The labels used to train machine learning (ML) models are of paramount importance.
Typically for ML classification tasks, datasets contain hard labels, yet learning using soft …

Placing a human in the loop may help abate the risks of deploying AI systems in safety-
critical settings (eg, a clinician working with a medical AI system). However, mitigating risks …

In practice, preference learning from human feedback depends on incomplete data with
hidden context. Hidden context refers to data that affects the feedback received, but which is …

Reinforcement Learning from Human Feedback (RLHF) is a powerful paradigm for aligning
foundation models to human values and preferences. However, current RLHF techniques …

Individual human decision-makers may benefit from different forms of support to improve
decision outcomes. However, a key question is which form of support will lead to accurate …

The artificial intelligence research community is continuing to grapple with the ethics of its
work by encouraging researchers to discuss potential positive and negative consequences …

In practice, preference learning from human feedback depends on incomplete data with
hidden context. Hidden context refers to data that affects the feedback received, but which is …

Imitation learning (IL) has recently shown impressive performance in training a
reinforcement learning agent with human demonstrations, eliminating the difficulty of …

Reinforcement Learning from Human feedback (RLHF) has become a powerful tool to fine-
tune or train agentic machine learning models. Similar to how humans interact in social …

Developing machine learning models worthy of decision-maker trust is crucial to using
models in practice. Algorithmic transparency tools, such as explainability and uncertainty …