# XAI for safe AI: technical-legal misconceptions and misalignments

Marina Buitrago-Perez, Jose Maria de Fuentes, Ofelia Tejerina, Lorena

Gonzalez-Manzano, Nicolas Anciaux, Luis Ibanez-Lissen

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## To cite this version:

Marina Buitrago-Perez, Jose Maria de Fuentes, Ofelia Tejerina, Lorena Gonzalez-Manzano, Nicolas Anciaux, et al.. XAI for safe AI: technical-legal misconceptions and misalignments. JNIC 2025 - Jornadas Nacionales de Investigación en Ciberseguridad, Jun 2025, Zaragoza, Spain. hal-05203549

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# XAI for safe AI: technical-legal misconceptions and misalignments

Marina Buitrago-Perez

Computer Security Lab (COSEC)

Universidad Carlos III de Madrid

Leganes, Spain

100428967@alumnos.uc3m.es

Jose Maria de Fuentes

Computer Security Lab (COSEC)

Universidad Carlos III de Madrid

Leganes, Spain

josemaria.defuentes@uc3m.es

Ofelia Tejerina

Observatorio LegalTech

Garrigues-ICADE

Madrid, Spain

otejerina@comillas.edu

Lorena Gonzalez-Manzano

Computer Security Lab (COSEC)

Universidad Carlos III de Madrid

Leganes, Spain

lgmanzan@inf.uc3m.es

Nicolas Anciaux

PETSCRAFT project-team

Inria, Université Paris-Saclay, UVSQ

Paris, France

nicolas.anciaux@inria.fr

Luis Ibanez-Lissen

Computer Security Lab (COSEC)

Universidad Carlos III de Madrid

Leganes, Spain

luibanez@pa.uc3m.es

**Abstract**—Building trusted AI models is of outmost relevance both from a societal and legal perspectives. In this vein, explainable AI (XAI) has been set as one of the technical cornerstones. For XAI to be successful, the two perspectives, namely regulators and technologists, must be aligned. However, this cannot be taken for granted considering the different evolution paces of both perspectives in the context of AI. This paper identifies a number of misalignments between legal and technical issues. Moreover, a survey, distributed to 31 practitioners of both perspectives, show a number of misconceptions, that is, technical and legal issues that are misleading or unknown by professionals, thus calling for a coordinated development for both perspectives in the short term.

**Index Terms**—AI, explainable AI, safe AI, techniques, regulations, misalignments, misconceptions

## I. INTRODUCTION

Artificial Intelligence (AI) has become an integral component of modern technological ecosystems, driving innovations across diverse fields such as healthcare, finance, and autonomous systems. The increasing reliance on AI models, particularly those based on deep learning, has raised concerns about their robustness, fairness, and reliability. Many AI systems operate as black boxes, making it difficult to interpret their decision-making processes, which leads to significant risks in critical applications [1]. Security threats such as adversarial attacks [2] and data poisoning [3] have further exacerbated concerns regarding the safe deployment of AI. These vulnerabilities underscore the necessity for frameworks that enhance AI transparency and accountability.

Explainable AI (XAI) has emerged as a promising avenue to address the opaqueness of AI models by providing insights into their decision-making mechanisms [4]. The goal of XAI is to produce human-interpretable explanations of AI outputs,

thereby increasing user trust and enabling better oversight of AI behavior. However, while XAI methods contribute to understanding AI behavior, they do not necessarily guarantee the trustworthiness of AI systems, as explanations may not be insightful enough.

The concept of safe AI goes beyond interpretability. It includes requirements such as robustness, fairness, accountability, and compliance with legal and ethical standards [5]. Ensuring AI safety requires a combination of technical safeguards, regulatory measures, and socio-technical considerations. Despite advancements in AI governance, there is a gap between explainability and actual AI safety, making it crucial to analyze the limitations of XAI in achieving safe AI.

