  The unprecedented availability of large-scale human behavioral data is
profoundly changing the world we live in. Researchers, companies, governments,
financial institutions, non-governmental organizations and also citizen groups
are actively experimenting, innovating and adapting algorithmic decision-making
tools to understand global patterns of human behavior and provide decision
support to tackle problems of societal importance. In this chapter, we focus
our attention on social good decision-making algorithms, that is algorithms
strongly influencing decision-making and resource optimization of public goods,
such as public health, safety, access to finance and fair employment. Through
an analysis of specific use cases and approaches, we highlight both the
positive opportunities that are created through data-driven algorithmic
decision-making, and the potential negative consequences that practitioners
should be aware of and address in order to truly realize the potential of this
emergent field. We elaborate on the need for these algorithms to provide
transparency and accountability, preserve privacy and be tested and evaluated
in context, by means of living lab approaches involving citizens. Finally, we
turn to the requirements which would make it possible to leverage the
predictive power of data-driven human behavior analysis while ensuring
transparency, accountability, and civic participation.
