  The term ethics is widely used, explored, and debated in the context of
developing Artificial Intelligence (AI) based software systems. In recent
years, numerous incidents have raised the profile of ethical issues in AI
development and led to public concerns about the proliferation of AI technology
in our everyday lives. But what do we know about the views and experiences of
those who develop these systems -- the AI practitioners? We conducted a
grounded theory literature review (GTLR) of 38 primary empirical studies that
included AI practitioners' views on ethics in AI and analysed them to derive
five categories: practitioner awareness, perception, need, challenge, and
approach. These are underpinned by multiple codes and concepts that we explain
with evidence from the included studies. We present a taxonomy of ethics in AI
from practitioners' viewpoints to assist AI practitioners in identifying and
understanding the different aspects of AI ethics. The taxonomy provides a
landscape view of the key aspects that concern AI practitioners when it comes
to ethics in AI. We also share an agenda for future research studies and
recommendations for practitioners, managers, and organisations to help in their
efforts to better consider and implement ethics in AI.