Several prior works have explored the relationship between XAI and safe AI. [6] discuss different explainability techniques and their applicability in high-stakes environments, while [7] examine how XAI can aid in identifying adversarial vulnerabilities. Additionally, [8] investigate counterfactual explanations as a means of enhancing AI accountability, though their effectiveness in ensuring compliance with legal standards remains debated. Other studies, such as [9], argue that inherently interpretable models should be prioritized over post hoc explainability techniques to achieve true AI safety.

**Motivation.** Despite these efforts, there is still an unre-solved technical-legal challenge in aligning XAI methodolo-gies with the stringent requirements of safe AI, warranting further interdisciplinary research. However, this requires mu-tual knowledge between technologists and lawyers, which is yet to be proven. For example, the European AI Act requires human operators be able “to correctly interpret the high-risk AI system’s output”. However, from a technical viewpoint, interpreting, explaining and understanding are three different notions with diverse mechanisms and level of maturity. There-fore, successful development of safe AI models requires a common ground of understanding between all stakeholders.

This work has been supported by Grant PID2023-1503100B-100 (MORE4AIO, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, the Spanish Agencia Estatal de Investigación and FEDER funds
<figure><img src="image_3.png" /><figcaption>Fig. 1: Methodology</figcaption></figure>

**Contribution.** In this paper, we analyse the degree of mutual knowledge between these communities. This helps us identify a number of *misalignments*, which unveil the fields in which more joint cooperation is required. In the same vein, we identify a number of *misconceptions*, that is, notions that are misleading or unknown. These findings pave the way for the creation of a common technical background to support future safe AI developments.

More precisely, our contributions are as follows:

C1. We identify a number of takeaways that summarize (1) the state of the art of XAI for safe AI from a technical perspective, (2) the underlying regulations and (3) the misalignments between these two areas. Although some papers have addressed these issues [10]–[13], they did not provide with concrete items to work on.

C2. We measure the degree of (lack of) knowledge in the technical and legal communities regarding the identified takeaways. In this regard, we conduct a survey among 31 professionals. Although they mostly belong to the European Union (Spain and France), it also includes citizens from China and Argentina. This leads us to identify misalignments and misconceptions that deserve further training/upskilling, technical or legal characterization.

The remaining of the paper is structured as follows. Section II describes the methodology. Sections III, IV and V show the identified takeaways concerning technical and legal aspects of XAI for safe AI, as well as the misalignments between these two, respectively. Section VI measures the actual misconceptions and misalignments considering real-world professionals. Section VII describes the related work. Lastly, Section VIII concludes the paper and points out future work directions.

## II. METHODOLOGY

The methodology followed in this paper is composed by two main steps, as depicted in Figure 1.

**Identification of technical, legal and misalignments between the two (Figure 1, upper part).**

We conducted a literature review of major academic databases through Google Scholar from 2019 to the present. Some of the most important search terms were: "XAI", "XAI Survey", "Explainable AI", "XAI limitations", "AI transparency", "AI interpretability", "Safe AI", "XAI security threats", "XAI attacks", "safe AI", "Secure explainability". After manual inspection of 117 papers, 39 papers were finally considered as relevant. This review synthesized the current state of XAI research within both legal regulations and technical spheres. These findings exposed a notable gap between technological and regulatory concerns.

To further explore this gap, we searched for articles published from 2020 onward that explicitly address the intersection of these two domains. Using the query terms "XAI" and "GDPR", "XAI" and "AI Act", "XAI" and "legal", "Data protection in XAI", "XAI" and "trade secrets", "XAI legal challenges", "XAI" and ("misalignment" OR "misconception"), "privacy" and "XAI", "regulation" and "XAI", "XAI" and "knowledge gaps". 52 papers were manually reviewed to finally take 13 papers into consideration.

**Measuring real-world misconceptions and misalignments (Figure 1, lower part).** The takeaways collected in the previous phase are useful in describing the status of XAI for safe AI from a theoretical perspective. However, it lacks real-world vision. For this purpose, we developed a targeted survey aimed at gathering insights from technologists and legal experts working with AI. The necessity of this survey became clear as our literature analysis highlighted a critical lack of multidisciplinary understanding and the poor exchange of information between these two key groups. Thus, it helped us identifying important real-world misconceptions and misalignments to further inspire a research roadmap.

