# IT Professionals Trust in Artificial Intelligence vs. Human Experts for Achieving Sustainable Development Goals

Dejan Glavas, Gilles Grolleau, Naoufel Mzoughi

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Dejan Glavas, Gilles Grolleau, Naoufel Mzoughi. IT Professionals Trust in Artificial Intelligence vs. Human Experts for Achieving Sustainable Development Goals. Sustainable Futures, In press, 10, pp.101153. 10.1016/j.sftr.2025.101153. hal-05209259

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# IT Professionals Trust in Artificial Intelligence vs. Human Experts for Achieving Sustainable Development Goals

**Abstract:** This study investigates trust of information technology (IT) professionals in artificial intelligence (AI), human experts, and their combination for achieving Sustainable Development Goals (SDGs). Through a survey of IT project managers and frequent AI users across France, the United Kingdom, and Belgium, our findings reveal that respondents place significantly higher trust in human experts and combinations of AI and human expertise, compared to AI. Notably, individuals who most frequently use AI technology show stronger preference for human-AI collaborative approaches, suggesting that familiarity with AI leads to better understanding of its complementary role with human expertise. The study demonstrates that successful AI implementation for achieving SDGs requires careful integration with human oversight rather than standalone deployment.

**Keywords:** Artificial intelligence; sustainable development; SDG.

**JEL codes:** O33; Q01.

## 1. Introduction

Advancing Sustainable Development Goals (SDGs) is key for the betterment of the society (UN General Assembly, 2015). In parallel, the integration of artificial intelligence (AI) into various domains is increasing, making it crucial to examine whether this innovative tool can serve this important goal. Interestingly, Vinuesa et al. (2020) concluded that AI may act as an enabler on 134 targets (79%) but also stress that 59 targets (35%, also across all SDGs) may experience a negative impact from the development of AI. Existing research also suggests that
the application of AI in industrial fields can promote the development of a green economy (Yang & Liu, 2024). Aside from the literature, several use cases demonstrate that AI can positively impact various SDGs (e.g., Nti et al., 2022; Saba & Pretorius, 2024), such as matching electricity supply and demand, improving climate models, identifying areas of high plastic pollution, and fine-tuning school curricula to individual students. While positive use cases are often emphasized, we advocate for a more cautious analysis by highlighting the risks and potential failures of AI in the context of the SDGs. These studies emphasize the significant potential of AI but often fail to adequately address some human-related obstacles that can hinder the realization of these possibilities.

As a step in this direction, little is known on whether people trust AI in the context of SDGs' achievement.<fnref n="1" /> We define trust very simply as the willingness of an agent to rely on AI or individuals, based on perceived competence, integrity, and reliability in a given task or context. Given the potential of AI (United Nations, 2021), understanding these trust dynamics will help build effective policymaking and implementation of AI-driven solutions to reach SDGs. We focus on IT experts and AI frequent users, whose expertise and implementation roles uniquely position them to understand and influence how AI is deployed toward achieving the SDGs. On one hand, although the survey instrument was initially designed to examine other research topics, we took the opportunity to incorporate additional questions (see Section 3) that could provide valuable insights into trust in AI in the context of achieving sustainable development goals. On the other hand, targeting this population makes sense for several reasons: first, IT professionals have a practical understanding of AI projects within

<footnote n="1">¹ This should not occult that other studies—not related to SDGs and thus not within the scope of this paper—examined trust in AI and suggest that people express various levels of trust in AI applications (see, e.g., Dujmović, 2017; Tamò-Larrieux et al., 2024; see also the systematic literature review of Yang & Wibowo, 2022). This literature also reveals that different AI systems might affect differently various forms of trust, e.g., cognitive or emotional trust (see, e.g., Glikson & Woolley, 2020).</footnote>
their organizations. Unlike other potential samples, this population possess hands-on
experience with AI implementation, enabling them to provide informed assessments of AI
capabilities and limitations in practical settings. Their perspectives offer practical insights into
the alignment—or disconnect—between AI’s potential and its real-world application in
sustainable development efforts. Second, their direct involvement in technology deployment
gives them unique knowledge of the challenges and opportunities of human-AI collaboration.
Third, their technical background ensures they can accurately evaluate AI’s potential
contributions to SDGs rather than basing judgments on media portrayals or common
misconceptions about AI capabilities.

Specifically, this empirically-based study aims to investigate whether IT professionals
trust AI,<fnref n="2" /> human experts, or a combination of both in advancing SDGs. This issue is important
because AI-related promises can remain unfulfilled if individuals, especially those who are
most familiar with, do not trust AI-based solutions. Mistrust can come from unjustified AI
aversion or over-emphasis of AI abuses (Zhao & Gómez Fariñas, 2023). Studying trust in AI
is key to maximize its benefits while minimizing potential drawbacks regarding SDGs
(Vinuesa et al., 2020; Nti et al., 2022).

According to the European Commission's AI Watch initiative, a High-Level Expert Group on Artificial Intelligence, AI systems are “software (and possibly also hardware) systems designed by humans that, given a complex goal, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected

<footnote n="2">² It is worth noting that while AI encompasses a broad range of technologies and applications (e.g., machine learning, natural language processing, robotics), our survey referred to AI in a general sense. Given that our respondents are all IT experts, we can reasonably expect a more uniform understanding of AI compared to the public, although we cannot entirely rule out potential variations in interpretation among experts. Future studies might benefit from either providing a clear definition of AI, specifying particular AI applications, or exploring how perceptions of trust differ across various types of AI systems.</footnote>
structured or unstructured data, reasoning on the knowledge, or processing the information,
derived from this data and deciding the best action(s) to take to achieve the given goal”
(Samoili et al., 2021). This definition encompasses core AI technologies including machine
learning (supervised, unsupervised, and reinforcement learning), natural language processing,
computer vision, knowledge representation and reasoning, planning and optimization
algorithms, as well as robotics and multi-agent systems (Samoili et al., 2021). The integration
of AI across diverse fields has sparked considerable research interest. Recent work has
explored the potential of AI to enhance user experience design processes, demonstrating its
capacity to augment human capabilities in creative domains (Stige et al., 2023). Moreover,
investigations into consumer preferences for AI-based recommendations versus human
expertise have demonstrated the complex dynamics of trust in AI (Jin & Zhang, 2023). These
studies underscore the broader implications of AI adoption and highlight the key importance
of understanding human perceptions and trust in AI systems. By examining trust in AI within
the context of SDGs, our study contributes to this growing body of literature, offering insights
into the links between human expertise and AI in addressing global challenges.

