AI Literacy: A Critical and Necessary Competency in the Judiciary and Public Sector Administration

There is a hype to explore and use AI applications in the legal domain, in particular in the judiciary and public sector administration. With that comes the question what that responsible use of AI tools in practise entail.

Art. 4. (AI literacy) of the EU AI Act states:

Providers and deployers of AI systems shall take measures to ensure, to their best extent, a sufficient level of AI literacy of their staff and other persons dealing with the operation and use of AI systems on their behalf, taking into account their technical knowledge, experience, education and training and the context the AI systems are to be used in, and considering the persons or groups of persons on whom the AI systems are to be used.

So, but now what does that mean in practise and what is generally meant by AI literacy? This is what we want to focus on in this edition of the Legal Informatics Newsletter.

Introduction: AI Literacy as a Responsibility in the Digital Age

As artificial intelligence (AI) increasingly shapes our digital landscape, the concept of AI literacy is gaining recognition as a critical responsibility. Clearly that is of particular importance in the legal domain where explainability and transparency are highest objective.

AI literacy is about understanding the workings and effects of AI applications.  AI literacy goes beyond simply knowing how to use a tool—it requires an understanding of the principles, algorithms, and potential biases that underpin these systems.

In sectors where fairness, accountability, and transparency are paramount, professionals must be equipped not only to use AI tools but to comprehend their inner workings and limitations. This requires proactive education and training before the deployment of AI. Moreover, as the judiciary and public sector increasingly rely on AI for critical tasks, the ethical obligation extends to ensuring all professionals share a uniform understanding of the technology, its implications, and its proper usage.

AI literacy involves educating users to recognize how AI operates and the contexts in which it can and should be applied. With the increasing digitization of workflows and the rise of AI-enhanced decision-making tools, this uniformity is not only a legal requirement under the EU AI Act but generally essential to uphold public trust and maintain the integrity of legal processes.

For users in the legal domain, it is important to understand why AI literacy is essential, how it can be achieved and upheld, and why it will become a key issue as technology and workflows continue to evolve. Taxonomies and ontologies will play a key role in establishing a uniform understanding, fostering explainability, and ensuring that professionals in legal contexts can maintain transparency and accountability.

Defining AI Literacy: More than Just Knowing How to Use AI

AI literacy goes beyond merely knowing how to operate AI tools—it encompasses understanding how these systems function, their design principles, their limitations, and the ethical considerations that accompany their deployment. This broader conception of AI literacy is especially critical in sectors such as the judiciary and public administration, where decisions have significant social and legal implications.

What is AI Literacy?

1. Definition of AI Literacy and Its Importance

AI literacy can be described as the set of competencies required to effectively engage with AI technologies in a responsible and informed manner. This includes not only technical skills but also critical thinking abilities to assess the inputs and outputs of AI systems, an understanding of their limitations, and an awareness of their potential biases and ethical challenges.

In the legal domain, AI literacy must empower professionals to critically evaluate AI-driven insights, recognize when and how AI should be used, and ensure that outputs align with ethical and legal standards. This means that AI literacy isn’t just about operational proficiency but also about achieving a deep understanding of the underlying technology and the implications of its use.

Recent literature suggests that AI literacy should be structured in a multi-level competency model to ensure comprehensive understanding and practical application.

2. Levels of AI Literacy

AI literacy involves equipping individuals with the ability to critically engage with AI systems, understand their functions, recognize their strengths and weaknesses, and navigate ethical considerations. In the legal domain, this is particularly important because AI-driven decisions can have profound consequences for justice, rights, and public trust. AI literacy emphasizes both the technical aspects and the societal impact of AI, aligning closely with the legal profession’s values of fairness and accountability.

AI literacy can be understood as progressing through three primary levels, each building on the competencies of the previous one:

Foundational Level:

1. Basic Knowledge: Understanding what AI is, fundamental concepts such as machine learning and natural language processing, and basic applications in legal contexts.
2. Core Competencies: Ability to recognize when AI is being used and appreciate its basic capabilities and limitations. At this level, the focus is on increasing awareness and addressing common misconceptions about AI.

Intermediate Level:

1. Contextual Proficiency: Developing a deeper understanding of AI tools and their integration into legal workflows. This includes awareness of regulatory frameworks, like the EU AI Act, and ethical concerns related to biases and fairness.
2. Core Competencies: Professionals at this level should be able to evaluate and interpret AI outputs, critically assess their accuracy, and identify potential biases or ethical issues in their application.

