Recent years have seen a rapid increase in the use of behavioural science to address the
priorities of public and private sector actors. There is now a vibrant ecosystem of …

The interplay of sanctions, perceptions, and crime has special significance in criminology
and is central to a long tradition of perceptual deterrence research as well as to more recent …

Enhancing vaccine uptake is a critical public health challenge. Overcoming vaccine
hesitancy, and failure to follow through on vaccination intentions requires effective …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

We formalize predictive optimization, a category of decision-making algorithms that use
machine learning (ML) to predict future outcomes of interest about individuals. For example …

Over 30,000 field experiments with The Washington Post and Upworthy showed that readers
prefer simpler headlines (eg, more common words and more readable writing) over more …

Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS dataset, are
commonly used in algorithmic fairness papers due to benchmarking practices of comparing …

Encouraging routine COVID-19 vaccinations is likely to be a crucial policy challenge for
decades to come. To avert hundreds of thousands of unnecessary hospitalizations and …

Public health organizations increasingly use social media advertising campaigns in pursuit
of public health goals. In this paper, we evaluate the impact of about 40millionofsocialmediaadvertisementsthatw …

Choice architecture tools, commonly known as nudges, powerfully impact decisions and can
improve welfare. Yet it is unclear who is most impacted by nudges. If nudge effects are …