## III. XAI FOR SAFE AI. TECHNICAL STATE OF THE ART

This section provides with an overview of existing techniques, to later identify a number of takeaways.

### A. Overview of techniques

XAI aims to provide a layer of transparency to deep learning models. Since these models, including LLMs are extremely complex, providing human-understandable explanations is relevant to build a certain degree of trust in their operation [14].

Various XAI techniques, including feature attribution methods, surrogate models, and counterfactual explanations, have been proposed to improve interpretability [15]. On the one hand, model-agnostic methods, such as SHAP [14] and LIME [15], generate explanations applicable across different AI models. On the other hand, model-specific techniques leverage internal structures, such as attention mechanisms in transformers, to provide insights. As an example, linear probing is a commonly used technique for evaluating the knowledge encoded in the internal representations of a model [16]. It involves training a relatively simple linear classifier, such as a logistic regression model, using features extracted from the model. Among these features, internal activations are a
promising approach. They are the intermediate representations
of data as it passes through the model layers [17].

Despite the interest of these techniques, they currently face Tech5
two main limitations. On one side, currently interpretability
comes at the cost of accuracy. Thus, when models expend a lot
of internal resources in providing explanations, they sacrifice
their predictive power [18]. On the other side, the relative
immaturity of these technologies make them be potentially Tech6
inconsistent. Therefore, explanations from different XAI tech-
niques may not be compatible themselves.

B. Takeaways

Tech1 **SHAP and LIME are the most popular XAI techniques.** SHAP uses game theory to compute the impact of each input feature whereas LIME works by building simple models around individual predictions to show which features are more relevant in each case. Grad-CAM [19]–[24] is frequently used in healthcare applications, Tech7 offering visual explanations of neural network decisions. DeepLIFT [19], [20], [25], [26] provides insights into deep learning models by tracking neuron activations. Counterfactual explanations [11], [12], [18], [19], [25]–[28] are widely applied in legal and financial AI systems to explain decision-making by presenting alternative scenarios. Other popular methods include DALEX [10], [18], [23] and Anchors [18], [23], [29], which enhance Tech8 interpretability in machine learning models.

Tech2 **Lack of XAI benchmarks.** Explainability remains ill-defined due to the absence of universally accepted metrics, evaluation protocols, or agreed-upon benchmarks [10]–[12], [19]–[23], [25]–[28], [30], [31]. Researchers struggle to measure, compare, or even define “interpretability” consistently, hindering reproducibility and objective assessment of different XAI approaches. Multiple metrics are proposed, but their utility is not standardized. Without a concrete evaluation framework explainability remains unevenly implemented and difficult to benchmark across domains and teams.

Tech3 **XAI Increases the attack surface of AI systems** XAI introduces new security vulnerabilities. Explainability methods can be leveraged by adversaries to reverse-engineer AI models, leading to model extraction attacks. Additionally, explanations can reveal biases that attackers may exploit to manipulate model behavior. Studies emphasize the need for robust defense mechanisms to mitigate these risks, but the field lacks standardized security solutions for XAI [11], [20], [24], [26], [28], [31]–[33].

Tech4 **Limits of Interpretable models and challenges in scaling XAI** High-performance AI architectures, especially deep learning, often lack intuitive transparency, and simplifying them for explainability may reduce accuracy. Local explanation methods are not always trustworthy for complex, evolving systems, and scaling these approaches to large datasets or real-time environments is computationally

intensive [11], [18]–[23], [25], [26], [28], [32],
[34], [35].

**Biases impose a challenge to XAI.** Researchers stress that explanations must reveal potential discriminatory factors and enable robust fairness assessments, but standardized bias detection and mitigation techniques remain insufficient [11], [22], [24], [26], [30]–[32], [35].

