Social robots are robots designed to interact and communicate directly with humans, following traditional social norms. However, many of these current robots operate in discrete settings with predefined expectations for specific social interactions. In ...

Machine learning -- the part of artificial intelligence aimed at eliciting knowledge from data and automated decision making without explicit instructions -- is making great strides, with new algorithms being invented every day. These algorithms find ...

Fairness in machine learning has become a significant area of research as risk assessments and other algorithmic decision-making systems are increasingly used in high-stakes applications such as criminal justice, consumer lending, and child welfare ...

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the "long tail" is critical for ...

Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user. Understanding the reasons behind predictions and queries is important when assessing how the learner works and, in turn, trust. Consequently, ...

In recent news, organizations have been considering the use of facial and emotion recognition for applications involving youth such as tackling surveillance and security in schools. However, the majority of efforts on facial emotion recognition research ...

The wide adoption of machine learning in the critical domains such as medical diagnosis, law, education had propelled the need for interpretable techniques due to the need for end users to understand the reasoning behind decisions due to learning ...

Millions of people worldwide are absent from their country's census. Accurate, current, and granular population metrics are critical to improving government allocation of resources, to measuring disease control, to responding to natural disasters, and ...

A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information as part of a statistical model, and any commitment to protect these ...

Autonomous vehicles are regularly touted as holding the potential to provide significant benefits for diverse populations. There are significant technological barriers to be overcome, but as those are solved, autonomous vehicles are expected to reduce ...

As more researchers have become aware of and passionate about algorithmic fairness, there has been an explosion in papers laying out new metrics, suggesting algorithms to address issues, and calling attention to issues in existing applications of ...

A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduce neural network decisions to a single set of features. In response, we ...

As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to ...