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Towards Responsible Generative AI: A Reference Architecture for Designing Foundation Model based Agents
Foundation models, such as large language models (LLMs), have been widely recognised as transformative AI technologies due to their capabilities to understand and generate content, including plans with reasoning capabilities.
Exploring Qualitative Research Using LLMs
We compared the results with human classification and reasoning.
A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture
The recent release of large language model (LLM) based chatbots, such as ChatGPT, has attracted huge interest in foundation models.
Towards Responsible AI in the Era of Generative AI: A Reference Architecture for Designing Foundation Model based Systems
The release of ChatGPT has drawn huge interests on foundations models.
Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach
In this article, we first summarise the major challenges in operationalising responsible AI at scale and introduce how we use the Responsible AI Pattern Catalogue to address those challenges.
Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering
Rather than staying at the principle or algorithm level, we focus on patterns that AI system stakeholders can undertake in practice to ensure that the developed AI systems are responsible throughout the entire governance and engineering lifecycle.
Towards a Roadmap on Software Engineering for Responsible AI
Although AI is transforming the world, there are serious concerns about its ability to behave and make decisions responsibly.
Responsible-AI-by-Design: a Pattern Collection for Designing Responsible AI Systems
In the meantime much effort has been put into responsible AI from the algorithm perspective, but they are limited to a small subset of ethical principles amenable to mathematical analysis.
AI Ethics Principles in Practice: Perspectives of Designers and Developers
As consensus across the various published AI ethics principles is approached, a gap remains between high-level principles and practical techniques that can be readily adopted to design and develop responsible AI systems.
Software Engineering for Responsible AI: An Empirical Study and Operationalised Patterns
These patterns provide concrete, operationalised guidance that facilitate the development of responsible AI systems.
AI and Ethics -- Operationalising Responsible AI
In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences.
How Can Human Values Be Addressed in Agile Methods? A Case Study on SAFe
We elaborate how some current Agile artefacts (e. g., user story), roles (e. g., product owner), ceremonies (e. g., stand-up meeting), and practices (e. g., business-facing testing) in SAFe can be modified to support the inclusion of human values in software.
Software Engineering
