The 5th IEEE International
Conference on Data Science
and Advanced Analytics

1–4 October 2018
Turin — Italy

Data Science for Social Good

Keynote Speaker

Marcel Salathé

Aims and Scope

Nowadays, we are witnessing an ever increasing interest in the exciting opportunities provided by data science when applied to the fields of social innovation, philanthropy, international development and humanitarian aid. From the analysis of satellite imagery to mapping poverty, to using Facebook data to track the global digital gender gap, the field of “Data Science for Social Good” is growing fast. Data from industrial actors (e.g. mobile phones data, remote sensing, satellite imagery) as well as data from digital traces generated by the pervasiveness of the Web in combination with state-of-the-art knowledge generated by data science can be synergically exploited to solve issues around many social problems and support global agencies and policymakers in implementing better and more impactful policies and interventions.

The goal of this session is to gather researchers from the fields of data science, machine learning and artificial intelligence together with experts in the social and political sciences to present and discuss applications of data science with a high social impact. The session will consist of: i) one or two invited presentations from the leading practitioners in the field, and ii) a series of contributed presentations on applied research that fits the theme of data science for social good in a broad sense.

In particular, in this special session we want to cover examples of Data Collaboratives, a new form of collaboration, beyond the public-private partnership model, in which participants from different sectors — especially companies — exchange their data to create public value. Also, the session will particularly welcome those contributions which aim at closing the gap between developing models and algorithms, and their practical applications in the workflow of non-profit organizations. Other topics of interest will be: models interpretability in support of better decision making; data science applied to impact measurement of social good programs; algorithmic privacy and fairness in the context of social good.

Topics of Interest

  • International development
  • Humanitarian aid
  • Gender data gaps
  • Health in developing countries
  • Migrations
  • Education
  • Unemployment
  • Inequality and poverty reduction
  • Environment and sustainability


Daniela Paolotti
ISI Foundation,
Turin, Italy

Michele Tizzoni
ISI Foundation,
Turin, Italy

Contact Persons

Daniela Paolotti –
Michele Tizzoni –

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