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

TL;DR

This paper examines the benefits and risks of data-driven decision-making for social good, emphasizing transparency, privacy, and civic engagement to maximize positive impacts and mitigate negative consequences.

Contribution

It provides a comprehensive analysis of use cases, highlighting the need for transparency, accountability, and participatory approaches in social good algorithms.

Findings

Data-driven algorithms can improve societal outcomes in health, safety, and finance.

Potential negative impacts include bias, privacy violations, and lack of accountability.

Living lab approaches involving citizens are essential for responsible deployment.

Abstract

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.