Advanced Level:

1. Technical and Critical Expertise: Gaining comprehensive knowledge of AI system architecture, decision-making processes, and advanced data analytics. Professionals in this category should understand the intricacies of specific AI applications and their implications for legal decision-making.
2. Core Competencies: Implementing AI-driven systems, leading projects that integrate AI into legal workflows, and maintaining human oversight and accountability.

3. Pathways for Training and Moving Between Levels

Training programs for AI literacy should facilitate a progressive learning experience that allows individuals to move from one level to the next. Research suggests two key approaches to achieving this progression: process and praxis. The process approach focuses on a dynamic, adaptive curriculum where teachers and learners collaborate to meet evolving needs, while the praxis approach emphasizes applying learning to real-world problems. For the legal domain, this could be implemented as:

• From Foundational to Intermediate: Introductory Modules: Provide foundational courses on AI principles, emphasizing awareness of ethical issues and basic technical knowledge. Hands-On Learning: Encourage the use of common legal AI tools for tasks like legal research and document analysis, paired with workshops on interpreting AI-generated results.
• From Intermediate to Advanced: Advanced Technical Training: Offer specialized courses focusing on complex AI applications in legal practices, such as predictive analytics, decision support tools, and regulatory compliance. Practical Case Studies: Use scenario-based learning to explore the ethical implications and practical challenges of AI in legal cases.

4. Recommended Levels for Legal Professionals

For most legal professionals, reaching an intermediate level of AI literacy should be the minimum goal. This level equips them to critically assess AI outputs, ensure compliance with ethical standards, and maintain transparency in decision-making. However, for senior professionals, such as judges, policymakers, or legal project leads, achieving an advanced level is recommended to effectively design, supervise, and audit AI systems in high-stakes contexts.

The Importance of Preemptive Education

A key aspect of AI literacy is ensuring that training and education occur before the deployment of AI tools. It is crucial for professionals in the judiciary and public sector to be educated not only in using AI tools but also in understanding their impact. Proactive training can help create an informed workforce capable of navigating and addressing challenges such as bias, data privacy, and algorithmic transparency.

This preemptive approach aligns with existing best practices in digital literacy, where the focus is on preparing individuals to critically engage with technology. For the judiciary, this entails understanding how AI systems have been trained, interpret and process information, what relevance particular input has, the way they produce outcomes, how such systems might be manipulated and recognizing potential areas where these systems could introduce risks, biases, or inaccuracies.

Leveraging Taxonomies and Ontologies for Uniform Understanding

An essential component of AI literacy is creating a common language and a shared understanding of key terms, concepts, and definitions. This is particularly important in legal contexts, where ambiguity can lead to misinterpretation and unequal application of the law. This is where taxonomies and ontologies come into play.

Taxonomies provide a hierarchical structure for organizing and categorizing information, while ontologies establish relationships between different concepts. By employing taxonomies and ontologies, organizations can streamline terminology and definitions, ensuring that professionals across various roles and jurisdictions are on the same page. This structured approach is vital in fostering explainability and transparency in AI applications, especially in legal and administrative settings where precise language and clarity are crucial.

Ultimately, AI literacy is not just about training individuals to use AI but educating them to understand the principles and mechanics behind AI tools. This deeper understanding lays the foundation for responsible use, fostering trust and accountability as digital workflows and AI applications continue to evolve.

How to Achieve and Uphold AI Literacy

Achieving and maintaining AI literacy is not a one-time endeavor but an ongoing process that involves structured training programs, continuous learning, and the integration of industry standards.

1. Establish Structured Training Programs

To ensure a foundational understanding of AI, organizations must implement structured training programs that cover the basics of AI, including its design principles, potential biases, and areas of application. Such programs should be tailored to specific roles within the judiciary and public administration to help professionals understand how AI fits within their domain.

Professionals in the legal domain need to be also are aware of the regulatory obligations related to (in particular high-risk) AI systems, including the need for documentation, explainability, and human oversight.

2. Continuous Learning and Development

AI literacy is not static. As technology evolves, so do the methods and tools used in legal and public administration. Thus, organizations should establish mechanisms for continuous learning and development, ensuring professionals stay up-to-date with the latest AI advancements, regulations, and ethical considerations. This can be achieved by organizing regular workshops, peer-to-peer learning and making available best practise examples to demonstrate real-world implications of AI-driven decisions and develop critical evaluation skills.

