The AIQ Meta-Testbed: Pragmatically Bridging Academic AI Testing and Industrial Q Needs
It addresses the problem of defining and ensuring AI quality for practitioners, but is incremental as it builds on existing concepts without major breakthroughs.
The paper tackles the lack of consensus on AI quality assurance by proposing a working definition and pragmatic testing approach, and introduces the AIQ Meta-Testbed as ongoing work to bridge academic and industrial needs.
AI solutions seem to appear in any and all application domains. As AI becomes more pervasive, the importance of quality assurance increases. Unfortunately, there is no consensus on what artificial intelligence means and interpretations range from simple statistical analysis to sentient humanoid robots. On top of that, quality is a notoriously hard concept to pinpoint. What does this mean for AI quality? In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing. Finally, we present our ongoing work on establishing the AIQ Meta-Testbed.