As generative AI reshapes industries, Johannes Schneider reveals the vital need for clear, accessible explanations to build trust and ensure AI aligns with societal values.
Study: Explainable Generative AI (GenXAI): a survey, conceptualization, and research agenda. Image Credit: Sabeen Zahid / Shutterstock
A review article recently published in the journal Artificial Intelligence Review explored the role of explainable artificial intelligence (XAI) in generative AI (GenAI). The researcher Johannes Schneider of the University of Liechtenstein examined the significant challenges and emerging criteria for explainability, offering a detailed classification of XAI methods tailored to GenAI. Schneider's goal was to fill the gap between technical experts and professionals from other fields, providing a roadmap for future research in this rapidly evolving area.
Background
GenAI represents a significant advancement in AI, shifting from pattern recognition to generating complex outputs for various tasks. It includes technologies that create text, images, audio, three-dimensional (3D) models, and videos from textual prompts.
Powered by advanced models like transformers, variational autoencoders, generative adversarial networks, and diffusion models, GenAI has shown remarkable capabilities in fields such as education, programming, and healthcare. This progress has enabled machines to pass university exams and achieve creative feats once thought impossible.
The growing economic potential of GenAI, coupled with its ability to produce outputs of immense complexity, highlights the need for a better understanding of the transparency of its outputs. However, the rise of GenAI also brings challenges, particularly in making its outputs explainable to build trust and reduce risks.
About the Review
In this paper, Schneider addressed the increasing importance of XAI in the era of GenAI. He argued that as GenAI systems become more complex and widely used, effective explanations become critical. The study aimed to identify the intricate challenges and specific requirements for XAI in GenAI and to provide a structured approach to improving explainability techniques.
The methodology combined a narrative review with taxonomy development, leveraging knowledge from pre-GenAI XAI research while exploring new aspects specific to GenAI. The study started with a technical overview of GenAI, focusing on text and image data to explain its multi-modality. It further delved into the complexity of GenAI systems, highlighting how the vast scale of training data and the multi-step processes involved in generating outputs create unique obstacles for explainability. It discussed GenAI model architectures like transformers and diffusion models and their impact on explainability.
Furthermore, Schneider emphasized the need for explanations that ensure not only verifiability and interactivity but also security and cost efficiency, which are critical as GenAI integrates into high-stakes applications. He also highlighted the importance of understanding GenAI's societal impact and enabling users to adjust complex outputs interactively.
Key Findings
Schneider identified several key outcomes related to XAI challenges and requirements for GenAI. One of the most significant challenges is the opacity surrounding commercial GenAI models, where the lack of access to internals and training data severely limits the effectiveness of many XAI approaches. The complexity of GenAI systems, involving large datasets and complex model architectures, complicates explainability efforts. Additionally, the interactive nature of many GenAI applications requires explanations that account for human-AI interactions. Explanations also need to be accessible to a wide range of users, from school children to corporate employees.
The study also highlighted the importance of output verification, given GenAI's potential to generate misleading or harmful content. In particular, Schneider discusses how explanations can help mitigate issues like AI hallucinations, where the AI generates information that is plausible but incorrect, thereby improving trust in GenAI outputs.
The author proposed a taxonomy for categorizing XAI techniques based on their input, output, and internal properties, distinguishing between intrinsic and extrinsic methods. Schneider noted that training data plays a pivotal role in GenAI's explainability, with the quality and composition of this data crucial in determining how well a model can generate understandable and trustworthy explanations. He discussed the role of training data and prompts in enhancing explainability, noting that the quality and composition of training data significantly impact a model's ability to generate meaningful explanations.
Furthermore, interactive explanations and verifiability were highlighted, particularly for addressing issues like AI hallucinations. The paper also covered the role of self-supervised pre-training, instruction tuning, and alignment tuning in improving performance and ensuring AI outputs align with human values.
Applications
This research has significant implications across various domains where GenAI is employed. In education, explainable GenAI can help educators understand and trust AI-generated content, enhancing its integration into teaching and learning. In healthcare, XAI can improve the reliability of AI-driven diagnostics and treatment recommendations, enabling healthcare professionals to verify and trust the outputs. The paper stresses that in these critical areas, the ability to explain AI decisions is not just a technical requirement but a societal necessity.
For businesses, explainable GenAI can enhance decision-making by providing transparent and verifiable AI-generated insights, fostering greater trust and further adoption of AI technologies. Additionally, XAI can reduce risks like biased or harmful content by allowing users to verify and understand AI outputs.
Conclusion
The review summarized that explainability is crucial in the era of GenAI. It provided an extensive survey of existing XAI techniques, clearly identifying key challenges and proposed a taxonomy to better understand and categorize XAI methods for GenAI. Schneider emphasized the importance of verifiability, interactivity, and security in explanations, calling for continued efforts to improve XAI techniques.
Future work should focus on developing more sophisticated XAI methods that can address the growing complexity of GenAI systems, and integrating XAI into various GenAI applications. Overall, this work represents a significant step toward ensuring that GenAI technologies are transparent, trustworthy, and aligned with human values.