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The unprecedented availability of large-scale human behavioral data isprofoundly changing the world we live in. Researchers, companies, governments,financial institutions, non-governmental organizations and also citizen groupsare actively experimenting, innovating and adapting algorithmic decision-makingtools to understand global patterns of human behavior and provide decisionsupport to tackle problems of societal importance. In this chapter, we focusour attention on social good decision-making algorithms, that is algorithmsstrongly influencing decision-making and resource optimization of public goods,such as public health, safety, access to finance and fair employment. Throughan analysis of specific use cases and approaches, we highlight both thepositive opportunities that are created through data-driven algorithmicdecision-making, and the potential negative consequences that practitionersshould be aware of and address in order to truly realize the potential of thisemergent field. We elaborate on the need for these algorithms to providetransparency and accountability, preserve privacy and be tested and evaluatedin context, by means of living lab approaches involving citizens. Finally, weturn to the requirements which would make it possible to leverage thepredictive power of data-driven human behavior analysis while ensuringtransparency, accountability, and civic participation.

The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good