**XAI understandability.** Effective XAI hinges on producing understandable explanations for non-technical stakeholders, such as clinicians or financial analysts, who risk over-reliance on or mistrust of AI if explanations are opaque or overly complex [10], [11], [20], [25], [32], [34], [35]. Automation bias can emerge when superficial justifications lull users into uncritical acceptance of AI outputs. Conversely, overly detailed technical explanations can deter adoption. Achieving clarity, brevity, and domain relevance is crucial to fostering trust, proper reliance, and informed oversight of AI systems.

**Need for domain-specific XAI.** Close collaboration between domain experts and AI developers is essential to ensure that explanations are meaningful, legally compliant, and technically feasible [11], [12], [29], [35]. Because one-size-fits-all solutions are ineffective, sustained interdisciplinary efforts are necessary to accommodate each field's data characteristics, legal context, and specific interpretability objectives.

**A few domains are attracting most attention concerning XAI.** Healthcare remains the most extensively studied domain, with significant emphasis on improving transparency in medical decision-making and diagnostics [11], [21]–[25], [30], [36], [37]. Other well-explored domains include finance [11], [18], [24], [25], [30], [36], law [12], [25], [27], [30], [35], cybersecurity [11], [26], [31], [33], and IoT [19], [23], along with Edge AI [19] and autonomous systems [11], [19], though to a lesser extent.

IV. LEGAL ISSUES RELATED TO XAI AND SAFE AI

In this section, the impact of XAI in two main AI-related issues are described – the automated decision making leveraging personal data, and personal data protection and trade secrets. Afterwards, a number of takeaways are identified.

It must be noted that while regulations do not typically
opt for the concept of "safe AI", they impose a number of
requirements to meet a minimum level of trustworthiness.

A. XAI and the right not to be subject to a decision based
solely on automated processing under GDPR

In the European Regulatory framework, as set forth in art. 22(1) General Data Protection Regulation (hereinafter, GDPR) “[t]he data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her”.

The recent judgment by the Court of Justice of the European
Union (CJEU) in case C-634/21 [38] sheds light on how
automated processes, such as credit scoring, fall within the scope of Article 22(1) if they significantly affect individuals and if the outcomes heavily influence subsequent decisions by third parties. The ruling underscores the requirement for meaningful human oversight in automated profiling processes: even when a human ostensibly grants final approval, any automated component that decisively shapes the outcome may be captured by the prohibition in art. 22(1) if the purported human review is merely symbolic rather than substantive.

Given the importance of ensuring human oversight is not merely symbolic but genuinely influential, regulatory frameworks stress the need for transparency in its implementation. To this end, the Spanish Data Protection Agency mandates that, as part of the Data Protection Impact Assessment, data controllers must identify and document both the degree of human intervention and the specific point at which such intervention occurs during the decision-making process [39].

Extending these principles, the European Union Artificial Intelligence Act (hereinafter, AI Act) also underscores the importance of human oversight in art. 14(4)(c) AI Act, particularly for high-risk AI systems. It mandates that, where appropriate and proportionate, human operators must be able “to correctly interpret the high-risk AI system’s output, taking into account, for example, the interpretation tools and methods available”. This requirement has propelled XAI into the spotlight, as these techniques offer an additional layer of transparency to “black-box” models [40]. By clarifying the reasoning behind automated decisions—such as how input features are weighted or which logical pathways lead to a given conclusion—XAI establishes a clearer, more comprehensible link between inputs and outputs [41]. In turn, it facilitates the substantive human review that both the GDPR and the emerging AI regulatory framework increasingly demand.

## B. Implications of XAI for data protection, privacy and trade secrets

Art. 5(1)(f) GDPR imposes the duty to process personal data “[…] in a manner that ensures appropriate security of the personal data, including protection against unauthorized or unlawful processing and against accidental loss, destruction or damage, using appropriate technical or organizational measures.” From this provision, it follows that any AI system handling personal data must be equipped with robust safeguards to deter improper access or exposure of sensitive information.

