Abstract
This scoping review examines the research landscape about publics’ views on the ethical challenges of AI. To elucidate how the concerns voiced by the publics are translated within the research domain, this study scrutinizes 64 publications sourced from PubMed® and Web of Science™. The central inquiry revolves around discerning the motivations, stakeholders, and ethical quandaries that emerge in research on this topic. The analysis reveals that innovation and legitimation stand out as the primary impetuses for engaging the public in deliberations concerning the ethical dilemmas associated with AI technologies. Supplementary motives are rooted in educational endeavors, democratization initiatives, and inspirational pursuits, whereas politicization emerges as a comparatively infrequent incentive. The study participants predominantly comprise the general public and professional groups, followed by AI system developers, industry and business managers, students, scholars, consumers, and policymakers. The ethical dimensions most commonly explored in the literature encompass human agency and oversight, followed by issues centered on privacy and data governance. Conversely, topics related to diversity, nondiscrimination, fairness, societal and environmental well-being, technical robustness, safety, transparency, and accountability receive comparatively less attention. This paper delineates the concrete operationalization of calls for public involvement in AI governance within the research sphere. It underscores the intricate interplay between ethical concerns, public involvement, and societal structures, including political and economic agendas, which serve to bolster technical proficiency and affirm the legitimacy of AI development in accordance with the institutional norms that underlie responsible research practices.
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1 Introduction
Current advances in the research, development, and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics that is accompanied by calls for AI technology to be democratically accountable and trustworthy from the publics’Footnote 1 perspective [1,2,3,4,5]. Consequently, several ethics guidelines for AI have been released in recent years. As of early 2020, there were 167 AI ethics guidelines documents around the world [6]. Organizations such as the European Commission (EC), the Organization for Economic Co-operation and Development (OECD), and the United Nations Educational, Scientific and Cultural Organization (UNESCO) recognize that public participation is crucial for ensuring the responsible development and deployment of AI technologies,Footnote 2 emphasizing the importance of inclusivity, transparency, and democratic processes to effectively address the societal implications of AI [11, 12]. These efforts were publicly announced as aiming to create a common understanding of ethical AI development and foster responsible practices that address societal concerns while maximizing AI’s potential benefits [13, 14]. The concept of human-centric AI has emerged as a key principle in many of these regulatory initiatives, with the purposes of ensuring that human values are incorporated into the design of algorithms, that humans do not lose control over automated systems, and that AI is used in the service of humanity and the common good to improve human welfare and human rights [15]. Using the same rationale, the opacity and rapid diffusion of AI have prompted debate about how such technologies ought to be governed and which actors and values should be involved in shaping governance regimes [1, 2].
While industry and business have traditionally tended to be seen as having no or little incentive to engage with ethics or in dialogue, AI leaders currently sponsor AI ethics [6, 16, 17]. However, some concerns call for ethics, public participation, and human-centric approaches in areas such as AI with high economic and political importance to be used within an instrumental rationale by the AI industry. A growing corpus of critical literature has conceived the development of AI ethics as efforts to reduce ethics to another form of industrial capital or to coopt and capture researchers as part of efforts to control public narratives [12, 18]. According to some authors, one of the reasons why ethics is so appealing to many AI companies is to calm critical voices from the publics; therefore, AI ethics is seen as a way of gaining or restoring trust, credibility and support, as well as legitimation, while criticized practices are calmed down to maintain the agenda of industry and science [12, 17, 19, 20].
Critical approaches also point out that despite regulatory initiatives explicitly invoking the need to incorporate human values into AI systems, they have the main objective of setting rules and standards to enable AI-based products and services to circulate in markets [20,21,22] and might serve to avoid or delay binding regulation [12, 23]. Other critical studies argue that AI ethics fails to mitigate the racial, social, and environmental damage of AI technologies in any meaningful sense [24] and excludes alternative ethical practices [25, 26]. As explained by Su [13], in a paper that considers the promise and perils of international human rights in AI governance, while human rights can serve as an authoritative source for holding AI developers accountable, its application to AI governance in practice shows a lack of effectiveness, an inability to effect structural change, and the problem of cooptation.
In a value analysis of AI national strategies, Wilson [5] concludes that the publics are primarily cast as recipients of AI’s abstract benefits, users of AI-driven services and products, a workforce in need of training and upskilling, or an important element for thriving democratic society that unlocks AI's potential. According to the author, when AI strategies articulate a governance role for the publics, it is more like an afterthought or rhetorical gesture than a clear commitment to putting “society-in-the-loop” into AI design and implementation [5, pp. 7–8]. Another study of how public participation is framed in AI policy documents [4] shows that high expectations are assigned to public participation as a solution to address concerns about the concentration of power, increases in inequality, lack of diversity, and bias. However, in practice, this framing thus far gives little consideration to some of the challenges well known for researchers and practitioners of public participation with science and technology, such as the difficulty of achieving consensus among diverse societal views, the high resource requirements for public participation exercises, and the risks of capture by vested interests [4, pp. 170–171]. These studies consistently reveal a noteworthy pattern: while references to public participation in AI governance are prevalent in the majority of AI national strategies, they tend to remain abstract and are often overshadowed by other roles, values, and policy concerns.
Some authors thus contended that the increasing demand to involve multiple stakeholders in AI governance, including the publics, signifies a discernible transformation within the sphere of science and technology policy. This transformation frequently embraces the framework of “responsible innovation”,Footnote 3 which emphasizes alignment with societal imperatives, responsiveness to evolving ethical, social, and environmental considerations, and the participation of the publics as well as traditionally defined stakeholders [3, 28]. When investigating how the conception and promotion of public participation in European science and technology policies have evolved, Macq, Tancoine, and Strasser [29] distinguish between “participation in decision-making” (pertaining to science policy decisions or decisions on research topics) and “participation in knowledge and innovation-making”. They find that “while public participation had initially been conceived and promoted as a way to build legitimacy of research policy decisions by involving publics into decision-making processes, it is now also promoted as a way to produce better or more knowledge and innovation by involving publics into knowledge and innovation-making processes, and thus building legitimacy for science and technology as a whole” [29, p. 508]. Although this shift in science and technology research policies has been noted, there exists a noticeable void in the literature in regard to understanding how concrete research practices incorporate public perspectives and embrace multistakeholder approaches, inclusion, and dialogue.
While several studies have delved into the framing of the publics’ role within AI governance in several instances (from Big Tech initiatives to hiring ethics teams and guidelines issued from multiple institutions to governments’ national policies related to AI development), discussing the underlying motivations driving the publics’ participation and the ethical considerations resulting from such involvement, there remains a notable scarcity of knowledge concerning how publicly voiced concerns are concretely translated into research efforts [30, pp. 3–4, 31, p. 8, 6]. To address this crucial gap, our scoping review endeavors to analyse the research landscape about the publics’ views on the ethical challenges of AI. Our primary objective is to uncover the motivations behind involving the publics in research initiatives, identify the segments of the publics that are considered in these studies, and illuminate the ethical concerns that warrant specific attention. Through this scoping review, we aim to enhance the understanding of the political and social backdrop within which debates and prior commitments regarding values and conditions for publics’ participation in matters related to science and technology are formulated and expressed [29, 32, 33] and which specific normative social commitments are projected and performed by institutional science [34, p. 108, [35, p. 856].
2 Methods
We followed the guidance for descriptive systematic scoping reviews by Levac et al. [36], based on the methodological framework developed by Arksey and O’Malley [37]. The steps of the review are listed below:
2.1 Stage 1: identifying the research question
The central question guiding this scoping review is the following: What motivations, publics and ethical issues emerge in research addressing the publics’ views on the ethical challenges of AI? We ask:
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What motivations for engaging the publics with AI technologies are articulated?
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Who are the publics invited?
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Which ethical issues concerning AI technologies are perceived as needing the participation of the publics?
2.2 Stage 2: identifying relevant studies
A search of the publications on PubMed® and Web of Science™ was conducted on 19 May 2023, with no restriction set for language or time of publication, using the following search expression: (“AI” OR “artificial intelligence”) AND (“public” OR “citizen”) AND “ethics”. The search was followed by backwards reference tracking, examining the references of the selected publications based on full-text assessment.
2.3 Stage 3: study selection
The inclusion criteria allowed only empirical, peer-reviewed, original full-length studies written in English to explore publics’ views on the ethical challenges of AI as their main outcome. The exclusion criteria disallowed studies focusing on media discourses and texts. The titles of 1612 records were retrieved. After the removal of duplicates, 1485 records were examined. Two authors (HM and SS) independently screened all the papers retrieved initially, based on the title and abstract, and afterward, based on the full text. This was crosschecked and discussed in both phases, and perfect agreement was achieved.