The remainder of this article is organized as follows. The next section compares
humans and AI in advancing SDGs. Section 3 is devoted to the survey design and data.
Section 4 provides the main results and discusses them. Section 5 is our conclusion.

## 2. Literature and hypotheses

Before developing our hypotheses, it is worth exploring some differences between humans
and AI with regards to SDGs (Table 1). Although we focus on individuals’ trust in AI and
humans in achieving SDGs rather than measuring how do they effectively contribute to
SDGs, understanding their relative strengths and limitations is important to consider how each
can be effectively leveraged in strategies for sustainable development. For example, in
addressing SDG 13 (Climate Action), AI systems can process vast amounts of climate data and generate sophisticated climate models to predict environmental changes (Vinuesa et al., 2020; see also Saba & Pretorius, 2024 about SDG 3), while human experts contribute key local knowledge, stakeholder engagement skills, and ethical judgment in developing culturally appropriate climate adaptation strategies. This combination has proven particularly effective in coastal regions, where AI's predictive capabilities for sea-level rise complement human experts' understanding of community needs and traditional ecological knowledge in designing resilient infrastructure and evacuation plans (Nishant et al., 2020).

[Table 1 around here]

The comparison between human and AI capabilities reveals distinct but complementary strengths in addressing SDGs challenges. Humans excel in areas requiring emotional intelligence, contextual understanding, and ethical judgment (Ryan, 2020; Wilson et al., 2022). Their ability to integrate complex social cues, adapt to novel situations, and make ethical decisions remains unmatched by artificial systems (Müller, 2020). Particularly in SDG contexts, humans demonstrate superior capabilities in stakeholder engagement, cultural sensitivity, and understanding the broader societal implications of decisions (Nishant et al., 2020). Recent studies highlight humans' unique ability to handle ambiguous situations and make value-based judgments essential for sustainable development (Olorunfemi et al., 2024).

Conversely, AI systems demonstrate superior capabilities in data processing, pattern recognition, and computational tasks (Vinuesa et al., 2020). They excel at analyzing large-scale environmental data, optimizing resource allocation, and identifying complex relationships in sustainability metrics (Yang & Liu, 2024; Nti et al., 2022). AI's strength lies in its ability to process vast amounts of information simultaneously, identify subtle patterns, and maintain consistency in repetitive tasks (Kulkov et al., 2023). However, research indicates that AI's current limitations in understanding context, handling unexpected
situations, and making ethical judgments necessitate human oversight, particularly in sustainability applications where decisions can have far-reaching social and environmental impacts (Zhao & Gómez Fariñas, 2023). These complementary roles in advancing SDGs highlight the need for oversight and safeguards to prevent potential abuses, while suggesting that optimal solutions lie in leveraging the strengths of both approaches.

## *Hypotheses development*

The literature on AI in sustainable development highlights that while AI offers promising avenues for promoting SDGs, it primarily functions within three main aspects: organizational, technical, and processing (Kulkov et al., 2023). Although AI's role in fostering SDGs is acknowledged, its impact is often framed in a complementary rather than a primary capacity compared to human experts. For instance, AI supports organizations in strategic alignment, infrastructure development, and change management, but the actualization of these strategies relies heavily on human judgment and oversight. This dependence suggests that individuals may trust AI in driving sustainable development but perceive human experts as more capable of navigating the complex, interrelated systems that underpin SDGs challenges. The increasing adoption of AI tools raises important questions about human trust and preferences regarding AI versus human expertise (Jin & Zhang, 2023), including in creative fields like user experience design where AI is beginning to augment human capabilities (Stige et al., 2023).

Moreover, the ethical, social, and cultural dimensions of SDGs further underscore the importance of human expertise in this domain. AI systems, while proficient in optimizing processes and enhancing efficiency, often fall short in addressing the ethical implications of their applications. For example, issues such as data privacy, bias in algorithmic decision-making, and the societal impact of AI-driven automation require a level of ethical
consideration and human-centric approach that AI alone cannot provide (Nishant et al., 2020).

Human experts play a key role in ensuring that AI applications align with ethical standards and contribute positively to social equity and justice. This human oversight fosters trust and acceptance among stakeholders, highlighting the key role of human judgment in complementing AI's technical capabilities.

Noteworthy, recent studies provide additional context for understanding trust in AI for sustainability-related concerns. Chen et al. (2023) demonstrated that AI promotes corporate sustainable development through multiple pathways, including improved resource allocation and stakeholder engagement. However, they also found that cultural factors and public pressure can hinder AI's positive effects, supporting the importance of human oversight and expertise in AI implementation. Furthermore, trust in AI can be built through AI anthropomorphization—a process in which people attribute human characteristics to machines. AI lacks intrinsic ethical judgment and is often seen as lacking trustworthiness because it cannot possess emotive states or be held accountable for its actions, two essential requirements for trust according to prevalent definitions (Ryan, 2020). Despite its capacity for rational decisions, AI is limited to providing reliable, yet sometimes ethically inadequate, outcomes. This highlights the need for a balance between AI's analytical capabilities and human expertise's nuanced ethical judgment. This necessity leads AI-experienced individuals to prefer a combination of human expertise and AI for achieving SDGs rather than relying on either alone.

In addition, the interaction between AI and human experts can significantly enhance decision-making processes in sustainable development. AI's analytical capabilities allow for processing vast amounts of data and identifying patterns that may not be immediately evident to human experts. However, human experts contribute to critical ethical judgment, contextual understanding, and adaptability, ensuring that AI-generated insights are applied effectively
and ethically. This symbiotic relationship is key, as AI systems alone may produce ethically inadequate outcomes due to their lack of intrinsic ethical considerations and emotive states, which are essential for trust and accountability (Ryan, 2020). Therefore, AI-experienced individuals are more likely to trust a combination of AI and human expertise to push SDGs forward, leveraging the strengths of both to achieve balanced and ethically sound solutions.