In turn, art. 5(2) GDPR introduces the principle of accountability. It implies adopting security policies as well as demonstrating their effectiveness. These mechanisms (arts 25 and 32 GDPR) must be in line with the state of the art considering the risks at stake.

Consistent with this regulatory approach, in art. 15(5) AI Act it is stipulated that “[h]igh-risk AI systems shall be resilient against attempts by unauthorised third parties to alter their use, outputs or performance by exploiting system vulnerabilities” and that “[t]he technical solutions to address AI specific vulnerabilities shall include, where appropriate, measures to prevent, detect, respond to, resolve and control

for attacks trying to manipulate the training data set (data poisoning), or pre-trained components used in training (model poisoning), inputs designed to cause the AI model to make a mistake (adversarial examples or model evasion), confidentiality attacks or model flaws.”

As analyzed above, XAI systems exhibit notable vulnerabilities when compared to traditional black-box models, since the interpretability that benefits users may likewise facilitate adversarial attacks. For instance, methods such as SHAP, by disclosing critical information about a model’s internal logic, can inadvertently enable attackers to identify key features and decision thresholds, refining their strategies for evading detection in security systems. Similarly, the use of LIME to interpret black-box models expands the attack surface by generating local explanations via surrogate models, thus revealing the original model’s decision boundaries, enabling targeted attacks and gradient estimation. Furthermore, LIME can be exploited to extract proprietary models, compromising intellectual property, or to poison model explanations or manipulate feature importance, thereby distorting security assessments and concealing system vulnerabilities.

Therefore, although XAI aligns with the transparency principle by allowing data subjects to seek explanations of automated decisions, it may also jeopardize the confidentiality requirement of arts. 5(1)(f), 25 and 32 GDPR, as well as the protection requirements imposed by art 15(5) AI Act.

## C. Takeaways

**Law 1 Regulatory conflicts between transparency, data protection and intellectual property and trade secrets.**

While European regulations impose strict data privacy safeguards, they simultaneously demand increasing model explainability. This dual requirement generates friction, particularly in sectors where disclosing AI decision rationales risks inadvertently exposing sensitive data [10]–[12], [22], [26], [27], [29], [32], [35], [42]. The lack of established legal doctrine or jurisprudence further exacerbates this challenge, leaving organizations without clear precedents to navigate these conflicting obligations. Moreover, the transparency mandate threatens enterprises’ ability to safeguard trade secrets and intellectual property—an issue that remains largely overlooked in academic discourse [12], [27], [32].

**Law 2 Lack of international legal frameworks for AI and XAI.** There is no comprehensive global framework governing AI and the expected standards in explainability. The fragmented nature of AI regulations worldwide results in inconsistencies in implementation, making compliance challenging for organizations operating across multiple jurisdictions. Researchers have pointed out that without international consensus on what constitutes explainability and how it should be enforced, companies and developers are left to navigate a complex and often contradictory regulatory landscape [25], [27], [35].

**Law 3 Secure explainability remains elusive under legal constraints.** Balancing transparency with security is complex,
as elucidating AI decisions may introduce vulnerabilities like model inversion attacks, which can compromise user privacy or leak proprietary information. Current XAI methods often fail to satisfy both legal transparency demands and protective measures simultaneously. This predicament is especially pronounced in sensitive domains where compliance with data protection laws and ensuring robust model security converge, making truly “secure explainability” an unsettled goal [12].

## V. IDENTIFYING TECHNICAL-LEGAL MISALIGNMENTS

The literature underscores a profound gap between legal and technical understandings of AI transparency, resulting in regulatory ambiguity, unrealistic expectations [43], [44], and challenges in implementation [35]. Legal frameworks such as the AI Act and GDPR mandate transparency yet fail to delineate its precise scope, leaving AI developers without clear compliance guidelines to operationalize legal mandates, whereas policymakers often underestimate the technical constraints inherent in model architectures [10], [12], [45]. The misalignment between XAI methodologies and legal requirements further complicates certification and oversight, as regulators tend to assume transparency can be achieved through simplified explanations for end-users, whereas researchers emphasize the necessity of advanced interpretability methods [46]. This disciplinary divide stresses the urgency of interdisciplinary collaboration to establish meaningful and practicable transparency standards [13], [27], [47]–[49].