3. Role of Taxonomies and Ontologies

As mentioned above Taxonomies and Ontologies are a key component for (in oarticular) legal AI literacy.

Taxonomies and ontologies offer a solution by:

• Providing a uniform vocabulary for discussing AI concepts and applications, reducing the risk of misinterpretation or ambiguity.
• Facilitating explainability by creating structured frameworks that clarify relationships between different legal concepts and AI applications. This uniformity helps all stakeholders, from administrators to judges, operate with a shared understanding of AI’s role and implications.

Ideally such taxonomies and ontologies should be centrally introduced to and generally accepted in  the legal domain in order to achieve a broad common understanding and supporting a high level of transparency and explainability for the citizens affected by the usage of AI tools in the judiciary and public sector administration.

The EU Environment for AI Literacy

In the European Union, the legal and regulatory landscape surrounding AI emphasizes transparency, accountability, and safety. This environment serves as a foundation for promoting AI literacy, particularly in high-risk sectors like the judiciary and automated decision making in the public sector administration.

1. EU AI Act and High-Risk AI Applications

The EU AI Act has become a cornerstone of the European regulatory framework for AI. It categorizes AI systems based on their potential risk, with high-risk AI applications being subject to the most stringent requirements. This categorization includes AI systems used in the judiciary and automated decision making in public administration, where the consequences of algorithmic decisions can have significant implications for individuals and society.

Under the AI Act, high-risk AI systems are required to meet specific criteria related to safety, transparency, and accountability. These requirements include:

• Ensuring explainability and documentation of AI systems so that outcomes can be understood by human users and stakeholders.
• Implementing robust human oversight mechanisms to prevent and mitigate risks associated with automated decision-making.
• Adhering to harmonized standards to demonstrate compliance with the Act’s guidelines, thereby ensuring that AI systems operate fairly and without introducing biases.

2. The Role of Uniform Terminologies

A major aspect of AI literacy is the importance of having uniform terminologies. In regulatory frameworks, ambiguity in definitions and concepts can lead to misinterpretation and unequal application of AI guidelines. Establishing clear and consistent language is crucial for meeting regulatory standards and achieving transparency.

To this end, taxonomies and ontologies play an essential role. They help to

• develop standardized definitions and relationships for legal concepts and AI applications, which are crucial in sectors where precision and clarity are vital.
• ensure that professionals share a common understanding of AI-related terms, reducing the likelihood of discrepancies or misunderstandings in the interpretation of regulations.

3. Ethical Guidelines for Trustworthy AI

In addition to the AI Act, the European Commission’s High-Level Expert Group on AI has published the Ethical Guidelines for Trustworthy AI, which serve as a voluntary but influential framework. These guidelines outline key requirements for the deployment of AI systems, including human agency and oversight, technical robustness, and privacy and data governance. Professionals in the judiciary and public sector should integrate these principles into their training and AI literacy programs.

Why AI Literacy Will Become a Key Issue in the Future: The Call for AI Literacy in Legal Education

As AI technologies continue to advance, the need for comprehensive AI literacy will intensify, particularly in sectors like the judiciary and public administration. However, achieving this goal requires a proactive approach, beginning with integrating AI literacy into legal education. By equipping future legal professionals with the skills to navigate these new technologies, we can ensure that they are prepared for the complexities of tomorrow’s digital landscape.

1. Evolving AI Technologies and Increased Complexity

AI technologies are rapidly evolving, moving beyond basic automation to encompass predictive analytics, decision support, and cognitive automation. Legal professionals in the future will be increasingly reliant on advanced tools like machine learning algorithms and natural language processing systems to streamline workflows and make data-driven decisions. With this evolution comes the need for a deeper understanding of the underlying systems.

To meet these needs, AI literacy must be a mandatory core component of legal education. Law schools and legal training programs need to integrate AI-related modules to teach students not only the technology but also its intersection with law, ethics, and decision-making processes.

2. Integration of Digital Workflows and AI Tools

With the rapid digitalization of legal processes and public sector operations, understanding how AI interacts with digital workflows is increasingly essential. As AI tools are becoming embedded in legal practice, legal professionals must be prepared to:

• ensure interoperability across digital systems, enhancing the efficiency of AI-enhanced workflows.
• foster consistency in data interpretation and terminology, which is essential for collaboration across departments and legal contexts.