Given the previous discussion, we formulate the following hypotheses:

H1: IT experts trust less AI for achieving SDGs, compared to human experts.

H2: IT experts trust a combination of human experts and AI more than AI alone or humans alone for achieving SDGs.<fnref n="3" />

### 3. Survey design and data

We conducted a survey in January 2024 which was administered with the help of Panelabs, a survey institute that partners with many research institutions. As emphasized in the introduction, Panelabs distributed the questionnaire electronically to a targeted panel consisting of IT project managers and frequent AI users.

In order to test our hypotheses, participants were asked to indicate whether they agree/disagree with three statements about their trust in AI, human experts, and a combination of both, respectively, in reaching SDGs on 7-point Likert scales (1: fully disagree; 7: fully agree). Precisely, the statements read as follows: "I trust the solutions recommended by AI to achieve sustainable development goals", "I trust the solutions recommended by a group of human experts to achieve sustainable development goals", and "I trust the solutions recommended by a combination of human experts and AI to achieve sustainable development goals".

<footnote n="3">³ We acknowledge that differences might exist between experts' behavioral and self-reported trust in AI (see Kulms & Kopp, 2019). Despite its interest, this distinction is beyond the scope of this paper.</footnote>
To ensure respondent familiarity with SDGs before presenting the trust questions, participants were first asked to identify which SDGs their company aims to achieve through AI. This question presented the complete list of all 17 SDGs with their full official descriptions (e.g., Goal 1: End poverty in all its forms everywhere; Goal 13: Take urgent action to combat climate change and its impacts; etc.), serving both as an educational primer and ensuring that subsequent trust responses were based on comprehensive understanding of SDG scope and content.

The questionnaire development followed a systematic multi-stage process to ensure content validity, construct validity, and clarity. First, an initial version of the survey instrument was developed based on established scales from technology acceptance literature and trust research, adapted specifically for the AI and SDG context. Second, the questionnaire underwent expert validation through submission to a panel of 29 experts, comprising 20 academics from diverse business disciplines (finance, marketing, strategy, operations, and management) and 9 industry experts with specialized knowledge in AI applications and implementation. These experts reviewed the questionnaire for content validity, clarity of wording, logical flow, cultural appropriateness across target countries, and suitability for the target population of IT professionals and frequent AI users. Third, based on expert feedback, revisions were made to improve question clarity, eliminate ambiguous terminology, and ensure comprehensive coverage of relevant constructs while maintaining appropriate survey length. Particular attention was given to ensuring that AI-related terminology would be consistently understood across different levels of technical expertise. The final questionnaire was then implemented through Panelabs' electronic survey platform with built-in quality controls.

To ensure data quality, Panelabs implemented several controls. First, the 5% of fastest respondents were excluded to avoid rushed responses. Next, questionnaires with duplicate
Internet Protocol (IP) addresses were removed to ensure that only one questionnaire was filled
per household. The quality of the IP addresses was also verified using multiple criteria, such
as checking for indirect connection usage or IP addresses flagged as malicious. Additionally,
IP geolocation was checked to ensure that they came from the countries targeted by the
survey. Finally, responses to open-ended questions were examined to detect and eliminate
respondents who provided inconsistent answers. To enable comprehensive bias assessment
and methodological evaluation, Table A4 in the Appendix provides the complete survey
instrument exactly as presented to respondents, including all question wording and response
options. This allows independent evaluation of potential bias sources, question clarity, and
response option balance. While potential biases, such as social desirability bias, cannot be
entirely ruled out, the precautions implemented allow for a reasonable degree of trust in the
findings of our survey.

The main characteristics of the sample are reported in Table 2. Respondents are from
three countries: France (44%), the United Kingdom (51%), and Belgium (5%). The spectrum
of company size is broad, ranging from small businesses with 1 to 50 employees (14%) to
large organizations with more than 10,001 employees (18%). The sample is well-balanced
across company size with each range accounting for about 17-18% of the total. In terms of
roles, 89% of respondents are involved in IT project management, and 68% frequently use AI.
The survey reveals that most respondents are within the 30-39 and 40-49 age groups,
representing 36% and 34% of participants, respectively. This indicates a significant
concentration of individuals in their prime working years. The 20-29 and 50-59 age groups
each make up 12% of the respondents, suggesting a notable, yet more modest, participation
from younger and older professionals. Meanwhile, the 60-69 age group accounts for just 5%,
reflecting a smaller representation of senior professionals in the survey. Gender parity is
respected among the participants, with a balanced distribution between men and women, each representing 50% of respondents. This helps to avoid gender bias in responses.

Diversity is also present at hierarchical levels: 40% are middle managers, followed by 23% senior executives and 27% employees. The services sector accounts for 30% of respondents and the finance, insurance, and real estate sector represents 14% of the respondents. The transport, communications, electricity, gas, and sanitary services sector comprises 9% of the survey respondents, highlighting the adoption of AI in these essential infrastructure areas. The retail sector contributes 7% of the respondents, reflecting its increasing interest in AI for customer insights and operational efficiency. Finally, the data indicates that a sizable portion of respondents, around 40%, work in a diverse array of sectors outside the major categories, including agriculture, mining, and manufacturing. In terms of employment type, the vast majority (86%) of participants hold a full-time position. Part-time workers represent 7%, complemented by self-employed workers (4%).

[Table 2 around here]

**4. Results and discussion**

Our analysis employed a dual approach to handling the 7-point Likert scale data, following best practices in the literature. For hypothesis testing, we treated responses as continuous variables and conducted paired t-tests, an approach supported by methodological literature demonstrating that parametric tests are not only appropriate but statistically superior when Likert scales have five or more response points (Norman, 2010) and are interpreted at the aggregate level (Lalla, 2017). This continuous treatment is particularly well-suited for trust research, where neutral attitudinal gradations are theoretically meaningful. For the multinomial logistic regression, we employed a categorical variable identifying which option (AI, human, or combination) received the highest trust rating, providing methodological triangulation. Neutral responses (point 4) were deliberately retained, as methodological
research demonstrates they reflect theoretically relevant ambivalence rather than measurement error (Kulas & Stachowski, 2009), which is particularly important in AI trust research where uncertainty represents a valid attitude. The consistency of results across both statistical approaches provides evidence for the robustness and validity of our findings.

