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!!
Data analytics is about the search for patterns between different data sets.
Architecture is about the creation of patterns between different material sets.
Data visualization borrows from both camps to present information or knowledge in a user-friendly format.
The Data Lake and Data Pool are dirty, beware.
Wonder why it requires so much cleaning in order to become useful in Data Analytics?
It has been estimated that 70 to 90% of time is spent on cleaning dirty Data?
Maybe there is a market for an embedded Data cleaning agent?
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 the common point of entry to data science: modeling, simulation, analysis, and optimization actually the API development, instead of the GUI?
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?
Is it still just enough to state “Do No Harm”?
Algorithms are becoming useful tools. How do we maintain their role only for good?
Can an Algorithm learn right from wrong?
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.”
How can digital data become human- centered with Empathy?
Numbers and data are honest, and unemotional.
How will AI learn Empathy?
How will AI account for parameters that fall out of the norm, when the dataset rules are written to exclude some factors simply as, noise?
Where is the serendipity in AI?