Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible Artificial Intelligence Engineering Approach

The recent release of ChatGPT has gained huge attention and discussion worldwide, with responsible artificial intelligence (RAI) being a crucial topic of discussion. One key question is, “How can we ensure that AI systems, like ChatGPT, are developed and adopted in a responsible way? ” To tackle RAI challenges, various ethical principles have been released by governments, organizations, and companies. However, those principles are very abstract and not practical enough. Further, significant efforts have been put on algorithm-level solutions that only address a narrow set of principles, such as fairness and privacy. To fill the gap, we adopt a pattern-oriented RAI engineering approach and build an RAI pattern catalog to operationalize RAI from a system perspective. In this article, we first summarize the major challenges in operationalizing RAI at scale and introduce how we use the RAI pattern catalog to address those challenges. We then examine the risks at each stage of the chatbot development process and recommend pattern-driven mitigations to evaluate the usefulness of the RAI pattern catalog in a real-world setting.

Authors

• Qinghua Lu Data61, CSIRO
• Yuxiu Luo
• Liming Zhu
• Mingjian Tang
• Xiwei Xu CSIRO
• Jon Whittle

Keywords

• Artificial intelligence
• Chatbots
• Stakeholders
• Intelligent systems
• Ethics
• Risk management
• Supply chains
• Financial Services
• Responsible Artificial Intelligence
• Ethical Principles
• Life Cycle Stages
• Entire Life Cycle
• Responsible Way
• AI Systems
• Stages Of The Development Process
• Question Bank
• Business
• Risk Assessment
• Training Data
• Learning Algorithms
• End-users
• Customer Service
• Planning Stage
• Subject Matter Experts
• Issue Of Bias
• Agile Development
• Existing Data Sources
• Indigenous Languages