Mean responses regarding trust in AI, human experts and a combination of both in reaching SDGs along with comparison tests (paired t-tests) are reported in Figure 1. We observed that respondents trust human experts more than AI for reaching SDGs, which supports our hypothesis H1. Moreover, participants significantly trust a combination of human experts over the two other options, although the difference with human experts is only significant at the 10% level. In other words, our hypothesis H2 is supported.

[Figure 1 around here]

To further illustrate the overall preference patterns, Figure 2 presents the distribution of trust preferences when respondents' highest individual trust scores are considered. The pie chart reveals a preference hierarchy among IT professionals: two-thirds (66.8%) trust the AI-human combination most, while 26.1% prefer human experts alone, and only 7.1% trust AI alone most highly. This distribution strongly supports our hypothesis H2, demonstrating that most IT professionals view collaborative human-AI approaches as superior to either standalone option for achieving SDGs. The dominance of the combination approach (nearly 67% of respondents) suggests that IT professionals recognize the complementary strengths of both AI capabilities and human expertise in addressing sustainable development challenges.

[Figure 2 around here]

To further analyze the factors driving participants' trust in human experts and the combination of AI and human experts over AI alone, we employed a multinomial logistic regression model with trust in AI as the base outcome. Due to the small number of respondents from Belgium (22), the regression analysis was restricted to French and British
individuals. Pearson correlation coefficients were calculated to check for multicollinearity, revealing no significant issues (results not reported but available from the authors upon request). We also examined potential multicollinearity issues through Variance Inflation Factor (VIF) analysis. As shown in Table A1 in the Appendix, all VIF values are well below the conventional threshold of 5, with a mean VIF of 1.9, indicating no significant multicollinearity concerns.

We included various individual characteristics as independent variables in the multinomial logistic regression model. These encompass demographic factors (e.g., age, gender), professional attributes (e.g., job role, full-time employment status, work in digital departments, involvement in IT-related projects), and company characteristics (e.g., company size, sector). The model aimed to identify how these factors influenced trust in human experts and the combination of AI and human experts compared to trust in AI alone.

[Table 3 around here]

The main result from Table 3 is that respondents who use AI most regularly are more likely to trust human experts and the combination of AI and human experts over AI alone to reach SDGs. Figure 3 visualizes this counterintuitive finding, showing that regular AI users demonstrate markedly different trust patterns compared to infrequent users. Specifically, regular AI users show higher trust in all three approaches: AI alone (*mean* = 4.69 vs. 3.32), human experts (*mean* = 5.18 vs. 5.02), and human-AI combinations (*mean* = 5.38 vs. 4.89). Interestingly, while regular AI users have substantially higher baseline trust in AI (a 1.37-point difference), the trust gaps between AI alone and human-involved approaches are smaller for regular users compared to infrequent users. For regular users, the gaps are 0.49 points (human experts vs. AI) and 0.69 points (combination vs. AI), while for infrequent users these gaps are much larger at 1.70 and 1.57 points respectively. This pattern suggests that regular AI users develop more calibrated trust assessments, with higher confidence in AI's
capabilities but also greater appreciation for the complementary value of human expertise in achieving SDGs.

[Figure 3 around here]

Our findings complement recent research on AI's role in sustainable development. While Chen et al. (2023) identified AI's positive impact on corporate sustainability through improved resource utilization and stakeholder management, Nasir et al. (2022) highlighted significant gaps in AI application across different SDGs. Our results suggesting higher trust in human-AI collaboration rather than AI alone may help address these imbalances by ensuring more comprehensive coverage of SDGs through combined human expertise and AI capabilities.

While our paper is empirically-based, it is worth noticing that our findings can be interpreted using the technology acceptance literature framework, particularly the Technology Acceptance Model (TAM) (Davis, 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). First, the relationship between most regular AI use and higher trust in human-AI collaboration reflects TAM's core principle that actual system uses shapes perceptions and acceptance patterns. Specifically, those with more AI experience appear to develop a more nuanced understanding of how to optimally leverage AI alongside human expertise rather than viewing it as a standalone solution. Second, the higher trust placed in human experts over AI alone resonates with UTAUT's performance expectancy construct - respondents appear to evaluate human experts as more capable of addressing the complex, multifaceted challenges of SDGs that require contextual understanding and ethical judgment. Third, the strong preference for human-AI collaboration aligns with UTAUT's facilitating conditions framework, suggesting that respondents view human expertise as a critical organizational support structure that enables effective AI implementation in SDGs contexts. This theoretical interpretation helps explain why our results show that regular AI
users, rather than trusting AI more, develop a more sophisticated understanding of its optimal
deployment in combination with human expertise.

Having established this theoretical grounding for our findings, it is important to address the statistical robustness of our analysis. While our model’s Pseudo R² below 20% might appear modest, this is not uncommon in social science research examining trust and behavioral factors. As Hemmert et al. (2016) note, logistic regression models in social sciences frequently exhibit Pseudo R² values below 0.2, particularly when studying complex human behaviors and perceptions. In the context of our study, the Pseudo R² value reflects the inherent complexity of trust dynamics in human-AI interactions, where decisions are shaped by firsthand experiences, organizational context, and broader societal factors beyond the measured variables.

To ensure the validity of our findings, we conducted several robustness checks. First, we re-estimated our main specification using robust standard errors (Table A2), which confirmed the significance of our key findings regarding most regular AI use. Second, we conducted additional analyses of trust drivers through ordered logistic regressions (Table A3), providing further support for our main conclusions. Finally, we tested the stability of our results by omitting various control variables, with results remaining consistent throughout these alternative specifications (detailed results available upon request).

Our findings have several important practical consequences for the implementation of
AI in SDGs contexts. The result that surveyed people tend to trust a combination of AI and
humans more than AI alone to reach the SDGs suggests that they recognize the need for
human oversight and control over AI systems used for achieving the SDGs. Humans must be
involved in the decision-making process, especially for high-stakes decisions impacting
human lives and rights to ensure an unbiased, ethical and responsible use of AI. A common
objection to AI alone is the lack of transparency and accountability. We contend that AI
systems used for SDG-related applications must be transparent and explainable, allowing humans to understand the reasoning behind their decisions and outputs. The development and implementation of AI solutions for the SDGs should involve diverse stakeholders, including marginalized communities, to ensure their needs and perspectives are considered. The mix of human experts and AI tools allows us to guarantee accountability. Rather than considering that everyone knows AI tools as most respondents in our survey, we posit that there is a need for capacity-building and education programs to equip policymakers, managers, and the public with the knowledge and skills to understand and effectively manage AI systems used for the SDGs. All these initiatives could help to keep humans at the center and lead to greater trust and more responsible AI for sustainable development.

