  Machine Learning (ML) is an application of Artificial Intelligence (AI) that
uses big data to produce complex predictions and decision-making systems, which
would be challenging to obtain otherwise. To ensure the success of ML-enabled
systems, it is essential to be aware of certain qualities of ML solutions
(performance, transparency, fairness), known from a Requirement Engineering
(RE) perspective as non-functional requirements (NFRs). However, when systems
involve ML, NFRs for traditional software may not apply in the same ways; some
NFRs may become more prominent or less important; NFRs may be defined over the
ML model, data, or the entire system; and NFRs for ML may be measured
differently. In this work, we aim to understand the state-of-the-art and
challenges of dealing with NFRs for ML in industry. We interviewed ten
engineering practitioners working with NFRs and ML. We find examples of (1) the
identification and measurement of NFRs for ML, (2) identification of more and
less important NFRs for ML, and (3) the challenges associated with NFRs and ML
in the industry. This knowledge paints a picture of how ML-related NFRs are
treated in practice and helps to guide future RE for ML efforts.
