ERCIM News 116
January 2019
Special theme Transparency in Algorithmic Decision Making
Guest editors Andreas Rauber (TU Wien and SBA), Roberto Trasarti and Fosca Giannotti (ISTI-CNR)
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by Claude Kirchner (Inria) and James Larrus (EPFL)
Science is in revolution. The formidable scientific and technological developments of the last century have dramatically transformed the way in which we conduct scientific research. The knowledge and applications that science produces has profound consequences on our society, both at the global level (for example, climate change) and the individual level (for example, impact of mobile devices on our daily lives). These developments also have a profound impact on the way scientists are working today and will work in the future. In particular, informatics and mathematics have changed the way we deal with data, simulations, models and digital twins, publications, and importantly, also with ethics.
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by Michele Loi and Markus Christen (University of Zurich)
The use of machine learning in decision-making has triggered an intense debate about “fair algorithms”. Given that fairness intuitions differ and can led to conflicting technical requirements, there is a pressing need to integrate ethical thinking into research and design of machine learning. We outline a framework showing how this can be done.
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by Arnaud Legrand (Univ. Grenoble Alpes/CNRS/Inria)
To accelerate the adoption of reproducible research methods, researchers from CNRS and Inria have designed a MOOC targeting PhD students, research scientists and engineers working in any scientific domain.
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by Catherine Tessier (Université de Toulouse)
The University of Toulouse and Inria have set up face-to-face training in research ethics and integrity for doctoral candidates based on debates about their own theses.
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by Judith ter Schure (CWI)
An estimated 85 % of global health research investment is wasted [1]; a total of one hundred billion US dollars in the year 2009 when it was estimated. The movement to reduce this waste recommends that previous studies be taken into account when prioritising, designing and interpreting new research. Yet current practice to summarize previous studies ignores two crucial aspects: promising initial results are more likely to develop into (large) series of studies than their disappointing counterparts, and conclusive studies are more likely to trigger meta-analyses than not so noteworthy findings. Failing to account for these apects introduces ‘accumulation bias’, a term coined by our Machine Learning research group to study all possible dependencies potentially involved in meta-analysis. Accumulation bias asks for new statistical methods to limit incorrect decisions from health research while avoiding research waste.