Drawing on our empirical results, we outline four steps policymakers can take to embed meaningful human-AI collaboration in SDG initiatives. First, we advocate for systematic human oversight. Any AI tool deployed for SDG purposes should undergo an ethics review, approved—and where appropriate, signed—by qualified experts. This reflects our finding that trust peaks when human judgment is formally integrated into the process. It also aligns with the spirit of the European Union AI Act (EU AI Act). Second, collaboration should be calibrated to risk. High-stakes decisions (e.g., disaster-response planning) warrant intensive joint scrutiny, whereas routine, low-risk tasks may rely on lighter-touch human checks. This approach is consistent with the preferences of experienced AI users in our sample. Third, it is essential to insist on transparency and explainability. Every key AI recommendation should be accompanied by a plain-language rationale for the decision. This, again, aligns with the EU AI Act's recommendations. Fourth, we recommend investing in targeted capacity-building. Ongoing training programs should empower human experts to read, question, and refine AI outputs, helping close the skills gap we observed—even among frequent AI users.
Our study advances current literature on digitalization and SDGs, by providing insights into the trust dynamics between AI tools and human experts in the context of SDG achievement (ElMassah & Mohieldin, 2020). Moreover, it contributes to the broader fields of human-computer interaction and technology acceptance by exploring perspectives on AI-human collaboration (Xu et al., 2021). Additionally, it informs the discourse on ethical AI deployment (Olorunfemi et al., 2024), emphasizing the necessity of maintaining human oversight in automated processes to mitigate risks associated with technological advancements.

## 5. Conclusion

Using a survey, we investigated whether AI-expert users trusted AI or humans for advancing SDGs. Our main finding is that surveyed people think a *combination of humans and AI is the best option to advance SDGs*. This finding suggests that, from one side, surveyed individuals trust the potential of AI tools to address global challenges, and, on the other side, they caution us against an over-reliance on AI or complete delegation and stress the need of human experts. We also discussed some tentative implications of our findings, but we remain modest and do not over-interpret the results of a one-time survey that cannot perfectly reflect an ongoing process.

Our study has some limitations that also indicate avenues for further research. First, AI tools are evolving permanently, and their perception is also likely to evolve. While we could expect enhanced performance on several grounds, we cannot rule out that this evolution can also lead to unethical outcomes, inconsistent with SDGs. So, enhancing and repeating this study at various points of time makes sense to better understand how these perceptions will evolve over time. Second, we considered SDGs in a broad sense while it is rather multi-faceted and can encompass environmental, social, and economic aspects, that can be reached
with various AI tools. For example, AI systems to make energy consumption more efficient and sustainable are very different from AI systems that are used to help visually impaired people or support the elderly for more social inclusion. Analyzing trust in AI by sustainability dimension is challenging. We also presented trust in a simplified, binary, or hierarchical manner (AI vs. humans), and our measurement of trust did not account for situational factors (e.g., decision type, level of AI autonomy, domain of application), which could limit the ecological validity of our findings. Considering trust in a more complex way—as a dynamic and situationally contingent construct—could enrich future research contributions. Third, our respondents were people familiar with AI tools while an important part of the human population concerned by SDGs are not frequent users of AI tools, especially in less developed countries. It is important to extend the range of surveyed people to include people unfamiliar with AI tools and avoid a WEIRD (Western, Educated, Industrialized, Rich, Democratic) bias. Indeed, this extension will allow us to reach a more representative view of how people perceived the role of AI in reaching SDGs. Fourth, while our focus on IT professionals and frequent AI users provided valuable insights from those with direct AI experience, it also represents a limitation in terms of sample diversity. Our respondents were primarily technology experts from developed nations, which may not fully represent the broader population's trust perceptions regarding AI and SDGs. Future research should expand participant diversity in several ways: (1) include individuals from various professional backgrounds beyond IT, particularly those working directly on SDG initiatives; (2) incorporate perspectives from developing nations where AI implementation for SDGs may face different challenges; (3) survey policy makers and NGO workers who influence AI adoption in SDG contexts; and (4) consider conducting comparative studies between AI experts and non-experts to understand how technical knowledge influences trust perceptions.
This expanded sampling approach would provide a more comprehensive understanding of societal trust in AI for advancing SDGs.

