Artificial Intelligence (AI), particularly through the advent of large-scale generative AI (GenAI) models such as Large Language Models (LLMs), has become a transformative element in contemporary technology. While these models have unlocked new possibilities, they simultaneously present significant challenges, such as concerns over data privacy and the propensity to generate misleading or fabricated content. Current frameworks for Responsible AI (RAI) often fall short in providing the granular guidance necessary for tangible application, especially for Accountability---a principle that is pivotal for ensuring transparent and auditable decision-making, bolstering public trust, and meeting increasing regulatory expectations. This study bridges the Accountability gap by introducing our effort towards a comprehensive metrics catalogue, formulated through a systematic multivocal literature review (MLR) that integrates findings from both academic and grey literature. Our catalogue delineates process metrics that underpin procedural integrity, resource metrics that provide necessary tools and frameworks, and product metrics that reflect the outputs of AI systems. This tripartite framework is designed to operationalize Accountability in AI, with a special emphasis on addressing the intricacies of GenAI.

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.

The increase of software supply chain threats has underscored the necessity for robust security mechanisms, among which the Software Bill of Materials (SBOM) stands out as a promising solution. SBOMs, by providing a machine-readable inventory of software composition details, play a crucial role in enhancing transparency and traceability within software supply chains. This empirical study delves into the practical challenges and solutions associated with the adoption of SBOMs through an analysis of 4,786 GitHub discussions across 510 SBOM-related projects. Through repository mining and analysis, this research delineates key topics, challenges, and solutions intrinsic to the effective utilization of SBOMs. Furthermore, we shed light on commonly used tools and frameworks for SBOM generation, exploring their respective strengths and limitations. This study underscores a set of findings, for example, there are four phases of the SBOM life cycle, and each phase has a set of SBOM development activities and issues; in addition, this study emphasizes the role SBOM play in ensuring resilient software development practices and the imperative of their widespread adoption and integration to bolster supply chain security. The insights of our study provide vital input for future work and practical advancements in this topic.

The rapid development of artificial intelligence (AI) has led to increasing concerns about the capability of AI systems to make decisions and behave responsibly. Responsible AI (RAI) refers to the development and use of AI systems that benefit humans, society, and the environment while minimising the risk of negative consequences. To ensure responsible AI, the risks associated with AI systems' development and use must be identified, assessed and mitigated. Various AI risk assessment frameworks have been released recently by governments, organisations, and companies. However, it can be challenging for AI stakeholders to have a clear picture of the available frameworks and determine the most suitable ones for a specific context. Additionally, there is a need to identify areas that require further research or development of new frameworks, as well as updating and maintaining existing ones. To fill the gap, we present a mapping study of 16 existing AI risk assessment frameworks from the industry, governments, and non-government organizations (NGOs). We identify key characteristics of each framework and analyse them in terms of RAI principles, stakeholders, system lifecycle stages, geographical locations, targeted domains, and assessment methods. Our study provides a comprehensive analysis of the current state of the frameworks and highlights areas of convergence and divergence among them. We also identify the deficiencies in existing frameworks and outlines the essential characteristics of a concrete and connected framework AI risk assessment (C2AIRA) framework. Our findings and insights can help relevant stakeholders choose suitable AI risk assessment frameworks and guide the design of future frameworks towards concreteness and connectedness.

The rapid growth of software supply chain attacks has attracted considerable attention to software bill of materials (SBOM). SBOMs are a crucial building block to ensure the transparency of software supply chains that helps improve software supply chain security. Although there are significant efforts from academia and industry to facilitate SBOM development, it is still unclear how practitioners perceive SBOMs and what are the challenges of adopting SBOMs in practice. Furthermore, existing SBOM-related studies tend to be ad-hoc and lack software engineering focuses. To bridge this gap, we conducted the first empirical study to interview and survey SBOM practitioners. We applied a mixed qualitative and quantitative method for gathering data from 17 interviewees and 65 survey respondents from 15 countries across five continents to understand how practitioners perceive the SBOM field. We summarized 26 statements and grouped them into three topics on SBOM's states of practice. Based on the study results, we derived a goal model and highlighted future directions where practitioners can put in their effort.

Games tend to have the properties of vast state space and high complexity, making them excellent benchmarks for evaluating various techniques, including AI ones. Techniques utilized in games capable of making them more attractive, immersive, smarter etc. can all be considered to be certain forms of game AI. Considering there are few reviews on the more recent work in the game AI field from the perspective of essential applications, in this paper, we make a systematic review of typical research from 2018 on three application fields of game AI: believable agents in non-player characters research, game level generation in procedural content generation, and player profiling in player modeling. We also provide a timeline of game AI history to give the readers a clearer picture of the game AI field. Moreover, general game AI and hybrid intelligence for games are discussed.