The screening process is summarized in Fig. 1. Based on title and abstract assessments, 1265 records were excluded because they were neither original full-length peer-reviewed empirical studies nor focused on the publics’ views on the ethical challenges of AI. Of the 220 fully read papers, 54 met the inclusion criteria. After backwards reference tracking, 10 papers were included, and the final review was composed of 64 papers.
2.4 Stage 4: charting the data
A standardized data extraction sheet was initially developed by two authors (HM and SS) and completed by two coders (SS and LN), including both quantitative and qualitative data (Supplemental Table “Data Extraction”). We used MS Excel to chart the data from the studies.
The two coders independently charted the first 10 records, with any disagreements or uncertainties in abstractions being discussed and resolved by consensus. The forms were further refined and finalized upon consensus before completing the data charting process. Each of the remaining records was charted by one coder. Two meetings were held to ensure consistency in data charting and to verify accuracy. The first author (HM) reviewed the results.
Descriptive data for the characterization of studies included information about the authors and publication year, the country where the study was developed, study aims, type of research (quantitative, qualitative, or other), assessment of the publics’ views, and sample. The types of research participants recruited as publics were coded into 11 categories: developers of AI systems; managers from industry and business; representatives of governance bodies; policymakers; academics and researchers; students; professional groups; general public; local communities; patients/consumers; and other (specify).
Data on the main motivations for researching the publics’ views on the ethical challenges of AI were also gathered. Authors’ accounts of their motivations were synthesized into eight categories according to the coding framework proposed by Weingart and colleagues [33] concerning public engagement with science and technology-related issues: education (to inform and educate the public about AI, improving public access to scientific knowledge); innovation (to promote innovation, the publics are considered to be a valuable source of knowledge and are called upon to contribute to knowledge production, bridge building and including knowledge outside ‘formal’ ethics); legitimation (to promote public trust in and acceptance of AI, as well as of policies supporting AI); inspiration (to inspire and raise interest in AI, to secure a STEM-educated labor force); politicization (to address past political injustices and historical exclusion); democratization (to empower citizens to participate competently in society and/or to participate in AI); other (specify); and not clearly evident.
Based on the content analysis technique [38], ethical issues perceived as needing the participation of the publics were identified through quotations stated in the studies. These were then summarized in seven key ethical principles, according to the proposal outlined by the EC's Ethics Guidelines for Trustworthy AI [39]: human agency and oversight; technical robustness and safety; privacy and data governance; transparency; diversity, nondiscrimination and fairness; societal and environmental well-being; and accountability.
2.5 Stage 5: collating, summarizing, and reporting the results
The main characteristics of the 64 studies included can be found in Table 1. Studies were grouped by type of research and ordered by the year of publication. The findings regarding the publics invited to participate are presented in Fig. 2. The main motivations for engaging the publics with AI technologies and the ethical issues perceived as needing the participation of the publics are summarized in Tables 2 and 3, respectively. The results are presented below in a narrative format, with complimentary tables and figures to provide a visual representation of key findings.
There are some methodological limitations in this scoping review that should be taken into account when interpreting the results. The use of only two search engines may preclude the inclusion of relevant studies, although supplemented by scanning the reference list of eligible studies. An in-depth analysis of the topics explored within each of the seven key ethical principles outlined by the EC's Ethics Guidelines for Trustworthy AI was not conducted. This assessment would lead to a detailed understanding of the publics’ views on ethical challenges of AI.
3 Results
3.1 Study characteristics
Most of the studies were in recent years, with 35 of the 64 studies being published in 2022 and 2023. Journals were listed either on the databases related to Science Citation Index Expanded (n = 25) or the Social Science Citation Index (n = 23), with fewer journals indexed in the Emerging Sources Citation Index (n = 7) and the Arts and Humanities Citation Index (n = 2). Works covered a wide range of fields, including health and medicine (services, policy, medical informatics, medical ethics, public and environmental health); education; business, management and public administration; computer science; information sciences; engineering; robotics; communication; psychology; political science; and transportation. Beyond the general assessment of publics’ attitudes toward, preferences for, and expectations and concerns about AI, the publics’ views on ethical challenges of AI technologies have been studied mainly concerning healthcare and public services and less frequently regarding autonomous vehicles (AV), education, robotic technologies, and smart homes. Most of the studies (n = 47) were funded by research agencies, with 7 papers reporting conflicts of interest.
Quantitative research approaches have assessed the publics’ views on the ethical challenges of AI mainly through online or web-based surveys and experimental platforms, relying on Delphi studies, moral judgment studies, hypothetical vignettes, and choice-based/comparative conjoint surveys. The 25 qualitative studies collected data mainly by semistructured or in-depth interviews. Analysis of publicly available material reporting on AI-use cases, focus groups, a post hoc self-assessment, World Café, participatory research, and practice-based design research were used once or twice. Multi or mixed-methods studies relied on surveys with open-ended and closed questions, frequently combined with focus groups, in-depth interviews, literature reviews, expert opinions, examinations of relevant curriculum examples, tests, and reflexive writings.
The studies were performed (where stated) in a wide variety of countries, including the USA and Australia. More than half of the studies (n = 38) were conducted in a single country. Almost all studies used nonprobability sampling techniques. In quantitative studies, sample sizes varied from 2.3 M internet users in an online experimental platform study [40] to 20 participants in a Delphi study [41]. In qualitative studies, the samples varied from 123 participants in 21 focus groups [42] to six expert interviews [43]. In multi or mixed-methods studies, samples varied from 2036 participants [44] to 21 participants [45].
3.2 Motivations for engaging the publics
The qualitative synthesis of the motivations for researching the publics’ views on the ethical challenges of AI is presented in Table 2 and ordered by the number of studies referencing them in the scoping review. More than half of the studies (n = 37) addressed a single motivation. Innovation (n = 33) and legitimation (n = 29) proved to have the highest relevance as motivations for engaging the publics in the ethical challenges of AI technologies, as articulated in 15 studies. Additional motivations are rooted in education (n = 13), democratization (n = 11), and inspiration (n = 9). Politicization was mentioned in five studies. Although they were not authors’ motivations, few studies were found to have educational [46, 47], democratization [48, 49], and legitimation or inspirations effects [50].
To consider the publics as a valuable source of knowledge that can add real value to innovation processes in both the private and public sectors was the most frequent motivation mentioned in the literature. The call for public participation is rooted in the aspiration to add knowledge outside “formal” ethics at three interrelated levels. First, at a societal level, by asking what kind of AI we want as a society based on novel experiments on public policy preferences [51] and on the study of public perceptions, values, and concerns regarding AI design, development, and implementation in domains such as health care [46, 52,53,54,55], public and social services [49, 56,57,58], AV [59, 60] and journalism [61]. Second, at a practical level, the literature provides insights into the perceived usefulness of AI applications [62, 63] and choices between boosting developers’ voluntary adoption of ethical standards or imposing ethical standards via regulation and oversight [64], as well as suggesting specific guidance for the development and use of AI systems [65,66,67]. Finally, at a theoretical level, literature expands the social-technical perspective [68] and motivated-reasoning theory [69].
Legitimation was also a frequent motivation for engaging the publics. It was underpinned by the need for public trust in and social licences for implementing AI technologies. To ensure the long-term social acceptability of AI as a trustworthy technology [70, 71] was perceived as essential to support its use and to justify its implementation. In one study [72], the authors developed an AI ethics scale to quantify how AI research is accepted in society and which area of ethical, legal, and social issues (ELSI) people are most concerned with. Public trust in and acceptance of AI is claimed by social institutions such as governments, private sectors, industry bodies, and the science community, behaving in a trustworthy manner, respecting public concerns, aligning with societal values, and involving members of the publics in decision-making and public policy [46, 48, 73,74,75], as well as in the responsible design and integration of AI technologies [52, 76, 77].
Education, democratization, and inspiration had a more modest presence as motivations to explore the publics’ views on the ethical challenges of AI. Considering the emergence of new roles and tasks related to AI, the literature has pointed to the public need to ensure the safe use of AI technologies by incorporating ethics and career futures into the education, preparation, and training of both middle school and university students and the current and future health workforce. Improvements in education and guidance for developers and older adults were also noticed. The views of the publics on what needs to be learned or how this learning may be supported or assessed were perceived as crucial. In one study [78], the authors developed strategies that promote learning related to AI through collaborative media production, connecting computational thinking to civic issues and creative expression. In another study [79], real-world scenarios were successfully used as a novel approach to teaching AI ethics. Rhim et al. [76] provided AV moral behavior design guidelines for policymakers, developers, and the publics by reducing the abstractness of AV morality.