### A. Takeaways

Misal1 **Regulatory vagueness and incomplete guidance on AI transparency** Multiple studies stress that both the GDPR and the AI Act impose transparency obligations yet fail to specify what level of openness is considered “sufficient” or “appropriate” [12], [35], [43], [44]. This lack of explicit criteria intensifies the challenges faced by AI developers, who must reconcile legal mandates with the absence of standardized metrics for algorithmic explainability, all while contending with safety requirements.

Misal2 **Persistent legal-technical mismatch in defining “transparency”** In both legal and technical discourse, “transparency” is conceptualized markedly different—legal experts prioritize accountability and user comprehension, whereas developers concentrate on elucidating how models process inputs [10], [13], [35], [44], [48]. This discrepancy results in divergent objectives: regulators expect thorough yet accessible explanations, while AI practitioners must navigate sophisticated model architectures that defy any simplistic, “one-size-fits-all” approach to explainability.

Misal3 **Unrealistic legislative expectations about XAI capabilities** Several authors argue that policymakers overestimate XAI’s capacity to produce intuitive, user-friendly explanations for every context [10], [44], [45], [47]. By treating XAI as an all-encompassing solution—rather

than a range of context-dependent methods and approaches—lawmakers risk imposing obligations that outstrip current technological limits, thereby complicating implementation efforts and straining developers’ capacity to meet regulatory standards.

## VI. MEASURING MISALIGNMENTS AND MISCONCEPTIONS IN PRACTICE

All previous findings come from the scientific domain. Therefore, it is necessary to confirm the current status of real-world professionals. This section focuses on this matter. First, the structure of a questionnaire distributed to professionals is presented. Afterwards, the profile of respondents is described. Lastly, the identified common grounds between both communities, their misconceptions and misalignments are presented. Figure 2 illustrates some relevant results of the survey.

### A. Survey structure

The online survey can be found in Google Forms<fnref n="1" />. The survey includes a balanced set of technical and legal questions. Technical questions were aimed to assess the baseline knowledge of jurists regarding core AI concepts, ensuring their ability to legislate and guide regulatory practices effectively. Conversely, legal questions were conceived to determine whether technologists have an adequate grasp of the regulatory requirements that are necessary for developing AI systems in compliance with the legal framework. All questions were aligned with the discovered takeaways, as shown in Table I.

<table><caption>TABLE I: Coverage of each takeaway in the survey</caption><thead><tr><th>Questions</th><th>Tech1</th><th>Tech2</th><th>Tech3</th><th>Tech4</th><th>Tech5</th><th>Tech6</th><th>Tech7</th><th>Tech8</th><th>Law1</th><th>Law2</th><th>Law3</th><th>Misal1</th><th>Misal2</th><th>Misal3</th></tr></thead><tbody><tr><td>Q1</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q2</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q3</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Q4</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Q7</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>Q8</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q9</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q10</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q11</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q12</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q13</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q14</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q15</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q16</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>Q17</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>Q18</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Q19</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q20</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q21</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td>✓</td></tr><tr><td>Q22</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Q23</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q24</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td></tr><tr><td>Q25</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>Q26</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Q27</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td><td></td><td>✓</td></tr><tr><td>Q28</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Q29</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Q30</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Q31</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Q32</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr></tbody></table>

The survey included a mix of question types, including true/false and Don’t Know/No Answer and Likert scale questions. The use of Likert scales allowed us to assess the intensity of respondents’ opinions, while true/false and “don’t know” responses provided a clearer indication of misunderstandings.

