Towards Bias-Free Data

Data’s Bias:  The Origins

In nature, there are the concepts that’s birds of a feather flock together, also  survival of the fittest, and many other behaviors  in support of unconscious biases.

Now we are moving into an era of data driven decisionpoints, how does Data’s Bias get identified and extracted from useful data sets?

It’s challenging to trust data will support anything other than the status quo if common and unconscious biases do not get cleaned out from the Data Lake or Data Pool!

In search of an Anti-Bias Data Cleaning Agent that results in Bias-Free Data!!

Design Build Data

Design Build Data may be an area for Design And Systems Thinking.

Structured, Semi-Structured and Un-Structured Data describe a spectrum of architectural solutions for the way data is design to fit together.

For the first time I looked at the city with all the buildings and began to visualize the similarity with data architecture!

Data is not something that we see everyday, yet for data to become useful, some architecture is required.

Design Build Data

Is Data Disposable?

In the Maker-Hacker environment is data disposable?

With research trending towards the Experiential, where is the common data points between one experiment and the next?

Data capture has a primary function of countering the one-off trends,  yet datasets are becoming more divergent overtime.

Is it the overlapping of distinct datasets that’s hold the promise in Data Driven Design?


Algorithms need Editors

A recent article on Mashable highlighted an important concept: Algorithms need Editors!

“Twitter’s ‘LasVagas’ hashtag fail shows the worst part of algorithms…

…Twitter’s system looked at the various Las Vegas shooting-related hashtags and chose the misspelling for whatever reason. And the people involved couldn’t do anything about it…

This is exactly why journalists have editors—and algorithms need them, too.”