Saturday, May 24, 2008

Open Notebook Science

"Some scientists think that their work consists entirely of exploring and discovering, and that they aren't responsible for the use their results are put to. Such a position is a mere illusion, willful blindness, or, at worst, just plain dishonesty. Knowledge gives power, and power requires a sense of responsibility and an idea that we are accountable for the direct or indirect consequences of our actions." - from a scientist-turned-Buddhist-monk

The hacker/'let's all work together for our mutual benefit' part of me loves the idea of Open Notebook Science, as discussed recently in the blogosphere. I can even appreciate the novelty of the idea of writing a dissertation online in real-time. ;)

But the part of me that has seen the darker side of human nature always balks at the thought of putting too much information out there. What is 'too much' information? Good question, and one that I've been thinking about for awhile.

Sharing is great, and putting science on the fast track to someplace better is great, but what about the possible detours or derailments that might stand between us and 'someplace better'? Can we universally say that knowledge is better than ignorance, therefore 'full-speed ahead'? Has the acquisition of knowledge always led to a better state of affairs? Can our collective morality be challenged by science that gives us too much too fast, and are those moral challenges 1) inevitable, regardless of the pace of the science, and 2) worth it in the end because they may eventually make us better?

The road to hell is paved with good intentions...

1 comment:

Jean-Claude Bradley said...

There are some that argue that getting more information can make things worse. See the book "Should I Get Tested for Cancer" for a very compelling case.

However in the more general case of scientific discovery getting at the truth and the whole truth has worked out very well in the past few centuries.

The opportunity that we have with Open Notebook Science is to link all of the raw data supporting scientific arguments to minimize misinterpretation and faulty assumptions that can cause problems.