Algorithm Arrogance at Facebook

Pope Paul V - wikipedia, portrait by Caravaggio | https://en.wikipedia.org/wiki/Pope_Paul_V#/media/File:Paul_V_Caravaggio.jpgPosted to a Marketplace report on the most recent content stream tweak by Facebook:

It’s algorithm arrogance. There are many data science specialists working at Facebook, but there is reason to believe the new stream tweaks will not improve appreciably. One reason: users have no way to designate content you *do not* want to see (perhaps ever). Another: Facebook search is so unfriendly that search is rarely used to discover what you *do* want to read. (It’s part of the ever-popular toilet paper roll user interface). In other words, there’s plenty of data but not enough of the right sort to improve personalized relevance. Sure, not everyone would use a recommendation / search facility, but for those who do, the results would improve. The data “science” folks have become so algorithm-arrogant that you’d be hard pressed to even find a resource to personalize and improve your feed — with more data.

Reviewing Peer Review

Screenshot of Retraction Watch post: http://bit.ly/1M0tJO8Peer review is thought to be the gold standard for advancing “proven” science, but those who regularly publish and act as peer reviewers know that peer review has its problems. A recent study published in BMJ Open looked at this issue, and was the subject of a post on Retraction Watch. I posted a somewhat lengthy comment which addresses some broader issues that have surfaced in my work with the Elsevier-sponsored Innovation Explorers group.

Why Computers (and Doctors) Need Narratology

Image of the StoryTellers Cafe

Image Credit: Loren Javier | Flickr

The analysis by Peter Kramer @PeterDKramer¬†in the New York Times story “Why Doctors Need Stories”¬†points, in part, to the challenge faced by clinical decision support systems (CDSS) — and the use of artificial intelligence in health care more generally. While CDSS adoption lags far behind its apparent value, it is true that CDSS is weak when it comes to sense-making from narrative. The latter is still a subject of much research in cognitive psychology, with much work remaining to be done. The widespread familiarity with machine learning and keyword search perhaps hides the importance of vignette-driven inference. And the point should probably apply beyond health care to other software-assisted analytics. Therein is to be found the real human role as knowledge worker.

IBM Watson? Work on your narratology.