Studies motivated by democratization promoted broader public participation in AI, aiming to empower citizens both to express their understandings, apprehensions, and concerns about AI [43, 78, 80, 81] and to address ethical issues in AI as critical consumers, (potential future) developers of AI technologies or would-be participants in codesign processes [40, 43, 45, 78, 82, 83]. Understanding the publics’ views on the ethical challenges of AI is expected to influence companies and policymakers [40]. In one study [45], the authors explored how a digital app might support citizens’ engagement in AI governance by informing them, raising public awareness, measuring publics’ attitudes and supporting collective decision-making.
Inspiration revolved around three main motivations: to raise public interest in AI [46, 48]; to guide future empirical and design studies [79]; and to promote developers’ moral awareness through close collaboration between all those involved in the implementation, use, and design of AI technologies [46, 61, 78, 84, 85].
Politicization was the less frequent motivation reported in the literature for engaging the publics. Recognizing the need for mitigation of social biases [86], public participation to address historically marginalized populations [78, 87], and promoting social equity [79] were the highlighted motives.
3.3 The invited publics
Study participants were mostly the general public and professional groups, followed by developers of AI systems, managers from industry and business, students, academics and researchers, patients/consumers, and policymakers (Fig. 2). The views of local communities and representatives of governance bodies were rarely assessed.
Representative samples of the general public were used in five papers related to studies conducted in the USA [88], Denmark [73], Germany [48], and Austria [49, 63]. The remaining random or purposive samples from the general public comprised mainly adults and current and potential users of AI products and services, with few studies involving informal caregivers or family members of patients (n = 3), older people (n = 2), and university staff (n = 2).
Samples of professional groups included mainly healthcare professionals (19 out of 24 studies). Educators, law enforcement, media practitioners, and GLAM professionals (galleries, libraries, archives, and museums) were invited once.
3.4 Ethical issues
The ethical issues concerning AI technologies perceived as needing the participation of the publics are depicted in Table 3. They were mapped by measuring the number of studies referencing them in the scoping review. Human agency and oversight (n = 55) was the most frequent ethical aspect that was studied in the literature, followed by those centered on privacy and data governance (n = 43). Diversity, nondiscrimination and fairness (n = 39), societal and environmental well-being (n = 39), technical robustness and safety (n = 38), transparency (n = 35), and accountability (n = 31) were less frequently discussed.
The concerns regarding human agency and oversight were the replacement of human beings by AI technologies and deskilling [47, 55, 67, 74, 75, 89, 90]; the loss of autonomy, critical thinking, and innovative capacities [50, 58, 61, 77, 78, 83, 85, 90]; the erosion of human judgment and oversight [41, 70, 91]; and the potential for (over)dependence on technology and “oversimplified” decisions [90] due to the lack of publics’ expertise in judging and controlling AI technologies [68]. Beyond these ethical challenges, the following contributions of AI systems to empower human beings were noted: more fruitful and empathetic social relationships [47, 68, 90]; enhancing human capabilities and quality of life [68, 70, 74, 83, 92]; improving efficiency and productivity at work [50, 53, 62, 65, 83] by reducing errors [77], relieving the burden of professionals and/or increasing accuracy in decisions [47, 55, 90]; and facilitating and expanding access to safe and fair healthcare [42, 53, 54] through earlier diagnosis, increased screening and monitoring, and personalized prescriptions [47, 90]. To foster human rights, allowing people to make informed decisions, the last say was up to the person themselves [42, 43, 46, 55, 64, 67, 73, 76]. People should determine where and when to use automated functions and which functions to use [44, 54], developing “job sharing” arrangements with machines and humans complementing and enriching each other [56, 65, 90]. The literature highlights the need to build AI systems that are under human control [48, 70] whether to confirm or to correct the AI system’s outputs and recommendations [66, 90]. Proper oversight mechanisms were seen as crucial to ensure accuracy and completeness, with divergent views about who should be involved in public participation approaches [86, 87].
Data sharing and/or data misuse were considered the major roadblocks regarding privacy and data governance, with some studies pointing out distrust of participants related to commercial interests in health data [55, 90, 93,94,95] and concerns regarding risks of information getting into the hands of hackers, banks, employers, insurance companies, or governments [66]. As data are the backbone of AI, secure methods of data storage and protection are understood as needing to be provided from the input to the output data. Recognizing that in contemporary societies, people are aware of the consequences of smartphone use resulting in the minimization of privacy concerns [93], some studies have focused on the impacts of data breaches and loss of privacy and confidentiality [43, 45, 46, 60, 62, 80] in relation to health-sensitive personal data [46, 93], potentially affecting more vulnerable populations, such as senior citizens and mentally ill patients [82, 90] as well as those at young ages [50], and when journalistic organizations collect user data to provide personalized news suggestions [61]. The need to find a balance between widening access to data and ensuring confidentiality and respect for privacy [53] was often expressed in three interrelated terms: first, the ability of data subjects to be fully informed about how data will be used and given the option of providing informed consent [46, 58, 78] and controlling personal information about oneself [57]; second, the need for regulation [52, 65, 87], with one study reporting that AI developers complain about the complexity, slowness, and obstacles created by regulation [64]; and last, the testing and certification of AI-enabled products and services [71]. The study by De Graaf et al. [91] discussed the robots’ right to store and process the data they collect, while Jenkins and Draper [42] explored less intrusive ways in which the robot could use information to report back to carers about the patient’s adherence to healthcare.
Studies discussing diversity, nondiscrimination, and fairness have pointed to the development of AI systems that reflect and reify social inequalities [45, 78] through nonrepresentative datasets [55, 58, 96, 97] and algorithmic bias [41, 45, 85, 98] that might benefit some more than others. This could have multiple negative consequences for different groups based on ethnicity, disease, physical disability, age, gender, culture, or socioeconomic status [43, 55, 58, 78, 82, 87], from the dissemination of hate speech [79] to the exacerbation of discrimination, which negatively impacts peace and harmony within society [58]. As there were cross-country differences and issue variations in the publics’ views of discriminatory bias [51, 72, 73], fostering diversity, inclusiveness, and cultural plurality [61] was perceived as crucial to ensure the transferability/effectiveness of AI systems in all social groups [60, 94]. Diversity, nondiscrimination, and fairness were also discussed as a means to help reduce health inequalities [41, 67, 90], to compensate for human preconceptions about certain individuals [66], and to promote equitable distribution of benefits and burdens [57, 71, 80, 93], namely, supporting access by all to the same updated and high-quality AI systems [50]. In one study [83], students provided constructive solutions to build an unbiased AI system, such as using a dataset that includes a diverse dataset engaging people of different ages, genders, ethnicities, and cultures. In another study [86], participants recommended diverse approaches to mitigate algorithmic bias, from open disclosure of limitations to consumer and patient engagement, representation of marginalized groups, incorporation of equity considerations into sampling methods and legal recourse, and identification of a wide range of stakeholders who may be responsible for addressing AI bias: developers, healthcare workers, manufacturers and vendors, policymakers and regulators, AI researchers and consumers.
Impacts on employment and social relationships were considered two major ethical challenges regarding societal and environmental well-being. The literature has discussed tensions between job creation [51] and job displacement [42, 90], efficiency [90], and deskilling [57]. The concerns regarding future social relationships were the loss of empathy, humanity, and/or sensitivity [52, 66, 90, 99]; isolation and fewer social connections [42, 47, 90]; laziness [50, 83]; anxious counterreactions [83, 99]; communication problems [90]; technology dependence [60]; plagiarism and cheating in education [50]; and becoming too emotionally attached to a robot [65]. To overcome social unawareness [56] and lack of acceptance [65] due to financial costs [56, 90], ecological burden [45], fear of the unknown [65, 83] and/or moral issues [44, 59, 100], AI systems need to provide public benefit sharing [55], consider discrepancies between public discourse about AI and the utility of the tools in real-world settings and practices [53], conform to the best standards of sustainability and address climate change and environmental justice [60, 71]. Successful strategies in promoting the acceptability of robots across contexts included an approachable and friendly looking as possible, but not too human-like [49, 65], and working with, rather than in competition, with humans [42].