All survey questions have a correct answer derived from the analyzed state of the art or directly from regulations. In yes/no questions, selecting “no” when the correct answer is “yes” (or vice versa) is considered a misconception. If a significant number of respondents choose “don’t know” in a particular area, we interpret this as a misalignment—if there is

<footnote n="1">¹https://forms.gle/VVKDdBsEMHwb8aZe8, last access May 2025.</footnote>
<figure><img src="image_4.png" /><figcaption>(a) Legal 1</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>(b) Legal 2</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(c) Legal 3</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(d) Tech1</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(e) Tech 2</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(f) Tech 3</figcaption></figure>

<caption>Fig. 2: Excerpt of survey results</caption>

B. Respondents profile

Participants were selected among the network of contacts of the authors, including academic researchers and lawyers. 67% of respondents are identified as technical experts, while about 33% as legal experts. All participants exhibited a high level of specialization in AI development, cybersecurity, AI governance, or technology law, holding roles such as Data Protection Officers, professionals in data protection agencies, LLM/NLP developers, AI cybersecurity professionals or AI/tech law practitioners. In terms of academic credentials, 36% hold a PhD, 45% a master's degree, and 16% a bachelor's degree as their highest qualification.

C. Common ground between technologists and legal professionals

SC1 Lack of universal XAI standards Both groups agree that no universally accepted standards exist for measuring AI explainability (Q1). This lack of a unified benchmark makes it difficult to compare different XAI approaches.

SC2 Uncertainty about scalability and real-time use Participants also share uncertainties regarding whether these explainability methods can effectively scale to large datasets or be applied in real-time scenarios (Q12), highlighting a broader knowledge gap in practical deployment.

SC3 **Incomplete and uncertain legal frameworks** Respondents consistently concur that neither a global nor an EU-level framework definitively outlines AI transparency obligations (Q24). This regulatory patchwork leaves organizations navigating evolving or incomplete guidelines.

D. Identified misconceptions

SMc1 **"Increasing explainability inevitably reduces predictive accuracy"** In Q3, it can be observed that opinions were sharply divided over whether enhancing transparency necessarily compromises model performance. This split suggests lingering uncertainty about the real trade-offs between explainability and accuracy.

SMc2 **"A clear, interpretable explanation implies the AI model is reliable"** As seen in Q14, many legal professionals overestimate models' trustworthiness based solely on its explainability. Technical experts, however, are more cautious, recognizing that transparency alone does not guarantee the model's correctness or robustness.

SMc3 **"Transparency requirements do not conflict with data protection, and current XAI fully ensures security"** This perspective, predominantly held by legal experts, encompasses two misconceptions. First, they perceive no genuine conflict between transparency mandates and data protection laws (Q16), overlooking potential risks in exposing sensitive data or model details. Second, according to Q25 answers, some respondents presume that existing XAI methods already satisfy both security and transparency obligations, thus highlighting the need for greater awareness within the legal sector.
## E. Identified misalignments

**SMa1 Uncertainty over the adequacy of common XAI methods in Deep Learning** A significant number of respondents—both legal and technical—remain unsure whether widely used explainability methods (e.g., SHAP, LIME) truly suffice for deep learning models. For instance, Q2 indicates that 90% of legal experts and 40% of technical experts expressed uncertainty. This reflects a broader lack of clarity about how and to what extent these methods effectively enhance AI transparency.

**SMa2 Regulatory ambiguities in GDPR and the AI Act** There is notable confusion regarding regulations such as the GDPR’s “right to explanation” (Q22) and the EU’s AI Act (Q28). Legal experts do not uniformly agree on how these requirements should be implemented, and technical experts often show limited familiarity with key legal texts or case law (Q27). This gap complicates efforts to reconcile transparency and security obligations, as well as questions around liability for non-compliance. Uncertainty about the AI Act’s specifics, in particular, underscores a deeper misalignment between legal and technical perspectives.

**SMa3 Cross-disciplinary disconnect on defining transparency** Technologists and legal professionals define “transparency” in different ways, as indicated in Q31. Many participants (Q21) also note that legal professionals often lack in-depth technical understanding. Consequently, these differences in viewpoint make it harder to develop shared strategies for AI transparency.

# VII. RELATED WORK

An overview of related literature is given in Table II, showing the coverage of technical and legal challenges, as well as any noted misalignments and misconceptions.

