The Cycle of Trust and Responsibility in Outsourced AI

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly becoming musthave capabilities. According to a 2019 Forbes Insights Report, "seventy-nine percent of executives agree that AI is already having a transformational impact on workflows and tools for knowledge workers, but only 5% of executives consider their companies to be industryleading in terms of taking advantage of AIpowered processes." (Forbes 2019) A major reason for this may be a shortage of on-staff expertise in AI/ML. This paper explores the intertwined issues of trust, adoption, training, and ethics of outsourcing AI development to a third party. We describe our experiences as a provider of outsourced natural language processing (NLP). We discuss how trust and accountability co-evolve as solutions mature from proof-of-concept to production-ready.