  As AI increasingly integrates with human decision-making, we must carefully
consider interactions between the two. In particular, current approaches focus
on optimizing individual agent actions but often overlook the nuances of
collective intelligence. Group dynamics might require that one agent (e.g., the
AI system) compensate for biases and errors in another agent (e.g., the human),
but this compensation should be carefully developed. We provide a theoretical
framework for algorithmic compensation that synthesizes game theory and
reinforcement learning principles to demonstrate the natural emergence of
deceptive outcomes from the continuous learning dynamics of agents. We provide
simulation results involving Markov Decision Processes (MDP) learning to
interact. This work then underpins our ethical analysis of the conditions in
which AI agents should adapt to biases and behaviors of other agents in dynamic
and complex decision-making environments. Overall, our approach addresses the
nuanced role of strategic deception of humans, challenging previous assumptions
about its detrimental effects. We assert that compensation for others' biases
can enhance coordination and ethical alignment: strategic deception, when
ethically managed, can positively shape human-AI interactions.
