QA for AI

Trust is mission-critical for any technology, so if AI solutions are to supplant and complement software, AI must reach the reliability standards currently expected from software. The difference is Quality Assurance (QA) has existed in software for three decades, and the burgeoning field of AI has barely begun to perform quality controls: 

1. We will take a journey through the history of QA, discuss why it is crucial, and discuss what lessons from other disciplines and industries we can apply to machine learning. 
2. Then, we will discuss what important role Explainable AI methods, not to mention best practices in MLOps, data engineering, and data science, can play. 
3. Lastly, we will discuss the challenge ahead. Given the many steps in a Machine learning (ML) pipeline and the many qualities to assess in an ML model, choreographing and standardizing tasks in a QA effort is a challenging undertaking. 

Thus session is ultimately about what business stakeholders and practitioners can do to make AI more trustworthy to the end-users of this technology.