  AI-based systems have been used widely across various industries for
different decisions ranging from operational decisions to tactical and
strategic ones in low- and high-stakes contexts. Gradually the weaknesses and
issues of these systems have been publicly reported including, ethical issues,
biased decisions, unsafe outcomes, and unfair decisions, to name a few.
Research has tended to optimize AI less has focused on its risk and unexpected
negative consequences. Acknowledging this serious potential risks and scarcity
of re-search I focus on unsafe outcomes of AI. Specifically, I explore this
issue from a Human-AI interaction lens during AI deployment. It will be
discussed how the interaction of individuals and AI during its deployment
brings new concerns, which need a solid and holistic mitigation plan. It will
be dis-cussed that only AI algorithms' safety is not enough to make its
operation safe. The AI-based systems' end-users and their decision-making
archetypes during collaboration with these systems should be considered during
the AI risk management. Using some real-world scenarios, it will be highlighted
that decision-making archetypes of users should be considered a design
principle in AI-based systems.
