Establishing and Evaluating Trustworthy AI: Overview and Research Challenges (2411.09973v1)

Abstract: AI technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered trustworthy. In this paper, we synthesize existing conceptualizations of trustworthy AI along six requirements: 1) human agency and oversight, 2) fairness and non-discrimination, 3) transparency and explainability, 4) robustness and accuracy, 5) privacy and security, and 6) accountability. For each one, we provide a definition, describe how it can be established and evaluated, and discuss requirement-specific research challenges. Finally, we conclude this analysis by identifying overarching research challenges across the requirements with respect to 1) interdisciplinary research, 2) conceptual clarity, 3) context-dependency, 4) dynamics in evolving systems, and 5) investigations in real-world contexts. Thus, this paper synthesizes and consolidates a wide-ranging and active discussion currently taking place in various academic sub-communities and public forums. It aims to serve as a reference for a broad audience and as a basis for future research directions.

• The paper presents six core requirements for trustworthy AI and details practical evaluation methodologies for each.
• It highlights challenges in balancing transparency, fairness, and robustness while ensuring human oversight and accountability.
• The research advocates for interdisciplinary approaches to develop adaptive frameworks for ethical and reliable AI systems.

Establishing and Evaluating Trustworthy AI: Overview and Research Challenges

The paper "Establishing and Evaluating Trustworthy AI: Overview and Research Challenges" delves deeply into the burgeoning discourse surrounding the criteria necessary for an AI system to be deemed trustworthy. Presented by a consortium of researchers from various institutions based in Graz, Austria, the paper emphasizes the increasing imperative for clear standards given AI's pervasive application across sectors and its potential for unwelcome consequences.

The authors articulate six fundamental requirements for trustworthy AI systems: human agency and oversight, fairness and non-discrimination, transparency and explainability, robustness and accuracy, privacy and security, and accountability. Each requirement is extensively explored in terms of definition, establishment techniques, and evaluation methodologies. The paper provides a multi-dimensional view of the landscape of trustworthy AI, emphasizing various research challenges and gaps that warrant further inquiry.

Six Requirements for Trustworthy AI:

1. Human Agency and Oversight: The principle supports empowering users and stakeholders, ensuring AI systems remain under equitable human control and guidance. Evaluating human agency presents considerable challenges, requiring assessments of users' AI literacy and understanding of the systems' workings to foster autonomy. The authors highlight the necessity of designing systems that cater to human oversight through clear interaction models.
2. Fairness and Non-Discrimination: With AI systems increasingly influencing societal functions, the authors stress the need for algorithms to be devoid of biases that could disadvantage any group unfairly. The challenges are underscored by the difficulty of defining universal fairness due to its context-dependent nature, as well as the technical difficulties arising from combining AI components with disparate fairness objectives.
3. Transparency and Explainability: Vital for engendering trust and allowing experts to validate AI functionality, transparency, and explainability require AI systems to be understandable and their decisions interpretable. The paper outlines a continuum of explanation methods, from global to local, highlighting the need for suitable explanation models that align with stakeholder expectations.
4. Robustness and Accuracy: These two interlinked qualities ensure that AI systems deliver correct results, even under adverse or unforeseen circumstances. While robustness refers to consistency across varying conditions, accuracy pertains to the correctness of results under standard conditions. The paper points out the lack of a unified robustness assessment and the trade-off intricacies between robustness and model accuracy.
5. Privacy and Security: Given the sensitive nature of the data AI systems operate on, protecting both data and AI models from unauthorized access and manipulation is critical. The paper reviews recent advancements in privacy-enhancing technologies (PETs) and suggests a multi-pronged defense strategy to combat various adversarial attacks. The integration of PETs presents its challenges, predominantly concerning a trade-off between computational demands and maintaining performance.
6. Accountability: The concept of accountability in AI covers the responsibility for AI outcomes and ensuring consequences for failures. Evaluative frameworks for accountability are contextual and demand comprehensive consideration of social, technical, and legal dimensions. Various frameworks are surveyed, but the paper notes the difficulty in universally applicable standards due to the broad nature of accountability.

Practical and Theoretical Implications:

The paper identifies numerous overarching challenges, such as the need for interdisciplinary research combining insights from technical, social, and policy perspectives, contextual dependencies in defining and applying trustworthiness criteria, and issues stemming from evolving AI systems that adapt post-deployment. Scholars assert that addressing these challenges will require new methodological frameworks that adapt to dynamic real-world contexts, especially in high-risk domains.

Future Developments:

The paper envisions the need for developing adaptive evaluation frameworks that can cope with the shifting dynamics of AI systems. Potential adaptations in methodologies are discussed, especially in relation to rapidly evolving areas like generative AI and LLMs, which introduce novel challenges to existing paradigms of trust, fairness, and accountability.

In conclusion, the researchers aim to synthesize a broad discussion across academic fields to propose a cohesive framework for addressing the multilayered facets of trustworthy AI. They emphasize the critical need for ongoing research that continuously integrates ethical considerations with technical advancement, thereby informing a rigorous AI governance framework. This paper establishes a foundation for future research directions and regulatory measures to ensure the robust and ethical advancement of AI technologies.