The unprecedented availability of large-scale human behavioraldata is profoundly changing the world we live in. Researchers, companies,governments, financial institutions, non-governmental organizations and alsocitizen groups are actively experimenting, innovating and adapting algorith-mic decision-making tools to understand global patterns of human behaviorand provide decision support to tackle problems of societal importance. In thischapter, we focus our attention on social good decision-making algorithms,that is algorithms strongly influencing decision-making and resource opti-mization of public goods, such as public health, safety, access to finance andfair 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 consequencesthat practitioners should be aware of and address in order to truly realizethe potential of this emergent field. We elaborate on the need for these algo-rithms to provide transparency and accountability, preserve privacy and betested and evaluated in context, by means of living lab approaches involvingcitizens. Finally, we turn to the requirements which would make it possible toleverage the predictive power of data-driven human behavior analysis whileensuring transparency, accountability, and civic participation.