (see Table VI). It indicates a moderate and statistically significant agreement between the opinions of lawmakers and practitioners concerning the AI ethics challenges (see Fig. 9). For example, lacking monitoring bodies is ranked second by the practitioners and fifth by the lawmakers. The practitioners mainly engage in team-oriented activities and are more concerned about human bias [31]. Continuous sociotechnical monitoring ensures delivering reliable, unbiased, and fair outcomes. Avoiding proper monitoring deems to bring high ethical harm to practitioners and increase reputational risk [31]. We also applied the independent t-test (see Tables VII and VIII) to assess the mean differences between both types of the population with respect to AI ethics challenges. The calculated significance value of Levene’s test is (p=0.051 >0.05); therefore, we assume the variances equally (see Table VII). The t-test results, assuming equal variances (t=1.291 and p=0.207 >0.05), show that practitioners and lawmakers consider the significance of identified challenges equally. We could suppose that practitioners and lawmakers are equally aware of the reported challenges and understand their importance. The group statistics for both variables are provided in Table VIII. Overall, we believe that practitioners and lawmakers are on the same page in considering AI ethics principles and challenges. However, for AI ethics challenges, the perceptions of practitioners and lawmakers are slightly different. We noticed that various AI ethics principles and guidelines are released in private and public sectors, which are very abstract, and incoherent for various stakeholders to implement [32]. The challenges of interpreting these vague principles are different with respect to the targeted group of stakeholders, This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.