A Brave New Data World at the Heidelberg Laureate Forum

I recently told you about my upcoming trip to Germany for the Heidelberg Laureate Forum.  One of the exciting events during the forum is a discussion on a selected "hot topic."  This year's hot topic is big data.  Because events will be recorded and publicly available afterwards, and because I'll be blogging about it myself, I thought I'd share some information about what will be happening.  The following is from the organizers.

Scientists and Society face together the ethical challenges of computational science

Summary

Massive spying; privacy breaks; anonymity reversed… the penetration of information technology in all aspects of life has spurred a long series of worrying stories of lost privacy and “big brother” control. But the brave new world of Big Data is also behind some of the most hopeful news in recent years: from the “Twitter-revolutions” to the findings in astronomy and genetics. This year's Hot Topic session will focus on the social and ethical challenges of computational science. How to protect privacy against mass surveillance, organized crime, and companies’ intrusions? How secure is our data? How is intellectual property changing? Should we blindly trust massive data mining? How is computational science best used for the good? How should we regulate this brave new world? During the session, experts in these issues will think together with big minds and talented youth from mathematics and computer science. The objective is drafting an agenda of how scientists can help society in using the opportunities and dealing with the challenges of computational science.

Why should the HLF host this session?

The nature of HLF (top level speakers and talented youth in a free-speaking atmosphere) is the ideal setting for an open-minded, well-grounded discussion on the ethical and societal challenges of computational science. Inquiring into its social impact is a moral imperative for researchers, in a time in which it is used for all kinds of purposes. But it is also an important strategic choice: computational science is surrounded by a halo of omnipotence and suspicion, which could hamper its many beneficial effects and interfere with research. The polarization around GMO and nanotechnology is partially due to the delay of the scientific community in engaging in social debates. While the relative balance around stem cells or IVF is partially due to ethical issues being taken into account from the beginning. The HLF could be a fertile ground for making scientists proactive and constructive allies to the public in the debate around the social challenges of computational science.

Subjects

• Big Data for the common good. It should be clear from the beginning that the benefits of Big Data and computational science largely outweigh the challenges, and that the latter must be tackled precisely to make the most out of the first. This can be done by providing one or a few very explicit examples of the use of Big Data for the common good.
   
• State of the art. Providing an objective and description of the main facts and figures about social and ethical challenges of computational science (the source of sensitive data, the size and degree of transparency, controversial application, etc.)
   
• Technical challenges. It is very important to break the halo of omnipotence of Big Data, showing the pitfalls associated careless data mining: quality of data (biases, gaps, heterogeneity), false positives, approximation in models, biases in interpretation, etc.
   
• Socio-ethical challenges. The bulkiest part of the event should gravitate around issues like privacy (informational self-determination, identity management, limits to anonymity, massive spying, cybercrime, companies’ intrusions, data-based discrimination, dangers for socio-diversity, commodification etc.), security, intellectual property, and computational manipulation of social behaviour.
   
• Constructive approaches. Speakers should be chosen in such a way to prioritize those that put forward technological solutions or regulatory approaches, rather than limiting themselves to criticism (eg. New deal on data, Personal data purse, compensation schemes, Internet bill of rights, etc.)