The publics were invited to participate in the following ethical issues related to technical robustness and safety: usability, reliability, liability, and quality assurance checks of AI tools [44, 45, 55, 62, 99]; validity of big data analytic tools [87]; the degree to which an AI system can perform tasks without errors or mistakes [50, 57, 66, 84, 90, 93]; and needed resources to perform appropriate (cyber)security [62, 101]. Other studies approached the need to consider both material and normative concerns of AI applications [51], namely, assuring that AI systems are developed responsibly with proper consideration of risks [71] and sufficient proof of benefits [96]. One study [64] highlighted that AI developers tend to be reluctant to recognize safety issues, bias, errors, and failures, and when they do so, they do so in a selective manner and in their terms by adopting positive-sounding professional jargon as AI robustness.
Some studies recognized the need for greater transparency that reduces the mystery and opaqueness of AI systems [71, 82, 101] and opens its “black box” [64, 71, 98]. Clear insights about “what AI is/is not” and “how AI technology works” (definition, applications, implications, consequences, risks, limitations, weaknesses, threats, rewards, strengths, opportunities) were considered as needed to debunk the myth about AI as an independent entity [53] and for providing sufficient information and understandable explanations of “what’s happening” to society and individuals [43, 48, 72, 73, 78, 102]. Other studies considered that people, when using AI tools, should be made fully aware that these AI devices are capturing and using their data [46] and how data are collected [58] and used [41, 46, 93]. Other transparency issues reported in the literature included the need for more information about the composition of data training sets [55], how algorithms work [51, 55, 84, 94, 97], how AI makes a decision [57] and the motivations for that decision [98]. Transparency requirements were also addressed as needing the involvement of multiple stakeholders: one study reported that transparency requirements should be seen as a mediator of debate between experts, citizens, communities, and stakeholders [87] and cannot be reduced to a product feature, avoiding experiences where people feel overwhelmed by explanations [98] or “too much information” [66].
Accountability was perceived by the publics as an important ethical issue [48], while developers expressed mixed attitudes, from moral disengagement to a sense of responsibility and moral conflict and uncertainty [85]. The literature has revealed public skepticism regarding accountability mechanisms [93] and criticism about the shift of responsibility away from tech industries that develop and own AI technologies [53, 68], as it opens space for users to assume their own individual responsibility [78]. This was the case in studies that explored accountability concerns regarding the assignment of fault and responsibility for car accidents using self-driving technology [60, 76, 77, 88]. Other studies considered that more attention is needed to scrutinize each application across the AI life cycle [41, 71, 94], to explainability of AI algorithms that provide to the publics the cause of AI outcomes [58], and to regulations that assign clear responsibility concerning litigation and liability [52, 89, 101, 103].
4 Discussion
Within the realm of research studies encompassed in the scoping review, the contemporary impetus for engaging the publics in ethical considerations related to AI predominantly revolves around two key motivations: innovation and legitimation. This might be explained by the current emphasis on responsible innovation, which values the publics’ participation in knowledge and innovation-making [29] within a prioritization of the instrumental role of science for innovation and economic return [33]. Considering the publics as a valuable source of knowledge that should be called upon to contribute to knowledge innovation production is underpinned by the desire for legitimacy, specifically centered around securing the publics’ endorsement of scientific and technological advancements [33, 104]. Approaching the publics’ views on the ethical challenges of AI can also be used as a form of risk prevention to reduce conflict and close vital debates in contention areas [5, 34, 105].
A second aspect that stood out in this finding is a shift in the motivations frequently reported as central for engaging the publics with AI technologies. Previous studies analysing AI national policies and international guidelines addressing AI governance [3,4,5] and a study analysing science communication journals [33] highlighted education, inspiration and democratization as the most prominent motivations. Our scoping review did not yield similar findings, which might signal a departure, in science policy related to public participation, from the past emphasis on education associated with the deficit model of public understanding of science and democratization of the model of public engagement with science [106, 107].
The underlying motives for the publics’ engagement raise the question of the kinds of publics it addresses, i.e., who are the publics that are supposed to be recruited as research participants [32]. Our findings show a prevalence of the general public followed by professional groups and developers of AI systems. The wider presence of the general public indicates not only what Hagendijk and Irwin [32, p. 167] describe as a fashionable tendency in policy circles since the late 1990s, and especially in Europe, focused on engaging 'the public' in scientific and technological change but also the avoidance of the issues of democratic representation [12, 18]. Additionally, the unspecificity of the “public” does not stipulate any particular action [24] that allows for securing legitimacy for and protecting the interests of a wide range of stakeholders [19, 108] while bringing the risk of silencing the voices of the very publics with whom engagement is sought [33]. The focus on approaching the publics’ views on the ethical challenges of AI through the general public also demonstrates how seeking to “lay” people’s opinions may be driven by a desire to promote public trust and acceptance of AI developments, showing how science negotiates challenges and reinstates its authority [109].
While this strategy is based on nonscientific audiences or individuals who are not associated with any scientific discipline or area of inquiry as part of their professional activities, the converse strategy—i.e., involving professional groups and AI developers—is also noticeable in our findings. This suggests that technocratic expert-dominated approaches coexist with a call for more inclusive multistakeholder approaches [3]. This coexistence is reinforced by the normative principles of the “responsible innovation” framework, in particular the prescription that innovation should include the publics as well as traditionally defined stakeholders [3, 110], whose input has become so commonplace that seeking the input of laypeople on emerging technologies is sometimes described as a “standard procedure” [111, p. 153].
In the body of literature included in the scoping review, human agency and oversight emerged as the predominant ethical dimension under investigation. This finding underscores the pervasive significance attributed to human centricity, which is progressively integrated into public discourses concerning AI, innovation initiatives, and market-driven endeavours [15, 112]. In our perspective, the importance given to human-centric AI is emblematic of the “techno-regulatory imaginary” suggested by Rommetveit and van Dijk [35] in their study about privacy engineering applied in the European Union’s General Data Protection Regulation. This term encapsulates the evolving collective vision and conceptualization of the role of technology in regulatory and oversight contexts. At least two aspects stand out in the techno-regulatory imaginary, as they are meant to embed technoscience in societally acceptable ways. First, it reinstates pivotal demarcations between humans and nonhumans while concurrently producing intensified blurring between these two realms. Second, the potential resolutions offered relate to embedding fundamental rights within the structural underpinnings of technological architectures [35].
Following human agency and oversight, the most frequent ethical issue discussed in the studies contained in our scoping review was privacy and data governance. Our findings evidence additional central aspects of the “techno-regulatory imaginary” in the sense that instead of the traditional regulatory sites, modes of protecting privacy and data are increasingly located within more privatized and business-oriented institutions [6, 35] and crafted according to a human-centric view of rights. The focus on secure ways of data storage and protection as in need to be provided from the input to the output data, the testing and certification of AI-enabled products and services, the risks of data breaches, and calls for finding a balance between widening access to data and ensuring confidentiality and respect for privacy, exhibited by many studies in this scoping review, portray an increasing framing of privacy and data protection within technological and standardization sites. This tendency shows how forms of expertise for privacy and data protection are shifting away from traditional regulatory and legal professionals towards privacy engineers and risk assessors in information security and software development. Another salient element to highlight pertains to the distribution of responsibility for privacy and data governance [6, 113] within the realm of AI development through engagement with external stakeholders, including users, governmental bodies, and regulatory authorities. It extends from an emphasis on issues derived from data sharing and data misuse to facilitating individuals to exercise control over their data and privacy preferences and to advocating for regulatory frameworks that do not impede the pace of innovation. This distribution of responsibility shared among the contributions and expectations of different actors is usually convoked when the operationalization of ethics principles conflicts with AI deployment [6]. In this sense, privacy and data governance are reconstituted as a “normative transversal” [113, p. 20], both of which work to stabilize or close controversies, while their operationalization does not modify any underlying operations in AI development.
Diversity, nondiscrimination and fairness, societal and environmental well-being, technical robustness and safety, transparency, and accountability were the ethical issues less frequently discussed in the studies included in this scoping review. In contrast, ethical issues of technical robustness and safety, transparency, and accountability “are those for which technical fixes can be or have already been developed” and “implemented in terms of technical solutions” [12, p. 103]. The recognition of issues related to technical robustness and safety expresses explicit admissions of expert ignorance, error, or lack of control, which opens space for politics of “optimization of algorithms” [114, p. 17] while reinforcing “strategic ignorance” [114, p. 89]. In the words of the sociologist Linsey McGoey, strategic ignorance refers to “any actions which mobilize, manufacture or exploit unknowns in a wider environment to avoid liability for earlier actions” [115, p. 3].