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<table><caption>Table 1. Comparing the capabilities of humans and AI</caption><thead><tr><th></th><th>Humans</th><th>Artificial intelligence</th></tr></thead><tbody><tr><td>Creativity</td><td>Humans excel in creative thinking, imagination, and artistic expression. They generate innovative ideas and adapt creatively to new situations. Human creativity comes from deep cognitive processes that blend experiences, emotions, and intuition to produce original and meaningful artistic works. This inherent artistic sensibility is difficult for AI to replicate, as humans can evaluate creative quality in ways that transcend mere data analysis (Criminali & Leonelli, 2023).</td><td>AI can assist in creative tasks such as generating art, music, or writing based on learned patterns and algorithms. However, it lacks human intuition and the ability to contextually evaluate creative quality, often generating outputs by mimicking patterns from existing data rather than genuinely creating (Esling & Devis, 2020).</td></tr><tr><td>Learning and adaptation</td><td>Humans excel at learning from their experiences, adapting behavior to new circumstances, and forming mental models that blend diverse sensory inputs, context, and prior knowledge. This allows for abstract reasoning, generalization, and handling novel situations with minimal data. Moreover, their capacity for metacognition enables humans to reflect on their thought processes, evaluate strategies, and adjust their learning mechanisms (Bellaiche et al., 2023).</td><td>AI systems, by contrast, rely heavily on supervised learning, leveraging vast datasets for training. They are good in pattern recognition but struggle with abstract reasoning and adaptability in unstructured environments. Continuous training and feedback loops improve their performance within specific domains, but their understanding and adaptability remain limited to pre-defined patterns and objectives (Bellaiche et al., 2023).</td></tr><tr><td>Emotional intelligence</td><td>Humans have a powerful ability to perceive, interpret, and respond to their own emotions and those of others. This is deeply linked to social interactions and relationships, where empathy, understanding, and emotional regulation play critical roles. Human emotional intelligence also includes the awareness of social norms and the ability to use emotional information to guide thinking and behavior (Whelan et al., 2019).</td><td>AI, through emotion AI technologies, has made improvements in recognizing and responding to human emotions by analyzing data such as voice inflections, facial expressions, and physiological signals. However, while AI can mimic aspects of emotional recognition, it lacks the depth of human experience and emotional responsiveness. AI's interactions are based on algorithms that detect patterns rather than genuine emotional comprehension, which limits its ability to fully understand or empathize with human emotions (Meng & Dai, 2021).</td></tr><tr><td>Physical abilities</td><td>Humans possess remarkable physical abilities due to their musculoskeletal system and central nervous system (CNS). The musculoskeletal system provides flexibility, enabling energy-efficient movements and shock absorption. The CNS effectively controls the musculoskeletal system, allowing for versatile movements, learning by repetition, and adaptation to new environments (Angelini et al., 2020).</td><td>AI lacks physical form but can control physical systems through robotics and automation. AI-powered robots can perform tasks like manufacturing, surgery, and exploration in environments that may be hazardous or inaccessible to humans (Angelini et al., 2020).</td></tr><tr><td>Ethical judgment</td><td>Humans possess ethical judgment and moral reasoning, which allow them to evaluate their decisions based on complex ethical principles and societal norms. They have an inherent understanding of agency and responsibility, which allows them to accurately assess moral wrongs and intentions in various real-world scenarios (Wilson et al., 2022).</td><td>AI systems lack inherent ethical judgment, relying instead on predefined rules, algorithms, and ethical frameworks. While some envision AI developing ethical decision-making skills, the challenge lies in embedding moral principles that accurately reflect human ethics and making those decisions interpretable and explainable (Müller, 2020).</td></tr><tr><td>Social intelligence</td><td>Humans possess advanced social intelligence, enabling them to navigate complex social situations through the interpretation of emotions and intentions. They have an important level of accuracy in understanding human interactions and making sense of social situations by integrating multimodal signals, such as body language, tone of voice, and facial expressions. This capacity allows humans to judge intentions and respond appropriately (Zadeh et al., 2019).</td><td>AI's current capabilities in social intelligence are measured through benchmarks like Social-IQ, which assesses AI's ability to understand social interactions by evaluating its responses to questions about real-world social situations. While AI systems have made quick progress, they still lag behind humans in their ability to comprehend and interpret nuanced social behaviors, as evidenced by their lower accuracy in social intelligence tasks compared to human performance (Zadeh et al., 2019).</td></tr></tbody></table>
<table><caption>Table 2. Sample characteristics (N=422)</caption><thead><tr><th>Variables</th><th></th><th>Mean</th></tr></thead><tbody><tr><td rowspan="3">Country of origin</td><td>UK</td><td>0.51</td></tr><tr><td>France</td><td>0.44</td></tr><tr><td>Belgium</td><td>0.05</td></tr><tr><td rowspan="3">Position</td><td>Senior manager</td><td>0.30</td></tr><tr><td>Manager</td><td>0.40</td></tr><tr><td>Other</td><td>0.30</td></tr><tr><td colspan="2">Fulltime job</td><td>0.85</td></tr><tr><td colspan="2">Work in digital department</td><td>0.66</td></tr><tr><td colspan="2">Work on IT related projects</td><td>0.89</td></tr><tr><td colspan="2">Regular use of AI</td><td>0.68</td></tr><tr><td rowspan="5">Company size</td><td>Very small (less than 50 employees)</td><td>0.14</td></tr><tr><td>Small (between 51 and 200 employees)</td><td>0.11</td></tr><tr><td>Medium (between 201 and 5000 employees)</td><td>0.50</td></tr><tr><td>Big (more than 5000 employees)</td><td>0.25</td></tr><tr><td>Sector 1 (transport, communications, electricity, gas, and sanitary services)</td><td>0.09</td></tr><tr><td rowspan="5">Sector of activity</td><td>Sector 2 (retailing)</td><td>0.07</td></tr><tr><td>Sector 3 (finance, insurance, and real estate)</td><td>0.14</td></tr><tr><td>Sector 4 (services)</td><td>0.30</td></tr><tr><td>Other sectors (agriculture, mining, manufacturing, etc.)</td><td>0.40</td></tr><tr><td>Age</td><td>Under 30 years old</td><td>0.12</td></tr><tr><td rowspan="5">Gender (=1 if male)</td><td>Between 30 and 39 years old</td><td>0.36</td></tr><tr><td>Between 40 and 49 years old</td><td>0.34</td></tr><tr><td>50 years old and above</td><td>0.18</td></tr><tr><td>Without a university degree</td><td>0.50</td></tr><tr><td>Education</td><td>Bachelor</td><td>0.40</td></tr><tr><td></td><td>Master and more</td><td>0.39</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>Figure 1. Trust in AI, human experts and a combination of both in reaching SDGs.</figcaption></figure>

N=422. Scale: 1=Strongly Disagree to 7=Strongly Agree. \*p\<0.05, \*\*p\<0.01, \*\*\*p\<0.001
<caption>Figure 2: Distribution of Trust Preferences for Achieving SDGs</caption>

Distribution of Trust Preferences for Achieving SDGs

Which Approach Do IT Professionals Trust Most?

