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Month: June 2018

Apple’s Newly Declared War on Data Collection (and Facebook?)

In the last week, beneath all the Trump and Kim Jong Un reporting, were several stories that state that Apple has in effect declared war on data collectors.  Make no mistake, what Apple is doing is making it significantly harder for companies big and small to collect your personal data.  The significance of this cannot be overstated in that many companies like Google and Facebook’s revenue is based on selling targeted advertising and if gathering this data becomes significantly more difficult, it could affect their bottom lines.

The First Volley:  No More Comments and Share Buttons

Last week, I was listening to the keynotes at the WWDC, and overall was pretty unimpressed as exec after exec droned on about new animojis or some other feature that I really didn’t care about, and then, Craig Federighi launched the first volley: Safari is going to block FaceBook and other social media like and share buttons as well as shared comment sections.  Facebook, Twitter and other sites use these buttons to track your activity when you are visiting other sites.  While it isn’t that big of a deal that this is happening on MacOS, it is VERY significant that Apple is instituting this change on iOS as well.  When I heard this, I was pretty shocked, but that was only the first volley, there were more to come.

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Adventures and Misadventures in Data Science Interviews

I’ve been waiting for some time to publish this, but I wanted to write about my experiences interviewing for data science jobs. Here’s my story, I worked at Booz Allen for nearly seven years but I felt it was time for a change. I very much like Booz Allen as a company and if anyone is interested in working there, please don’t hesitate to contact me.  But I felt I was ready for different challenges and started looking for work elsewhere.

Now that I started a new position, I thought I’d share some observations about what I learned from interviewing at numerous companies. I wasn’t tracking how many companies I interviewed with, but it was a lot. I have a lot of government experience and got a number of offers from government contracting firms. However, I came to the conclusion that in terms of career progression, joining another government contracting firm was not what I was looking for.

So here’s what I learned…

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My Ideal Workspace

As more and more research is showing that the open office design actually reduces productivity (here) and (here), I recently shared a post on LinkedIn about how github “de-broed” their workspace, but I thought I’d share my thoughts on what I like, and don’t like in a work space.  Above is a picture of my home office with some labels.  Not specifically labeled is that there is plenty of natural light.  One of the most depressing places I ever worked was a windowless cube farm where the developers liked to leave the lights off.  I was going out of my mind!!

  1. A Door:  My ideal workspace has a door so that when privacy is needed, I can close the door and when it is not, I can open it.
  2. A clock:  I know computers have clocks, but having a big visible clock is really helpful for making sure things run on time.
  3. A comfortable chair, with foot rest:  If I’m doing tech work for a long time, I don’t want to be sitting on something that will cause trips to the chiropractor.
  4. Big Monitors:  I’m a big fan of multiple, large monitors, as they really increase productivity.
  5. Music:  I like to listen to music, especially when coding.  When I’m working in more public spaces, I have headphones…
  6. Stress Relief:  I play trombone and when things get stressful, one can always reduce some stress by playing some Die Walkure …. LOUDLY.
  7. Lots of Geek Books:  Nothing sets the stage for coding than being surrounded by O’Reilly geek books.
  8. Family Photos or other Personal Items:  I do my best work in a space that feels like my own, so I think it is important that people can have a space with some of their personal items that feels like their own.   Hence… I’m not a fan of hoteling or workspaces that set people up to work on large tables.

What do you like in a work space?

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Book Review: Automating Inequality

I recently read Automating Inequality by Virginia Eubanks and would like to share some thoughts.  This review is the first of several book reviews I’ve been working on about books relating to the problems which are emerging from technology. I’ll keep this brief…

The Good:

I am glad that the conversation about social problems caused by technology is expanding.  Books like Automating Inequality are good contributors to that discussion.  In this book, Eubanks highlights a few situations where technology has negatively affected people’s lives, primarily poor people.  This technology also serves to limit poor people’s lives and opportunities, creating what she refers to as a digital poorhouse.

The use of machine learning can be a powerful tool for developing predictive analytics to  One abuse which I found particularly troubling was cited on pg. 137 which is a risk model which calculates a risk score for unborn children.

Vaithinathan’s team developed a predictive model using 132 variables–including length of time on public benefits, past involvement with the child welfare system, mother’s age, whether or not the child was born to a single parent, mental health, and correctional history–to rate the maltreatment risk of children in MSD’s historical data.  They found that their algorithm could predict with “fair, approaching good” accuracy whether these children woudl have a “substantiated finding of maltreatment” by the time they turn five.

 

What I Found Lacking:

What I found lacking in Automating Inequality was the lack of alternative proposals.   It is easy to criticize a technical solution, but these systems are often deployed against complex problems and finding a solution often requires a lot of vigilance, persistence and iteration.  Eubanks discusses the issue of welfare abuse, and seems to downplay the fact that welfare fraud is in fact a major issue in this country.  With some basic research on Google you can unfortunately find countless cases of individuals convicted of welfare fraud.  Clearly, welfare programs should make efforts to reduce fraud and make sure that their resources are going to people who truly need the assistance.

What Eubanks seemed to miss was what went wrong in the implementations that she highlighted.   In two cases, Eubanks highlighted several systems designed to improve the efficiency and efficacy of welfare programs.  From the book, it sounded as if the designers of these programs implemented various technical systems to automate the intake process for benefits.  What didn’t happen, and what Eubanks didn’t discuss in the book, was what was missing in these programs: continuous improvement.  The government agencies that implemented these programs took the approach that one would take when one is building a bridge or tunnel: get it done and once its done, move on to the next project.  This doesn’t work for information systems because they are never done.  Once you start using them, there will always be faults and opportunities to improve.  If an organization can rapidly iterate and improve the solution over time, they will end up with an effective solution.

Eubanks ends the book with a proposed code of ethics for data scientists and other technologists.  I wrote my own code of ethics for data scientists, and it is always interesting to me what others write on the subject.   I particularly liked these points from Eubanks’ Code of Ethics

  • I will not collect data for data’s sake, nor keep it just because I can
  • When informed consent and design convenience come into conflict, informed consent will always prevail.  (If only it were so… )

Overall, I found the book to be quite thought provoking, but I did disagree with some of the conclusions.

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