According to the analysis of Jobin et al. [11] of the global landscape of existing ethics guidelines for AI, transparency comprising efforts to increase explainability, interpretability, or other acts of communication and disclosure is the most prevalent principle in the current literature. Transparency gains high relevance in ethics guidelines because this principle has become a pro-ethical condition “enabling or impairing other ethical practices or principles” [Turilli and Floridi 2009, [11], p. 14]. Our findings highlight transparency as a crucial ethical concern for explainability and disclosure. However, as emphasized by Ananny and Crawford [116, p. 973], there are serious limitations to the transparency ideal in making black boxes visible (i.e., disclosing and explaining algorithms), since “being able to see a system is sometimes equated with being able to know how it works and governs it—a pattern that recurs in recent work about transparency and computational systems”. The emphasis on transparency mirrors Aradau and Blanke’s [114] observation that Big Tech firms are creating their version of transparency. They are prompting discussions about their data usage, whether it is for “explaining algorithms” or addressing bias and discrimination openly.
The framing of ethical issues related to accountability, as elucidated by the studies within this scoping review, manifests as a commitment to ethical conduct and the transparent allocation of responsibility and legal obligations in instances where the publics encounters algorithmic deficiencies, glitches, or other imperfections. Within this framework, accountability becomes intricately intertwined with the notion of distributed responsibility, as expounded upon in our examination of how the literature addresses challenges in privacy and data governance. Simultaneously, it converges with our discussion on optimizing algorithms concerning ethical concerns on technical robustness and safety by which AI systems are portrayed as fallible yet eternally evolving towards optimization. As astutely observed by Aradau and Blanke [114, p. 171], “forms of accountability through error enact algorithmic systems as fallible but ultimately correctable and therefore always desirable. Errors become temporary malfunctions, while the future of algorithms is that of indefinite optimization”.
5 Conclusion
This scoping review of how publics' views on ethical challenges of AI are framed, articulated, and concretely operationalized in the research sector shows that ethical issues and publics formation are closely entangled with symbolic and social orders, including political and economic agendas and visions. While Steinhoff [6] highlights the subordinated nature of AI ethics within an innovation network, drawing on insights from diverse sources beyond Big Tech, we assert that this network is dynamically evolving towards greater hybridity and boundary fusion. In this regard, we extend Steinhoff's argument by emphasizing the imperative for a more nuanced understanding of how this network operates within diverse contexts. Specifically, within the research sector, it operates through a convergence of boundaries, engaging human and nonhuman entities and various disciplines and stakeholders. Concurrently, the advocacy for diversity and inclusivity, along with the acknowledgement of errors and flaws, serves to bolster technical expertise and reaffirm the establishment of order and legitimacy in alignment with the institutional norms underpinning responsible research practices.
Our analysis underscores the growing importance of involving the publics in AI knowledge creation and innovation, both to secure public endorsement and as a tool for risk prevention and conflict mitigation. We observe two distinct approaches: one engaging nonscientific audiences and the other involving professional groups and AI developers, emphasizing the need for inclusivity while safeguarding expert knowledge. Human-centred approaches are gaining prominence, emphasizing the distinction and blending of human and nonhuman entities and embedding fundamental rights in technological systems. Privacy and data governance emerge as the second most prevalent ethical concern, shifting expertise away from traditional regulatory experts to privacy engineers and risk assessors. The distribution of responsibility for privacy and data governance is a recurring theme, especially in cases of ethical conflicts with AI deployment. However, there is a notable imbalance in attention, with less focus on diversity, nondiscrimination, fairness, societal, and environmental well-being, compared to human-centric AI, privacy, and data governance being managed through technical fixes. Last, acknowledging technical robustness and safety, transparency, and accountability as foundational ethics principles reveals an openness to expert limitations, allowing room for the politics of algorithm optimization, framing AI systems as correctable and perpetually evolving.
Data availability
This manuscript has data included as electronic supplementary material. The dataset constructed by the authors, resulting from a search of publications on PubMed® and Web of Science™, analysed in the current study, is not publicly available. But it can be available from the corresponding author on reasonable request.
Notes
In this article, we will employ the term "publics" rather than the singular "public" to delineate our viewpoint concerning public participation in AI. Our option is meant to acknowledge that there are no uniform, monolithic viewpoints or interests. From our perspective, the term "publics" allows for a more nuanced understanding of the various groups, communities, and individuals who may have different attitudes, beliefs, and concerns regarding AI. This choice may differ from the terminology employed in the referenced literature.
The following examples are particularly illustrative of the multiplicity of organizations emphasizing the need for public participation in AI. The OECD Recommendations of the Council on AI specifically emphasizes the importance of empowering stakeholders considering essential their engagement to adoption of trustworthy [7, p. 6]. The UNESCO Recommendation on the Ethics of AI emphasizes that public awareness and understanding of AI technologies should be promoted (recommendation 44) and it encourages governments and other stakeholders to involve the publics in AI decision-making processes (recommendation 47) [8, p. 23]. The European Union (EU) White Paper on AI [9, p. 259] outlines the EU’s approach to AI, including the need for public consultation and engagement. The Ethics Guidelines for Trustworthy AI [10, pp. 19, 239], developed by the High-Level Expert Group on AI (HLEG) appointed by the EC, emphasize the importance of public participation and consultation in the design, development, and deployment of AI systems.
“Responsible Innovation” (RI) and “Responsible Research and Innovation” (RRI) have emerged in parallel and are often used interchangeably, but they are not the same thing [27, 28]. RRI is a policy-driven discourse that emerged from the EC in the early 2010s, while RI emerged largely from academic roots. For this paper, we will not consider the distinctive features of each discourse, but instead focus on the common features they share.
References
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., Floridi, L.: Artificial intelligence and the ‘good society’: the US, EU, and UK approach. Sci. Eng. Ethics 24, 505–528 (2017). https://doi.org/10.1007/s11948-017-9901-7
Cussins, J.N.: Decision points in AI governance. CLTC white paper series. Center for Long-term Cybersecurity. https://cltc.berkeley.edu/publication/decision-points-in-ai-governance/ (2020). Accessed 8 July 2023
Ulnicane, I., Okaibedi Eke, D., Knight, W., Ogoh, G., Stahl, B.: Good governance as a response to discontents? Déjà vu, or lessons for AI from other emerging technologies. Interdiscip. Sci. Rev. 46(1–2), 71–93 (2021). https://doi.org/10.1080/03080188.2020.1840220
Ulnicane, I., Knight, W., Leach, T., Stahl, B., Wanjiku, W.: Framing governance for a contested emerging technology: insights from AI policy. Policy Soc. 40(2), 158–177 (2021). https://doi.org/10.1080/14494035.2020.1855800
