Search This Blog

Showing posts with label algorithms. Show all posts
Showing posts with label algorithms. Show all posts

Wednesday, February 22, 2017

Shining light on the dark side of big data

Does the shift toward more data and algorithmic direction for our business decisions assure us that organizations and businesses are operating to everyone's advantage? There are a number of issues involved that some people feel need to be addressed going forward.
Numbers don't lie, or do they? Perhaps the fact that they are perceived to be absolutely objective is what makes us accept the determinations of algorithms without questioning what factors could have shaped the outcome.
That's the argument Cathy O'Neil makes in Weapons of Math Destruction: How Big Data Increases Inequality and Threatens DemocracyWhile we tend to think of big data as a counterforce to biased, just decisions, O'Neil finds that in practice, they can reinforce biases even while claiming unassailable objectivity.
 “The models being used today are opaque, unregulated, and uncontestable, even when they’re wrong.”   The math destruction posed by algorithms is the result of models that reinforces barriers, keeping particular demographic populations disadvantaged by identifying them as less worthy of credit, education, job opportunities, parole, etc. 

Now the organizations and businesses that make those decisions can point to the authority of the algorithm and so shut down any possible discussion that question the decision. In that way, big data can be misused to increase inequality. As algorithms are not created in a vacuum but are born of minds operating in a human context that already has some set assumptions, they actually can extend the reach of human biases rather than counteract them.  

“Even algorithms have parents, and those parents are computer programmers, with their values and assumptions, “Alberto Ibargüenhttps://www.knightfoundation.org/articles/ethics-and-governance-of-artificial-intelligence-fund,  president and  CEO and of the John S. and James L. Knight Foundation wrote.  “As computers learn and adapt from new data, those initial algorithms can shape what information we see, how much money we can borrow, what health care we receive, and more.”

I spoke with the foundation’s VP of Technology Innovation, John Bracken about its partnership with the MIT Media Lab and the Berkman Klein Center for Internet & Society as well as other individuals and organizations to create a $27 million fund for research in this area. 
The idea is to open the way to “bridging” together “people across fields and nations” to pull together a range of experiences and perspectives on the “social impact” of the development of artificial intelligence. As AI is on the road “to impact every aspect of human life,” it is important to think about sharping policies  for the “tools to be built” and how they are to be implemented.
Read more in 

Algorithms' Dark Side: Embedding Bias into Code

Tuesday, May 19, 2015

When efficiency, algorithms, and labor laws collide

Timeclock Wikipedia Commons
Flexibility is considered a virtue and an essential component an agile organization which can respond to changing needs in real-time. However, when that type of flexibility comes at the expense of employees, the company may not only be crossing the line of ethics but of law.

On April 10, New York Attorney General Eric Schneiderman directed his office to send a letter (posted by the Wall Street Journal) to 13 major retailers.  What Gap Inc., Abercrombie & Fitch, J. Crew Group Inc., L. Brands, Burlington Coat Factory, TJX Companies, Urban Outfitters, Target Corp., Sears Holding Corp., Williams Sonoma Inc., Crocs, Ann Inc. and J.C. Penney Co. Inc were all asked were to account for questionable scheduling practices known as “on-call” shifts.


Read more in 

The Legal Limits for On-Call Shifts

Thursday, May 7, 2015

Big data alone is not enough for an agile enterprise

Ever get a promotional email or ad that has no relevance to you? We all have, and it’s usually due to the marketing algorithms used to analyze big data inputs responding incorrectly to the wrong signal. For example, eBay started applying algorithms to the tags used to track customers in 2007 to measure the relevance of search results on its site. After a couple of years of success, the results became less accurate and seemed more random and arbitrary. The algorithms no longer worked because one of the tags had shifted. Events like that one resulted in customers seeing search results or receiving marketing emails that made no sense to them.
“The algorithm is not a human brain and doesn’t realize that the parameters have changed when tags change,” Ratzesberger observed. If a change is made to a variable, everything “downstream” from that variable must change, too, or the complex results can backfire.

The solution to this entire problem of achieving agility at scale is the Sentient Enterprise, a concept that Ratzesberger developed with Dr. Mohan Sawhney, a professor at Kellogg School of Management at Northwestern University. 
Read more here