Legal education should address this by offering practical training on how digital workflows integrate with AI, ensuring that future professionals are well-prepared to navigate complex, tech-driven legal environments.

3. The Importance of Explainability and Transparency

In sectors like the judiciary, where decisions have significant societal impact, explainability and transparency are critical. AI literacy must enable legal professionals to:

• understand how AI models generate outcomes and how decisions are influenced by the data and algorithms used.
• communicate these AI supported decisions clearly and effectively to parties, clients, stakeholders, and the public, preserving trust and accountability.

4. Structured Knowledge Systems for Consistency

The integration of taxonomies and ontologies into AI literacy training can help legal professionals achieve a consistent and shared understanding of AI-related terminology. These structured knowledge systems are essential for ensuring that:

• AI-driven legal processes adhere to uniform standards, reducing ambiguity and misinterpretation.
• Legal professionals have a shared understanding of the relationships between legal concepts and AI applications, fostering greater clarity and collaboration across sectors.

5. Call to Include AI Literacy in Legal Education

Given the rapidly advancing role of AI in law and public administration, it is essential that AI literacy becomes a fundamental part of legal education. Legal education must prepare students not only to practice law but to interact responsibly with AI technologies that are reshaping legal workflows.

Outlook: The Future of AI Literacy in the Legal Domain

The legal field is in the process of a significant transformation driven by advancements in AI. As AI tools become more sophisticated, their applications in case management, decision support, legal research in the judiciary and public sector administration will expand. This trend underscores the importance of AI literacy, which equips professionals not only to leverage these tools effectively but also to critically evaluate and ethically govern their use.

Looking ahead, we can expect increasing complexity in AI applications, alongside a demand for explainable AI and compliance with emerging regulations, such as the EU AI Act. This will even more require professionals to maintain a high level of AI literacy, supported by continuous education and adherence to established standards. Additionally, the integration of digital workflows and automated processes will necessitate uniform terminologies and structured knowledge systems to promote transparency and collaboration across legal and administrative sectors.

Call to Action: Prioritizing AI Literacy in Legal Education and Practice

The responsibility for achieving AI literacy lies not only with individuals but also with institutions, educators, and policymakers. Here are key steps that stakeholders should take:

1. Integrate AI Literacy into Legal Education: Law schools and training programs should incorporate AI-focused curricula, offering both foundational knowledge and advanced, scenario-based learning. This will ensure that future legal professionals are prepared to engage with AI tools in a meaningful and ethical way.
2. Establish Continuous Learning Pathways: Legal institutions and public administrations should provide ongoing training for professionals at all levels, ensuring they stay updated on the latest AI technologies, regulations, and best practices. This training should include technical aspects, ethical considerations, and practical applications of AI in legal contexts.
3. Adopt Standards and Best Practices: Institutions should adhere to harmonized standards to demonstrate compliance and maintain accountability. This includes promoting the use of taxonomies and ontologies to streamline terminology and ensure a consistent understanding of AI applications.
4. Promote Collaboration Across Disciplines: Collaboration between legal professionals, technologists, data scientists, linguists and ethicists is crucial for navigating the challenges of AI deployment. Legal institutions should encourage cross-disciplinary dialogue to address issues such as algorithmic bias, transparency, and data protection.

Conclusion: Building a Responsible AI-Driven Future

The rapid integration of AI in the legal domain presents both opportunities and challenges. By prioritizing AI literacy, professionals can harness the potential of AI while upholding the values of fairness, accountability, and transparency. However, achieving AI literacy requires a concerted effort from educators, policymakers, and legal institutions to establish clear training pathways, adhere to ethical guidelines, and promote a culture of continuous learning.

The path forward involves more than just mastering technology—it requires developing a critical mindset and a shared understanding of AI’s implications for society and justice. By embedding AI literacy into legal education and practice, the legal profession can confidently navigate the complexities of the future and maintain its commitment to upholding the rule of law in an AI-driven world.

So let us embrace AI literacy today to shape a more transparent, fair, and accountable tomorrow. We can achieve this with a commitment to building a legal workforce that is not only technically proficient but also ethically grounded, ready to meet the challenges and opportunities of AI with integrity and purpose.