I was also interviewed by Strata Conference Chair Edd Dumbill for the inaugural issue of Big Data Journal on whether big data inferences, like criminal profiling, should be outlawed — effectively defining a new category of machine-perpetrated thoughtcrime.
One clarifying tweak I’d make to my quote in Jordan’s piece is to add the word “alone”:
"Because geeks like me can do stuff like this, we can make stuff work - it’s not our job ALONE to figure out if it’s right or not. We often don’t know.”
My point is that geeks alone can’t make the tough policy calls without the help of the more wonkish humanitarians among us — historians, philosophers, activists, ethicists, anthropologists, economists, and journalists. Technologists, too often, just don’t have the training or experience.
Update 2: But that’s not all! Jordan also wrote a companion piece on how London police used low-tech predictive policing in the 1990’s to reduce rapes. Essentially, they noticed that clothesline theft was a gateway crime to rape. Woah.
Too often we geeks, suits, and wonks live in our own worlds of making it work, making it sell, or making it right, respectively. We don’t seem to have the capacity to routinely cross-pollinate. This may be why knowledge grows exponentially and wisdom grows only linearly. Conferences like Strata give us a venue to escape from our mile deep and inch wide comfort zones.
Take the talk by Quid’s Amy Heineike on maps, not lists. She references Eli Pariser’s warning on how listed search results spoon feed us the “best” result at the top. Heineike argues that maps lend themselves to mindful exploration even for non-spatial data. I certainly agree. What’s more, she also strikes a hopeful chord for data science. In the Filter Bubble (which I highly recommend), Pariser quotes Marshall McLuhan who warns that “we shape our tools and thereafter our tools shape us.” Heineike provides a great example of how we then reshape our tools — something I discussed at Strata+Hadoop World last Fall.
Twitter’s Nathan Marz sounded similar “people meet data” themes during his keynote on human fault-tolerance. His battle cry is for data systems that protect themselves from human error, similarly to how we protect systems from hardware faults. His ground truth axiom that the worst problems result from lost or corrupt data (especially silent corruption) is spot-on. All else is recoverable. He recommends immutable systems where bugs can’t delete or corrupt data. Immutability resonates with my electrical engineering training where outputs are a result of transfer functions on inputs. This is the essence of functional programming that I’ve taken a liking to lately.
Finally, my talk (video here) on the sensitive topic of criminal profiling attempted to push the technology and the debate. We designed a felon classifier based on a defendant’s publicly available non-felony criminal record and personal data. The resulting classifier is available on GitHub here.
One of the motivations for the talk was to prove that big data inferences are not a new category of thoughtcrime. However, actions based on those inferences could very well be criminal. For predictive policing, the courts will be the final human arbiter on the admissibility of such computerized informants. Here’s my interview with O’Reilly’s Mac Slocum that touches on some of these issues:
What I found most interesting about this exercise is that the technology can only take us so far. The classifier’s operating point determines how many innocents will be classified as felons (false positives) and how many felons will go undetected (false negatives). Only we fallible humans can choose the right trade-off between tyranny and anarchy. Such is the line that the responsible innovator must walk between high-tech mercenary, traditional capitalist, and social entrepreneur.
The earth probably sees plastic as just another one of its children. Could be the only reason the earth allowed us to be spawned from it in the first place. It wanted plastic for itself. Didn’t know how to make it. Needed us. Could be the answer to our age-old egocentric philosophical question:
Us: “Why are we here?” Earth: “Plastic … asshole.”
Kurzweil expands on his decades-long thesis that the Law of Accelerating Returns (LOAR as he’s coined it) drives the exponential increase in price/performance of computing. By 2029, this growth in hardware/software will create an intelligence that rivals our brain’s wetware. The LOAR is based on five key concepts that underly all computing:
There’s a lot at stake in today’s election. As expected, the candidates have been vocal about education, health care, women’s rights, foreign policy, the environment, economics — even Big Bird. But my question to the candidates is: “What’s your stand on the balance between government regulation and technology innovation?”
If the republicans win, we can count on business-friendly policies that drive innovation. If the democrats win, we’ll see continued focus on consumer protections. Regardless of who wins, one thing is certain: the country loses if we fail to find the balance between the two.