The identification of technical issues surrounding XAI for safe AI has been extensively addressed. Most of these studies focus on widely used explanation methods (e.g. [26], [30]) and their application in a few domains that are attracting most interest (v. gr. [21], [22], [27], [35]), yet devote comparatively less attention to secure explainability and the introduction of new security vulnerabilities (e.g. [24], [31], [33]).

Similarly, certain legal aspects have received substantial research attention. For instance, in [42] the tension between mandated transparency and the opacity of large-scale models is highlighted. In addition, much of the literature featuring legal aspects also addresses misalignments (e.g. [47]–[49]) such as ambiguous regulatory language [35] and divergent definitions of “transparency” across disciplines [44], while offering limited insight into practical strategies that reconcile security requirements with transparency obligations [12]. This underscores a notable deficit in actionable guidance.

Interestingly, none of these previous studies surveyed both technologists and jurists to evaluate real-world knowledge gaps, misconceptions, and cross-domain frictions between technical and legal fields. Consequently, they do not capture the actual level of expertise among practitioners. The present

work fills this gap, thereby providing a more comprehensive perspective on the current state of XAI in practice.

<table><caption>TABLE II: Related work</caption><thead><tr><th>Ref</th><th>Year</th><th>Technical issues</th><th>Legal issues</th><th>Misalignments</th><th>Misconceptions</th></tr></thead><tbody><tr><td>[28]</td><td>2020</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[45]</td><td>2021</td><td>-</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[27]</td><td>2022</td><td>-</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[26]</td><td>2022</td><td>✓</td><td>✓</td><td>-</td><td>-</td></tr><tr><td>[20]</td><td>2022</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[43]</td><td>2022</td><td>-</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[30]</td><td>2023</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[11]</td><td>2023</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[18]</td><td>2023</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[33]</td><td>2023</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[44]</td><td>2023</td><td>-</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[49]</td><td>2023</td><td>-</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[10]</td><td>2024</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[25]</td><td>2024</td><td>✓</td><td>✓</td><td>-</td><td>-</td></tr><tr><td>[19]</td><td>2024</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[34]</td><td>2024</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[32]</td><td>2024</td><td>✓</td><td>✓</td><td>-</td><td>-</td></tr><tr><td>[21]</td><td>2024</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[22]</td><td>2024</td><td>✓</td><td>✓</td><td>-</td><td>-</td></tr><tr><td>[24]</td><td>2024</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[12]</td><td>2024</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[42]</td><td>2024</td><td>-</td><td>✓</td><td>-</td><td>-</td></tr><tr><td>[29]</td><td>2024</td><td>✓</td><td>✓</td><td>-</td><td>-</td></tr><tr><td>[31]</td><td>2024</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[13]</td><td>2024</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[46]</td><td>2024</td><td>✓</td><td>-</td><td>✓</td><td>✓</td></tr><tr><td>[47]</td><td>2024</td><td>-</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[48]</td><td>2024</td><td>-</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>[36]</td><td>2025</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[37]</td><td>2025</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[23]</td><td>2025</td><td>✓</td><td>-</td><td>-</td><td>-</td></tr><tr><td>[35]</td><td>2025</td><td>-</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>OURS</td><td>2025</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

# VIII. CONCLUSION

The growing relevance of AI models in diverse scenarios, including high-risk ones, calls for the adoption of safety measures in their development and operation. However, the notion of safe AI may be dramatically different from the technical and legal perspectives. In the same vein, explainability (XAI) can take different roles and relevance levels to achieve safe AI, depending on the extent, purpose and expectations put into it. This paper has analysed the current status of the technical and legal aspects of XAI for safe AI. Beyond identifying several takeaways, a number of misconceptions and misalignments have been discovered.

As future work, our findings are helpful for a roadmap of further research that should tackle training/upskilling as well as technical and legal coordinated advances. The creation of a methodological framework for such a coordination, together with the standardization of XAI, are also future directions.

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