<figure><img src="image_3.png" /></figure>

N=422. Based on highest individual trust score
<figure><img src="image_4.png" /><figcaption>Figure 3: Trust Preferences by AI Usage Frequency</figcaption></figure>
<table><caption>Table 3. Multinomial logistic estimation of participants' characteristics on the trust in human experts and the combination of AI and human experts (trust in AI as base outcome)</caption><thead><tr><th rowspan="2">Variables</th><th colspan="2">Coefficients and significance</th></tr><tr><th>Trust in human experts</th><th>Trust in the combination</th></tr></thead><tbody><tr><td>French</td><td>0.51 (0.53)</td><td>0.76 (0.50)</td></tr><tr><td>Senior</td><td>0.15 (0.72)</td><td>0.03 (0.68)</td></tr><tr><td>Manager</td><td>-0.36 (0.73)</td><td>0.07 (0.69)</td></tr><tr><td>Fulltime job</td><td>-0.57 (0.94)</td><td>-0.39 (0.88)</td></tr><tr><td>Digital department</td><td>-0.66 (0.57)</td><td>-0.46 (0.53)</td></tr><tr><td>IT related projects</td><td>1.19 (1.20)</td><td>1.22 (1.15)</td></tr><tr><td>Regular use of AI</td><td>2.64** (1.09)</td><td>2.56** (1.08)</td></tr><tr><td>Company size: Small</td><td>-0.85 (1.07)</td><td>-1.16 (0.98)</td></tr><tr><td>Company size: Medium</td><td>-0.17 (0.98)</td><td>-0.56 (0.92)</td></tr><tr><td>Company size: Big</td><td>0.45 (1.17)</td><td>0.23 (1.11)</td></tr><tr><td>Sector: transport, communications, electricity, gas, and sanitary services</td><td>-1.18 (0.84)</td><td>-0.55 (0.68)</td></tr><tr><td>Sector: retailing</td><td>-0.23 (0.76)</td><td>-1.32* (0.73)</td></tr><tr><td>Sector: finance, insurance, and real estate</td><td>1.14 (0.86)</td><td>0.60 (0.83)</td></tr><tr><td>Sector: services</td><td>0.83 (0.63)</td><td>0.30 (0.60)</td></tr><tr><td>Age: 30-39</td><td>0.29 (0.76)</td><td>-0.17 (0.67)</td></tr><tr><td>Age: 40-49</td><td>0.67 (0.83)</td><td>-0.09 (0.75)</td></tr><tr><td>Age: above 50</td><td>1.70 (1.09)</td><td>0.33 (1.03)</td></tr><tr><td>Male</td><td>-0.17 (0.49)</td><td>-0.43 (0.46)</td></tr><tr><td>Education: Bachelor</td><td>1.26* (0.72)</td><td>1.33* (0.68)</td></tr><tr><td>Master Education and more</td><td>0.29 (0.66)</td><td>0.08 (0.61)</td></tr><tr><td>Cons.</td><td>-3.27 (2.35)</td><td>-1.29 (2.21)</td></tr><tr><td>Number of observations</td><td colspan="2">400</td></tr><tr><td>Log Likelihood</td><td colspan="2">-287.79</td></tr><tr><td>LR Chi2</td><td colspan="2">64.34***</td></tr><tr><td>Pseudo R2</td><td colspan="2">0.10</td></tr></tbody></table>

Standard errors between brackets. ***, **, and * refer to parameter significance at the 1%, 5%, and 10% levels, respectively.
# Appendix

<caption>Table A1: Variation Inflation Factor</caption>

This table presents the Variation Inflation Factor (VIF) values for various predictor variables used in the regression analysis. The variables include different company sizes, age groups, education levels, job positions, work-related attributes, and sector categories. The mean VIF value is also provided.

<table><thead><tr><th></th><th>(1)<br>VIF</th></tr></thead><tbody><tr><td>Company size: Medium</td><td>3.26</td></tr><tr><td>Company size: Big</td><td>3.08</td></tr><tr><td>Age: 40-49</td><td>2.96</td></tr><tr><td>Age: 30-39</td><td>2.80</td></tr><tr><td>Age: above 50</td><td>2.65</td></tr><tr><td>Master Education and more</td><td>2.23</td></tr><tr><td>Education: Bachelor</td><td>2.05</td></tr><tr><td>Senior</td><td>1.92</td></tr><tr><td>Company size: Small</td><td>1.83</td></tr><tr><td>Manager</td><td>1.74</td></tr><tr><td>Digital department</td><td>1.44</td></tr><tr><td>Fulltime job</td><td>1.40</td></tr><tr><td>Sector: services</td><td>1.36</td></tr><tr><td>Regular use of AI</td><td>1.36</td></tr><tr><td>French</td><td>1.35</td></tr><tr><td>IT related projects</td><td>1.32</td></tr><tr><td>Sector: transport, communications, electricity, gas, and sanitary services</td><td>1.22</td></tr><tr><td>Sector: finance, insurance, and real estate</td><td>1.20</td></tr><tr><td>Male</td><td>1.15</td></tr><tr><td>Sector: retailing</td><td>1.13</td></tr><tr><td>Mean VIF</td><td>1.87</td></tr></tbody></table>
Table A2 : Main specification with robust standard errors

This table presents the results of our main regression analysis with robust standard errors instead of standard errors used in Table 3. The coefficients for regular use of AI are reported along with robust standard errors in parentheses. Control variables from our main specification are included in the model. The R-squared value and the number of observations are also provided.

<table><thead><tr><th></th><th>(1)<br>Trust in human experts</th><th>(2)<br>Trust in the combination</th></tr></thead><tbody><tr><td>Regular use of AI</td><td>2.64**<br>(1.12)</td><td>2.56**<br>(1.09)</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td></tr><tr><td>R<sup>2</sup></td><td colspan="2">0.10</td></tr><tr><td>Observations</td><td colspan="2">400</td></tr></tbody></table>

Robust standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.
Table A3: Drivers of trust in humans, AI and combination

The table presents the results of three ordered logistic regressions analyzing the drivers of trust in different contexts: Trust in AI, Trust in Humans, and Trust in the combination of both. The variables include demographic characteristics, company size, sector affiliation, and job-related attributes. The table also reports the R-squared values and the number of observations for each model.