Wilson, C.: Public engagement and AI: a values analysis of national strategies. Gov. Inf. Q. 39(1), 101652 (2022). https://doi.org/10.1016/j.giq.2021.101652
Steinhoff, J.: AI ethics as subordinated innovation network. AI Soc. (2023). https://doi.org/10.1007/s00146-023-01658-5
Organization for Economic Co-operation and Development. Recommendation of the Council on Artificial Intelligence. https://legalinstruments.oecd.org/en/instruments/oecd-legal-0449 (2019). Accessed 8 July 2023
United Nations Educational, Scientific and Cultural Organization. Recommendation on the Ethics of Artificial Intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000381137 (2021). Accessed 28 June 2023
European Commission. On artificial intelligence – a European approach to excellence and trust. White paper. COM(2020) 65 final. https://commission.europa.eu/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en (2020). Accessed 28 June 2023
European Commission. The ethics guidelines for trustworthy AI. Directorate-General for Communications Networks, Content and Technology, EC Publications Office. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai (2019). Accessed 10 July 2023
Jobin, A., Ienca, M., Vayena, E.: The global landscape of AI ethics guidelines. Nat. Mach. Intell. 1, 389–399 (2019). https://doi.org/10.1038/s42256-019-0088-2
Hagendorff, T.: The ethics of AI ethics: an evaluation of guidelines. Minds Mach. 30, 99–120 (2020). https://doi.org/10.1007/s11023-020-09517-8
Su, A.: The promise and perils of international human rights law for AI governance. Law Technol. Hum. 4(2), 166–182 (2022). https://doi.org/10.5204/lthj.2332
Ulnicane, I.: Emerging technology for economic competitiveness or societal challenges? Framing purpose in artificial intelligence policy. GPPG. 2, 326–345 (2022). https://doi.org/10.1007/s43508-022-00049-8
Sigfrids, A., Leikas, J., Salo-Pöntinen, H., Koskimies, E.: Human-centricity in AI governance: a systemic approach. Front Artif. Intell. 6, 976887 (2023). https://doi.org/10.3389/frai.2023.976887
Benkler, Y.: Don’t let industry write the rules for AI. Nature 569(7755), 161 (2019). https://doi.org/10.1038/d41586-019-01413-1
Phan, T., Goldenfein, J., Mann, M., Kuch, D.: Economies of virtue: the circulation of ‘ethics’ in Big Tech. Sci. Cult. 31(1), 121–135 (2022). https://doi.org/10.1080/09505431.2021.1990875
Ochigame, R.: The invention of “ethical AI”: how big tech manipulates academia to avoid regulation. Intercept. https://theintercept.com/2019/12/20/mit-ethical-ai-artificial-intelligence/ (2019). Accessed 10 July 2023
Ferretti, T.: An institutionalist approach to ai ethics: justifying the priority of government regulation over self-regulation. MOPP 9(2), 239–265 (2022). https://doi.org/10.1515/mopp-2020-0056
van Maanen, G.: AI ethics, ethics washing, and the need to politicize data ethics. DISO 1(9), 1–23 (2022). https://doi.org/10.1007/s44206-022-00013-3
Gerdes, A.: The tech industry hijacking of the AI ethics research agenda and why we should reclaim it. Discov. Artif. Intell. 2(25), 1–8 (2022). https://doi.org/10.1007/s44163-022-00043-3
Amariles, D.R., Baquero, P.M.: Promises and limits of law for a human-centric artificial intelligence. Comput. Law Secur. Rev. 48(105795), 1–10 (2023). https://doi.org/10.1016/j.clsr.2023.105795
Mittelstadt, B.: Principles alone cannot guarantee ethical AI. Nat. Mach. Intell. 1(11), 501–507 (2019). https://doi.org/10.1038/s42256-019-0114-4
Munn, L.: The uselessness of AI ethics. AI Ethics 3, 869–877 (2022). https://doi.org/10.1007/s43681-022-00209-w
Heilinger, J.C.: The ethics of AI ethics. A constructive critique. Philos. Technol. 35(61), 1–20 (2022). https://doi.org/10.1007/s13347-022-00557-9
Roche, C., Wall, P.J., Lewis, D.: Ethics and diversity in artificial intelligence policies, strategies and initiatives. AI Ethics (2022). https://doi.org/10.1007/s43681-022-00218-9
Diercks, G., Larsen, H., Steward, F.: Transformative innovation policy: addressing variety in an emerging policy paradigm. Res. Policy 48(4), 880–894 (2019). https://doi.org/10.1016/j.respol.2018.10.028
Owen, R., Pansera, M.: Responsible innovation and responsible research and innovation. In: Dagmar, S., Kuhlmann, S., Stamm, J., Canzler, W. (eds.) Handbook on Science and Public Policy, pp. 26–48. Edward Elgar, Cheltenham (2019)
Macq, H., Tancoigne, E., Strasser, B.J.: From deliberation to production: public participation in science and technology policies of the European Commission (1998–2019). Minerva 58(4), 489–512 (2020). https://doi.org/10.1007/s11024-020-09405-6
Cath, C.: Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philos. Trans. Royal Soc. A. 376, 20180080 (2018). https://doi.org/10.1098/rsta.2018.0080
Wilson, C.: The socialization of civic participation norms in government?: Assessing the effect of the Open Government Partnership on countries’e-participation. Gov. Inf. Q. 37(4), 101476 (2020). https://doi.org/10.1016/j.giq.2020.101476
Hagendijk, R., Irwin, A.: Public deliberation and governance: engaging with science and technology in contemporary Europe. Minerva 44(2), 167–184 (2006). https://doi.org/10.1007/s11024-006-0012-x
Weingart, P., Joubert, M., Connoway, K.: Public engagement with science - origins, motives and impact in academic literature and science policy. PLoS One 16(7), e0254201 (2021). https://doi.org/10.1371/journal.pone.0254201
Wynne, B.: Public participation in science and technology: performing and obscuring a political–conceptual category mistake. East Asian Sci. 1(1), 99–110 (2007). https://doi.org/10.1215/s12280-007-9004-7
Rommetveit, K., Van Dijk, N.: Privacy engineering and the techno-regulatory imaginary. Soc. Stud. Sci. 52(6), 853–877 (2022). https://doi.org/10.1177/03063127221119424
Levac, D., Colquhoun, H., O’Brien, K.: Scoping studies: advancing the methodology. Implement. Sci. 5(69), 1–9 (2010). https://doi.org/10.1186/1748-5908-5-69
Arksey, H., O’Malley, L.: Scoping studies: towards a methodological framework. Int. J. Soc. Res. Methodol. 8(1), 19–32 (2005). https://doi.org/10.1080/1364557032000119616
Stemler, S.: An overview of content analysis. Pract. Asses. Res. Eval. 7(17), 1–9 (2001). https://doi.org/10.7275/z6fm-2e34
European Commission. European Commission's ethics guidelines for trustworthy AI. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai (2021). Accessed 8 July 2023
Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., et al.: The moral machine experiment. Nature 563(7729), 59–64 (2018). https://doi.org/10.1038/s41586-018-0637-6
Liyanage, H., Liaw, S.T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A.L., de Lusignan, S.: Artificial intelligence in primary health care: perceptions, issues, and challenges. Yearb. Med. Inform. 28(1), 41–46 (2019). https://doi.org/10.1055/s-0039-1677901
Jenkins, S., Draper, H.: Care, monitoring, and companionship: views on care robots from older people and their carers. Int. J. Soc. Robot. 7(5), 673–683 (2015). https://doi.org/10.1007/s12369-015-0322-y
Tzouganatou, A.: Openness and privacy in born-digital archives: reflecting the role of AI development. AI Soc. 37(3), 991–999 (2022). https://doi.org/10.1007/s00146-021-01361-3
Liljamo, T., Liimatainen, H., Pollanen, M.: Attitudes and concerns on automated vehicles. Transp. Res. Part F Traffic Psychol. Behav. 59, 24–44 (2018). https://doi.org/10.1016/j.trf.2018.08.010
Couture, V., Roy, M.C., Dez, E., Laperle, S., Belisle-Pipon, J.C.: Ethical implications of artificial intelligence in population health and the public’s role in its governance: perspectives from a citizen and expert panel. J. Med. Internet Res. 25, e44357 (2023). https://doi.org/10.2196/44357
McCradden, M.D., Sarker, T., Paprica, P.A.: Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research. BMJ Open 10(10), e039798 (2020). https://doi.org/10.1136/bmjopen-2020-039798
Blease, C., Kharko, A., Annoni, M., Gaab, J., Locher, C.: Machine learning in clinical psychology and psychotherapy education: a mixed methods pilot survey of postgraduate students at a Swiss University. Front. Public Health 9(623088), 1–8 (2021). https://doi.org/10.3389/fpubh.2021.623088
