  Previous research on refugee status adjudications has shown that prediction
of the outcome of an application can be derived from very few features with
satisfactory accuracy. Recent research work has achieved between 70 and 90%
accuracy using text analytics on various legal fields among which refugee
status determination. Some studies report predictions derived from the judge
identity only. Additionally most features used for prediction are
non-substantive and external features ranging from news reports, date and time
of the hearing or weather. On the other hand, literature shows that noise is
ubiquitous in human judgments and significantly affects the outcome of
decisions. It has been demonstrated that noise is a significant factor
impacting legal decisions. We use the term "noise" in the sense described by D.
Kahneman, as a measure of how human beings are unavoidably influenced by
external factors when making a decision. In the context of refugee status
determination, it means for instance that two judges would take different
decisions when presented with the same application. This article explores ways
that machine learning can help reduce noise in refugee law decision making. We
are not suggesting that this proposed methodology should be exclusive from
other approaches to improve decisions such as training of decision makers,
skills acquisition or judgment aggregation, but rather that it is a path worth
exploring. We investigate how artificial intelligence and specifically
data-driven applications can be used to benefit all parties involved in refugee
status adjudications. We specifically look at decisions taken in Canada and in
the United States. Our research aims at reducing arbitrariness and unfairness
that derive from noisy decisions, based on the assumption that if two cases or
applications are alike they should be treated in the same way and induce the
same outcome.