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr><tr><th></th><th>Trust in AI</th><th>Trust in Humans</th><th>Trust in the<br>combination</th></tr></thead><tbody><tr><td>French</td><td>-0.22*<br>(0.12)</td><td>-0.17<br>(0.12)</td><td>0.16<br>(0.12)</td></tr><tr><td>Digital department</td><td>0.11<br>(0.14)</td><td>-0.20<br>(0.14)</td><td>-0.08<br>(0.14)</td></tr><tr><td>IT related projects</td><td>-0.56***<br>(0.20)</td><td>-0.07<br>(0.20)</td><td>-0.12<br>(0.21)</td></tr><tr><td>Regular use of AI</td><td>-0.85***<br>(0.13)</td><td>-0.09<br>(0.13)</td><td>-0.39***<br>(0.13)</td></tr><tr><td>Company size: Small</td><td>0.27<br>(0.23)</td><td>0.03<br>(0.23)</td><td>0.23<br>(0.23)</td></tr><tr><td>Company size: Medium</td><td>0.19<br>(0.19)</td><td>0.24<br>(0.19)</td><td>0.39**<br>(0.19)</td></tr><tr><td>Company size: Big</td><td>0.12<br>(0.21)</td><td>0.22<br>(0.21)</td><td>0.39*<br>(0.21)</td></tr><tr><td>Age: 30-39</td><td>0.19<br>(0.18)</td><td>0.15<br>(0.18)</td><td>0.18<br>(0.18)</td></tr><tr><td>Age: 40-49</td><td>-0.01<br>(0.19)</td><td>0.34*<br>(0.19)</td><td>0.15<br>(0.19)</td></tr><tr><td>Age: above 50</td><td>-0.31<br>(0.22)</td><td>0.42<br>(0.22)</td><td>0.12<br>(0.22)</td></tr><tr><td>Male</td><td>0.15<br>(0.11)</td><td>0.06<br>(0.11)</td><td>-0.04<br>(0.11)</td></tr><tr><td>Senior</td><td>0.46***<br>(0.16)</td><td>0.40**<br>(0.16)</td><td>0.18<br>(0.16)</td></tr><tr><td>Manager</td><td>0.13<br>(0.14)</td><td>0.16<br>(0.14)</td><td>0.13<br>(0.14)</td></tr><tr><td>Sector: transport, communications</td><td>0.01<br>(0.20)</td><td>-0.12<br>(0.20)</td><td>-0.14<br>(0.20)</td></tr><tr><td>Sector: retailing</td><td>-0.12<br>(0.23)</td><td>-0.23<br>(0.23)</td><td>-0.42*<br>(0.23)</td></tr><tr><td>Sector: finance, insurance, and real estate</td><td>0.10<br>(0.17)</td><td>-0.09<br>(0.17)</td><td>-0.15<br>(0.17)</td></tr><tr><td>Sector: services</td><td>-0.05<br>(0.13)</td><td>0.21<br>(0.13)</td><td>-0.11<br>(0.13)</td></tr><tr><td>Fulltime job</td><td>0.05<br>(0.18)</td><td>-0.17<br>(0.18)</td><td>-0.53***<br>(0.19)</td></tr><tr><td>Education: Bachelor</td><td>0.08<br>(0.15)</td><td>0.03<br>(0.15)</td><td>0.20<br>(0.15)</td></tr><tr><td>Education: Master and more</td><td>0.13<br>(0.16)</td><td>-0.03<br>(0.16)</td><td>0.17<br>(0.16)</td></tr><tr><td>R<sup>2</sup></td><td>0.08</td><td>0.02</td><td>0.02</td></tr><tr><td>Observations</td><td>400</td><td>400</td><td>400</td></tr></tbody></table>

Standard errors in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01.
<table><caption>Table A4 : Variables and corresponding survey questions</caption><thead><tr><th>Variable</th><th>Question</th><th>Possible Answers</th></tr></thead><tbody><tr><td>Age variables</td><td>Which age bracket do you belong to?</td><td>Under 20 / 20-29 / 30-39 / 40-49 / 50-59 / 60-69 / 70-79 / 80+</td></tr><tr><td>Digital department</td><td>In which department do you work?</td><td>General Management / Administration / IT / Data / Digital / Legal / Human Resources / Sales / Research / Marketing / Purchasing / Communications / Advertising / Training / Research & Development / Production / Finance / Accounting / Other</td></tr><tr><td>Education variables (Bachelor, Master...)</td><td>What is the highest degree you have obtained?</td><td>No formal education / Secondary education / High school diploma / Bachelor's / Equivalent / Master's / Equivalent / Doctorate (Ph.D.) or higher</td></tr><tr><td>Firm size variables</td><td>How many employees are there in your company?</td><td>1 to 50 / 51 to 200 / 201 to 500 / 501 to 1 000 / 1 001 to 5 000 / 5 001 to 10 000 / 10 001+</td></tr><tr><td>French</td><td>What is your country of residence?</td><td>France / Great Britain / Belgium</td></tr><tr><td>Fulltime job</td><td>What is your type of employment?</td><td>Full-time / Part-time / Intern / Self-employed / Prefer not to answer</td></tr><tr><td>IT related projects</td><td>In your professional role, are you involved in deploying IT projects (AI, infrastructure, servers, machine learning, data management, cloud, websites, software, apps...)?</td><td>Yes / No</td></tr><tr><td>Male</td><td>You identify as</td><td>Male / Female / Other: please specify / Prefer not to say</td></tr><tr><td>Regular use of AI</td><td>Do you regularly use artificial intelligence?</td><td>Yes / No</td></tr><tr><td>Sector variables</td><td>Which sector does your company belong to?</td><td>Agriculture, Forestry & Fishing / Mining / Construction / Manufacturing / Transport, Communications, Electricity, Gas & Sanitary Services / Wholesale Trade / Retail Trade / Finance, Insurance & Real Estate / Services / Public Administration / Other (please specify)</td></tr><tr><td>Seniority variables (Senior, Manager)</td><td>What is your position within the company?</td><td>Executive / Senior Manager / Middle Manager / Employee / Intern / Other (please specify) / Prefer not to answer</td></tr><tr><td>Trust in AI</td><td>I trust solutions recommended by AI to achieve sustainable development goals.</td><td>Strongly disagree / Disagree / Slightly disagree / Neutral / Slightly agree / Agree / Strongly agree</td></tr><tr><td>Trust in combination</td><td>I trust solutions recommended by a combination of human experts and AI to achieve sustainable development goals.</td><td>Strongly disagree / Disagree / Slightly disagree / Neutral / Slightly agree / Agree / Strongly agree</td></tr><tr><td>Trust in human experts</td><td>I trust solutions recommended by a group of human experts to achieve sustainable development goals.</td><td>Strongly disagree / Disagree / Slightly disagree / Neutral / Slightly agree / Agree / Strongly agree</td></tr></tbody></table>