As robots are increasingly deployed in real-world scenarios, a key question is how to best transfer knowledge learned in one environment to another, where shifting constraints and human preferences render adaptation challenging.
AI's rapid growth has been felt acutely by scholarly venues, leading to growing pains within the peer review process.
In AI-assisted decision-making, effective hybrid (human-AI) teamwork is not solely dependent on AI performance alone, but also on its impact on human decision-making.
Although systematic biases in decision-making are widely documented, the ways in which they emerge from different sources is less understood.
This bi-directional feedback loop allows humans to learn how the model responds to new data.