The Premise, the Proposed Solutions, and the Open Challenges

The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms (OPAL) project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development. Keyword: algorithmic decision-making ; algorithmic transparency ; fairness ; accountability ; social goo

Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics

The wealth of information provided by real-time streams of data has paved the way for life-changing technological advancements, improving the quality of life of people in many ways, from facilitating knowledge exchange to self-understanding and self-monitoring. Moreover, the analysis of anonymized and aggregated large-scale human behavioral data offers new possibilities to understand global patterns of human behavior and helps decision makers tackle problems of societal importance. In this article, we highlight the potential societal benefits derived from big data applications with a focus on citizen safety and crime prevention. First, we introduce the emergent new research area of big data for social good. Next, we detail a case study tackling the problem of crime hotspot classification, that is, the classification of which areas in a city are more likely to witness crimes based on past data. In the proposed approach we use demographic information along with human mobility characteristics as derived from anonymized and aggregated mobile network data. The hypothesis that aggregated human behavioral data captured from the mobile network infrastructure, in combination with basic demographic information, can be used to predict crime is supported by our findings. Our models, built on and evaluated against real crime data from London, obtain accuracy of almost 70% when classifying whether a specific area in the city will be a crime hotspot or not in the following month

Integrated genomic analysis identifies driver genes and cisplatinresistant progenitor phenotype in pediatric liver cancer

International audiencePediatric liver cancers (PLCs) comprise diverse diseases affecting infants, children and adolescents. Despite overall good prognosis, PLCs display heterogeneous response to chemotherapy. Integrated genomic analysis of 126 pediatric liver tumors showed a continuum of driver mechanisms associated with patient age, including new targetable oncogenes. In 10% of hepatoblastoma patients, all before 3 years old, we identified a mosaic premalignant clonal expansion of cells altered at the 11p15.5 locus. Analysis of spatial and longitudinal heterogeneity revealed an important plasticity between 'Hepatocytic', 'Liver Progenitor' and 'Mesenchymal' molecular subgroups of hepatoblastoma. We showed that during chemotherapy, 'Liver Progenitor' cells accumulated massive loads of cisplatin-induced mutations with a specific mutational signature, leading to the development of heavily mutated relapses and metastases. Drug screening in PLC cell lines identified promising targets for cisplatin-resistant progenitor cells, validated in mouse xenograft experiments. These data provide new insights into cisplatin resistance mechanisms in PLC and suggest alternative therapies

On the privacy-conscientious use of mobile phone data

The breadcrumbs we leave behind when using our mobile phones-who somebody calls, for how long, and from where-contain unprecedented insights about us and our societies. Researchers have compared the recent availability of large-scale behavioral datasets, such as the ones generated by mobile phones, to the invention of the microscope, giving rise to the new field of computational social science.Agence française de développemen