Kieslich, K., Keller, B., Starke, C.: Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence. Big Data Soc. 9(1), 1–15 (2022). https://doi.org/10.1177/20539517221092956
Willems, J., Schmidthuber, L., Vogel, D., Ebinger, F., Vanderelst, D.: Ethics of robotized public services: the role of robot design and its actions. Gov. Inf. Q. 39(101683), 1–11 (2022). https://doi.org/10.1016/J.Giq.2022.101683
Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R.H., Agyemang, B.: What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learn Environ. 10(15), 1–24 (2023). https://doi.org/10.1186/S40561-023-00237-X
Ehret, S.: Public preferences for governing AI technology: comparative evidence. J. Eur. Public Policy 29(11), 1779–1798 (2022). https://doi.org/10.1080/13501763.2022.2094988
Esmaeilzadeh, P.: Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives. BMC Med. Inform. Decis. Mak. 20(170), 1–19 (2020). https://doi.org/10.1186/s12911-020-01191-1
Laïï, M.C., Brian, M., Mamzer, M.F.: Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J. Transl. Med. 18(14), 1–13 (2020). https://doi.org/10.1186/S12967-019-02204-Y
Valles-Peris, N., Barat-Auleda, O., Domenech, M.: Robots in healthcare? What patients say. Int. J. Environ. Res. Public Health 18(9933), 1–18 (2021). https://doi.org/10.3390/ijerph18189933
Hallowell, N., Badger, S., Sauerbrei, A., Nellaker, C., Kerasidou, A.: “I don’t think people are ready to trust these algorithms at face value”: trust and the use of machine learning algorithms in the diagnosis of rare disease. BMC Med. Ethics 23(112), 1–14 (2022). https://doi.org/10.1186/s12910-022-00842-4
Criado, J.I., de Zarate-Alcarazo, L.O.: Technological frames, CIOs, and artificial intelligence in public administration: a socio-cognitive exploratory study in spanish local governments. Gov. Inf. Q. 39(3), 1–13 (2022). https://doi.org/10.1016/J.Giq.2022.101688
Isbanner, S., O’Shaughnessy, P.: The adoption of artificial intelligence in health care and social services in Australia: findings from a methodologically innovative national survey of values and attitudes (the AVA-AI Study). J. Med. Internet Res. 24(8), e37611 (2022). https://doi.org/10.2196/37611
Kuberkar, S., Singhal, T.K., Singh, S.: Fate of AI for smart city services in India: a qualitative study. Int. J. Electron. Gov. Res. 18(2), 1–21 (2022). https://doi.org/10.4018/Ijegr.298216
Kallioinen, N., Pershina, M., Zeiser, J., Nezami, F., Pipa, G., Stephan, A., Konig, P.: Moral judgements on the actions of self-driving cars and human drivers in dilemma situations from different perspectives. Front. Psychol. 10(2415), 1–15 (2019). https://doi.org/10.3389/fpsyg.2019.02415
Vrščaj, D., Nyholm, S., Verbong, G.P.J.: Is tomorrow’s car appealing today? Ethical issues and user attitudes beyond automation. AI Soc. 35(4), 1033–1046 (2020). https://doi.org/10.1007/s00146-020-00941-z
Bastian, M., Helberger, N., Makhortykh, M.: Safeguarding the journalistic DNA: attitudes towards the role of professional values in algorithmic news recommender designs. Digit. Journal. 9(6), 835–863 (2021). https://doi.org/10.1080/21670811.2021.1912622
Kaur, K., Rampersad, G.: Trust in driverless cars: investigating key factors influencing the adoption of driverless cars. J. Eng. Technol. Manag. 48, 87–96 (2018). https://doi.org/10.1016/j.jengtecman.2018.04.006
Willems, J., Schmid, M.J., Vanderelst, D., Vogel, D., Ebinger, F.: AI-driven public services and the privacy paradox: do citizens really care about their privacy? Public Manag. Rev. (2022). https://doi.org/10.1080/14719037.2022.2063934
Duke, S.A.: Deny, dismiss and downplay: developers’ attitudes towards risk and their role in risk creation in the field of healthcare-AI. Ethics Inf. Technol. 24(1), 1–15 (2022). https://doi.org/10.1007/s10676-022-09627-0
Cresswell, K., Cunningham-Burley, S., Sheikh, A.: Health care robotics: qualitative exploration of key challenges and future directions. J. Med. Internet Res. 20(7), e10410 (2018). https://doi.org/10.2196/10410
Amann, J., Vayena, E., Ormond, K.E., Frey, D., Madai, V.I., Blasimme, A.: Expectations and attitudes towards medical artificial intelligence: a qualitative study in the field of stroke. PLoS One 18(1), e0279088 (2023). https://doi.org/10.1371/journal.pone.0279088
Aquino, Y.S.J., Rogers, W.A., Braunack-Mayer, A., Frazer, H., Win, K.T., Houssami, N., et al.: Utopia versus dystopia: professional perspectives on the impact of healthcare artificial intelligence on clinical roles and skills. Int. J. Med. Inform. 169(104903), 1–10 (2023). https://doi.org/10.1016/j.ijmedinf.2022.104903
Sartori, L., Bocca, G.: Minding the gap(s): public perceptions of AI and socio-technical imaginaries. AI Soc. 38(2), 443–458 (2022). https://doi.org/10.1007/s00146-022-01422-1
Chen, Y.-N.K., Wen, C.-H.R.: Impacts of attitudes toward government and corporations on public trust in artificial intelligence. Commun. Stud. 72(1), 115–131 (2021). https://doi.org/10.1080/10510974.2020.1807380
Aitken, M., Ng, M., Horsfall, D., Coopamootoo, K.P.L., van Moorsel, A., Elliott, K.: In pursuit of socially ly-minded data-intensive innovation in banking: a focus group study of public expectations of digital innovation in banking. Technol. Soc. 66(101666), 1–10 (2021). https://doi.org/10.1016/j.techsoc.2021.101666
Choung, H., David, P., Ross, A.: Trust and ethics in AI. AI Soc. 38(2), 733–745 (2023). https://doi.org/10.1007/s00146-022-01473-4
Hartwig, T., Ikkatai, Y., Takanashi, N., Yokoyama, H.M.: Artificial intelligence ELSI score for science and technology: a comparison between Japan and the US. AI Soc. 38(4), 1609–1626 (2023). https://doi.org/10.1007/s00146-021-01323-9
Ploug, T., Sundby, A., Moeslund, T.B., Holm, S.: Population preferences for performance and explainability of artificial intelligence in health care: choice-based conjoint survey. J. Med. Internet Res. 23(12), e26611 (2021). https://doi.org/10.2196/26611
Zheng, B., Wu, M.N., Zhu, S.J., Zhou, H.X., Hao, X.L., Fei, F.Q., et al.: Attitudes of medical workers in China toward artificial intelligence in ophthalmology: a comparative survey. BMC Health Serv. Res. 21(1067), 1–13 (2021). https://doi.org/10.1186/S12913-021-07044-5
Ma, J., Tojib, D., Tsarenko, Y.: Sex robots: are we ready for them? An exploration of the psychological mechanisms underlying people’s receptiveness of sex robots. J. Bus. Ethics 178(4), 1091–1107 (2022). https://doi.org/10.1007/s10551-022-05059-4
Rhim, J., Lee, G.B., Lee, J.H.: Human moral reasoning types in autonomous vehicle moral dilemma: a cross-cultural comparison of Korea and Canada. Comput. Hum. Behav. 102, 39–56 (2020). https://doi.org/10.1016/j.chb.2019.08.010
Dempsey, R.P., Brunet, J.R., Dubljevic, V.: Exploring and understanding law enforcement’s relationship with technology: a qualitative interview study of police officers in North Carolina. Appl. Sci-Basel 13(6), 1–17 (2023). https://doi.org/10.3390/App13063887
Lee, C.H., Gobir, N., Gurn, A., Soep, E.: In the black mirror: youth investigations into artificial intelligence. ACM Trans. Comput. Educ. 22(3), 1–25 (2022). https://doi.org/10.1145/3484495
Kong, S.C., Cheung, W.M.Y., Zhang, G.: Evaluating an artificial intelligence literacy programme for developing university students? Conceptual understanding, literacy, empowerment and ethical awareness. Educ. Technol. Soc. 26(1), 16–30 (2023). https://doi.org/10.30191/Ets.202301_26(1).0002
Street, J., Barrie, H., Eliott, J., Carolan, L., McCorry, F., Cebulla, A., et al.: Older adults’ perspectives of smart technologies to support aging at home: insights from five world cafe forums. Int. J. Environ. Res. Public Health 19(7817), 1–22 (2022). https://doi.org/10.3390/Ijerph19137817
Ikkatai, Y., Hartwig, T., Takanashi, N., Yokoyama, H.M.: Octagon measurement: public attitudes toward AI ethics. Int J Hum-Comput Int. 38(17), 1589–1606 (2022). https://doi.org/10.1080/10447318.2021.2009669
Wang, S., Bolling, K., Mao, W., Reichstadt, J., Jeste, D., Kim, H.C., Nebeker, C.: Technology to support aging in place: older adults’ perspectives. Healthcare (Basel) 7(60), 1–18 (2019). https://doi.org/10.3390/healthcare7020060
Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y.H., Breazeal, C.: Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: an exploratory study. Int. J. Artif. Intell. Educ. 33, 290–324 (2022). https://doi.org/10.1007/s40593-022-00293-3
Henriksen, A., Blond, L.: Executive-centered AI? Designing predictive systems for the public sector. Soc. Stud. Sci. (2023). https://doi.org/10.1177/03063127231163756
Nichol, A.A., Halley, M.C., Federico, C.A., Cho, M.K., Sankar, P.L.: Not in my AI: moral engagement and disengagement in health care AI development. Pac. Symp. Biocomput. 28, 496–506 (2023)
Aquino, Y.S.J., Carter, S.M., Houssami, N., Braunack-Mayer, A., Win, K.T., Degeling, C., et al.: Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives. J. Med. Ethics (2023). https://doi.org/10.1136/jme-2022-108850
Nichol, A.A., Bendavid, E., Mutenherwa, F., Patel, C., Cho, M.K.: Diverse experts’ perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study. BMJ Open 11(7), e052287 (2021). https://doi.org/10.1136/bmjopen-2021-052287
Awad, E., Levine, S., Kleiman-Weiner, M., Dsouza, S., Tenenbaum, J.B., Shariff, A., et al.: Drivers are blamed more than their automated cars when both make mistakes. Nat. Hum. Behav. 4(2), 134–143 (2020). https://doi.org/10.1038/s41562-019-0762-8
Blease, C., Kaptchuk, T.J., Bernstein, M.H., Mandl, K.D., Halamka, J.D., DesRoches, C.M.: Artificial intelligence and the future of primary care: exploratory qualitative study of UK general practitioners’ views. J. Med. Internet Res. 21(3), e12802 (2019). https://doi.org/10.2196/12802
Blease, C., Locher, C., Leon-Carlyle, M., Doraiswamy, M.: Artificial intelligence and the future of psychiatry: qualitative findings from a global physician survey. Digit. Health 6, 1–18 (2020). https://doi.org/10.1177/2055207620968355
De Graaf, M.M.A., Hindriks, F.A., Hindriks, K.V.: Who wants to grant robots rights? Front Robot AI 8, 781985 (2022). https://doi.org/10.3389/frobt.2021.781985
Guerouaou, N., Vaiva, G., Aucouturier, J.-J.: The shallow of your smile: the ethics of expressive vocal deep-fakes. Philos. Trans. R Soc. B Biol. Sci. 377(1841), 1–11 (2022). https://doi.org/10.1098/rstb.2021.0083
McCradden, M.D., Baba, A., Saha, A., Ahmad, S., Boparai, K., Fadaiefard, P., Cusimano, M.D.: Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study. CMAJ Open 8(1), E90–E95 (2020). https://doi.org/10.9778/cmajo.20190151
Rogers, W.A., Draper, H., Carter, S.M.: Evaluation of artificial intelligence clinical applications: Detailed case analyses show value of healthcare ethics approach in identifying patient care issues. Bioethics 36(4), 624–633 (2021). https://doi.org/10.1111/bioe.12885
Tosoni, S., Voruganti, I., Lajkosz, K., Habal, F., Murphy, P., Wong, R.K.S., et al.: The use of personal health information outside the circle of care: consent preferences of patients from an academic health care institution. BMC Med. Ethics 22(29), 1–14 (2021). https://doi.org/10.1186/S12910-021-00598-3
Allahabadi, H., Amann, J., Balot, I., Beretta, A., Binkley, C., Bozenhard, J., et al.: Assessing trustworthy AI in times of COVID-19: deep learning for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients. IEEE Trans. Technol. Soc. 3(4), 272–289 (2022). https://doi.org/10.1109/TTS.2022.3195114
Gray, K., Slavotinek, J., Dimaguila, G.L., Choo, D.: Artificial intelligence education for the health workforce: expert survey of approaches and needs. JMIR Med. Educ. 8(2), e35223 (2022). https://doi.org/10.2196/35223
Alfrink, K., Keller, I., Doorn, N., Kortuem, G.: Tensions in transparent urban AI: designing a smart electric vehicle charge point. AI Soc. 38(3), 1049–1065 (2022). https://doi.org/10.1007/s00146-022-01436-9
Bourla, A., Ferreri, F., Ogorzelec, L., Peretti, C.S., Guinchard, C., Mouchabac, S.: Psychiatrists’ attitudes toward disruptive new technologies: mixed-methods study. JMIR Ment. Health 5(4), e10240 (2018). https://doi.org/10.2196/10240
Kopecky, R., Kosova, M.J., Novotny, D.D., Flegr, J., Cerny, D.: How virtue signalling makes us better: moral preferences with respect to autonomous vehicle type choices. AI Soc. 38, 937–946 (2022). https://doi.org/10.1007/s00146-022-01461-8
Lam, K., Abramoff, M.D., Balibrea, J.M., Bishop, S.M., Brady, R.R., Callcut, R.A., et al.: A Delphi consensus statement for digital surgery. NPJ Digit. Med. 5(100), 1–9 (2022). https://doi.org/10.1038/s41746-022-00641-6
Karaca, O., Çalışkan, S.A., Demir, K.: Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study. BMC Med. Educ. 21(112), 1–9 (2021). https://doi.org/10.1186/s12909-021-02546-6
Papyshev, G., Yarime, M.: The limitation of ethics-based approaches to regulating artificial intelligence: regulatory gifting in the context of Russia. AI Soc. (2022). https://doi.org/10.1007/s00146-022-01611-y
Balaram, B., Greenham, T., Leonard, J.: Artificial intelligence: real public engagement. RSA, London. https://www.thersa.org/globalassets/pdfs/reports/rsa_artificial-intelligence---real-public-engagement.pdf (2018). Accessed 28 June 2023
Hagendorff, T.: A virtue-based framework to support putting AI ethics into practice. Philos Technol. 35(55), 1–24 (2022). https://doi.org/10.1007/s13347-022-00553-z
Felt, U., Wynne, B., Callon, M., Gonçalves, M. E., Jasanoff, S., Jepsen, M., et al.: Taking european knowledge society seriously. Eur Comm, Brussels, 1–89 (2007). https://op.europa.eu/en/publication-detail/-/publication/5d0e77c7-2948-4ef5-aec7-bd18efe3c442/language-en
Michael, M.: Publics performing publics: of PiGs, PiPs and politics. Public Underst. Sci. 18(5), 617–631 (2009). https://doi.org/10.1177/09636625080985
Hu, L.: Tech ethics: speaking ethics to power, or power speaking ethics? J. Soc. Comput. 2(3), 238–248 (2021). https://doi.org/10.23919/JSC.2021.0033
Strasser, B., Baudry, J., Mahr, D., Sanchez, G., Tancoigne, E.: “Citizen science”? Rethinking science and public participation. Sci. Technol. Stud. 32(2), 52–76 (2019). https://doi.org/10.23987/sts.60425
De Saille, S.: Innovating innovation policy: the emergence of ‘Responsible Research and Innovation.’ J. Responsible Innov. 2(2), 152–168 (2015). https://doi.org/10.1080/23299460.2015.1045280
Schwarz-Plaschg, C.: Nanotechnology is like… The rhetorical roles of analogies in public engagement. Public Underst. Sci. 27(2), 153–167 (2018). https://doi.org/10.1177/0963662516655686
Taylor, R.R., O’Dell, B., Murphy, J.W.: Human-centric AI: philosophical and community-centric considerations. AI Soc. (2023). https://doi.org/10.1007/s00146-023-01694-1
van Dijk, N., Tanas, A., Rommetveit, K., Raab, C.: Right engineering? The redesign of privacy and personal data protection. Int. Rev. Law Comput. Technol. 32(2–3), 230–256 (2018). https://doi.org/10.1080/13600869.2018.1457002
Aradau, C., Blanke, T.: Algorithmic reason. The new government of self and others. Oxford University Press, Oxford (2022)
McGoey, L.: The unknowers. How strategic ignorance rules the word. Zed, London (2019)
Ananny, M., Crawford, K.: Seeing without knowing: limitations of the transparency ideal and its application to algorithmic accountability. New Media Soc. 20(3), 973–989 (2018). https://doi.org/10.1177/1461444816676645
Acknowledgements
The authors would like to express their gratitude to Rafaela Granja (CECS, University of Minho) for her insightful support in an early stage of preparation of this manuscript, and to the AIDA research netwrok for the inspiring debates.
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Open access funding provided by FCT|FCCN (b-on). Helena Machado and Susana Silva did not receive funding to assist in the preparation of this work. Laura Neiva received funding from FCT—Fundação para a Ciência e a Tecnologia, I.P., under a PhD Research Studentships (ref.2020.04764.BD), and under the project UIDB/00736/2020 (base funding) and UIDP/00736/2020 (programmatic funding).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by HM, SS, and LN. The first draft of the manuscript was written by HM and SS. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Machado, H., Silva, S. & Neiva, L. Publics’ views on ethical challenges of artificial intelligence: a scoping review. AI Ethics (2023). https://doi.org/10.1007/s43681-023-00387-1
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DOI: https://doi.org/10.1007/s43681-023-00387-1