An Evaluation-Driven Approach to Designing LLM Agents: Process and Architecture

The advent of Large Language Models (LLMs) has enabled the development of LLM agents capable of autonomously achieving under-specified goals and continuously evolving through post-deployment improvement, sometimes without requiring code or model updates. Conventional approaches, such as pre-defined test cases and code/model redevelopment pipelines, are inadequate for addressing the unique challenges of LLM agent development, particularly in terms of quality and risk control. This paper introduces an evaluation-driven design approach, inspired by test-driven development, to address these challenges. Through a multivocal literature review (MLR), we synthesize existing LLM evaluation methods and propose a novel process model and reference architecture specifically designed for LLM agents. The proposed approach integrates online and offline evaluations to support adaptive runtime adjustments and systematic offline redevelopment, improving runtime pipelines, artifacts, system architecture, and LLMs by continuously incorporating evaluation results, including fine-grained feedback from human and AI evaluators.

Blockchain-Enabled Accountability in Data Supply Chain: A Data Bill of Materials Approach

In the era of advanced artificial intelligence, highlighted by large-scale generative models like GPT-4, ensuring the traceability, verifiability, and reproducibility of datasets throughout their lifecycle is paramount for research institutions and technology companies. These organisations increasingly rely on vast corpora to train and fine-tune advanced AI models, resulting in intricate data supply chains that demand effective data governance mechanisms. In addition, the challenge intensifies as diverse stakeholders may use assorted tools, often without adequate measures to ensure the accountability of data and the reliability of outcomes. In this study, we adapt the concept of ``Software Bill of Materials" into the field of data governance and management to address the above challenges, and introduce ``Data Bill of Materials" (DataBOM) to capture the dependency relationship between different datasets and stakeholders by storing specific metadata. We demonstrate a platform architecture for providing blockchain-based DataBOM services, present the interaction protocol for stakeholders, and discuss the minimal requirements for DataBOM metadata. The proposed solution is evaluated in terms of feasibility and performance via case study and quantitative analysis respectively.

Integrating ESG and AI: A Comprehensive Responsible AI Assessment Framework

Artificial Intelligence (AI) is a widely developed and adopted technology across entire industry sectors. Integrating environmental, social, and governance (ESG) considerations with AI investments is crucial for ensuring ethical and sustainable technological advancement. Particularly from an investor perspective, this integration not only mitigates risks but also enhances long-term value creation by aligning AI initiatives with broader societal goals. Yet, this area has been less explored in both academia and industry. To bridge the gap, we introduce a novel ESG-AI framework, which is developed based on insights from engagements with 28 companies and comprises three key components. The framework provides a structured approach to this integration, developed in collaboration with industry practitioners. The ESG-AI framework provides an overview of the environmental and social impacts of AI applications, helping users such as investors assess the materiality of AI use. Moreover, it enables investors to evaluate a company's commitment to responsible AI through structured engagements and thorough assessment of specific risk areas. We have publicly released the framework and toolkit in April 2024, which has received significant attention and positive feedback from the investment community. This paper details each component of the framework, demonstrating its applicability in real-world contexts and its potential to guide ethical AI investments.

Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessment

The rapid growth of Artificial Intelligence (AI) has underscored the urgent need for responsible AI practices. Despite increasing interest, a comprehensive AI risk assessment toolkit remains lacking. This study introduces our Responsible AI (RAI) Question Bank, a comprehensive framework and tool designed to support diverse AI initiatives. By integrating AI ethics principles such as fairness, transparency, and accountability into a structured question format, the RAI Question Bank aids in identifying potential risks, aligning with emerging regulations like the EU AI Act, and enhancing overall AI governance. A key benefit of the RAI Question Bank is its systematic approach to linking lower-level risk questions to higher-level ones and related themes, preventing siloed assessments and ensuring a cohesive evaluation process. Case studies illustrate the practical application of the RAI Question Bank in assessing AI projects, from evaluating risk factors to informing decision-making processes. The study also demonstrates how the RAI Question Bank can be used to ensure compliance with standards, mitigate risks, and promote the development of trustworthy AI systems. This work advances RAI by providing organizations with a valuable tool to navigate the complexities of ethical AI development and deployment while ensuring comprehensive risk management.

Navigating Privacy and Copyright Challenges Across the Data Lifecycle of Generative AI

The advent of Generative AI has marked a significant milestone in artificial intelligence, demonstrating remarkable capabilities in generating realistic images, texts, and data patterns. However, these advancements come with heightened concerns over data privacy and copyright infringement, primarily due to the reliance on vast datasets for model training. Traditional approaches like differential privacy, machine unlearning, and data poisoning only offer fragmented solutions to these complex issues. Our paper delves into the multifaceted challenges of privacy and copyright protection within the data lifecycle. We advocate for integrated approaches that combines technical innovation with ethical foresight, holistically addressing these concerns by investigating and devising solutions that are informed by the lifecycle perspective. This work aims to catalyze a broader discussion and inspire concerted efforts towards data privacy and copyright integrity in Generative AI.