  Allocation of scarce resources is a recurring challenge for the public
sector: something that emerges in areas as diverse as healthcare, disaster
recovery, and social welfare. The complexity of these policy domains and the
need for meeting multiple and sometimes conflicting criteria has led to
increased focus on the use of algorithms in this type of decision. However,
little engagement between researchers across these domains has happened,
meaning a lack of understanding of common problems and techniques for
approaching them. Here, we performed a cross disciplinary literature review to
understand approaches taken for different areas of algorithmic allocation
including healthcare, organ transplantation, homelessness, disaster relief, and
welfare. We initially identified 1070 papers by searching the literature, then
six researchers went through them in two phases of screening resulting in 176
and 75 relevant papers respectively. We then analyzed the 75 papers from the
lenses of optimization goals, techniques, interpretability, flexibility, bias,
ethical considerations, and performance. We categorized approaches into
human-oriented versus resource-oriented perspective, and individual versus
aggregate and identified that 76% of the papers approached the problem from a
human perspective and 60% from an aggregate level using optimization
techniques. We found considerable potential for performance gains, with
optimization techniques often decreasing waiting times and increasing success
rate by as much as 50%. However, there was a lack of attention to responsible
innovation: only around one third of the papers considered ethical issues in
choosing the optimization goals while just a very few of them paid attention to
the bias issues. Our work can serve as a guide for policy makers and
researchers wanting to use an algorithm for addressing a resource allocation
problem.
