AI and Ethical use of data: Data Validity Part 1

March 13, 2019 Leave a comment

As mentioned yesterday I had the pleasure of taking a course on Ethics and Data Science.  Given that data science is a key area of the Machine Learning area of AI I thought I would expand on the subject as a starting point.  Each bullet I discuss has a more detailed discussion required.  But I truly believe that Ethical/Business Conduct requirements will be needed for all AI projects in order to provide transparency and explainability.

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So what are the risk considerations for ethics in AI ?  You will notice that there is overlapping considerations that have to be thought of and included.  

  • Data Validity
  • Algorithim Fairness
  • Informed Consent
  • Model Errors
  • Societal Impact
  • Ossification/Rigidity of ML models
  • Surveillance Impacts
  • Managing Change
  • Regression
  • Bias/Variance

So as an example of data validity:  and I am starting to see this criteria being included since it has a high risk or legal ramification.  In this day and age of access to third party data sets ( legally and illegally ) or the data sets that you have collected as an organization.  Are you questioning where the data comes from and if proper “informed consent” was given by individual providing that data or information ?  Did third party organizations thoroughly vet and validate the information ?  Has it been modified or redacted or scrambled ?  Can you still identify individuals or information by extrapolation ?  What proof will stand up in court if you are sued for accessing information not properly vetted by a third party.  Are you moving data from one line of business ( sales to marketing ) and are you violating any agreements that you have with clients or leads ?

 The course I took was done a few years ago and had a very optimistic tone to it that regulation would slow down innovation or drown skills; the recommending direction from the professor at that time was: don’t surprise people with outcomes and be able to explain how the model got to that outcome but leave how and what we analyze to the data scienctist.  I think we will see regulations step in.  Any time you have a practice: medicine, legal, engineering, real estate or accounting regulation has to be in place to protect human rights and the individual human.  

Filed under AI, Ethics, Explainability, Transparency Tagged with , ,

AI and Ethics

March 12, 2019 Leave a comment

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On a recent trip to New Orleans for work, we had the privilege of touring the location that houses and builds the floats for the Mardi Gras parade.  I took quite a few photographs but this grouping of money bags and Elvis got me thinking about ethics and AI.

You will be hearing more and more of this issue.  There are many who do not understand AI ( ML, NLP and RPA ) and are therefore concerned about what it will and will not do.

I had the benefit of taking a course on Ethics in Data Science as well as taking a course through MIT Sloan on Business strategy and AI ( I’m half way through so wish me luck ).

There are many factors for success or failure.  I think one key factor is the ability to bridge the gap between technology and business units.  

“Explainablity” is something that you will hear a lot about – the ability to clearly explain what AI is doing and how it came to its outcome.  That but also to clearly show there is no ossification of the system in that a hiring system is not hard wired the hiring practices of the the organization.  Or that young people are being denied loans at a  banking system due the the fact that the model is biased towards older applicants and that it was never given data sets for a population below the age of 35.

Ethics and AI will become more important to the point that we are now starting to talk about regulation and compliance to ensure good use of AI vs uncontrolled use of information and models.

The coin is in the air and flipping I wonder which way it will land ?

Maybe I should develop an AI Maturity Model assessment toolkit, I did this for Case Management systems and Cognitive Computing so I should be able to do it AI Maturity.  And in doing so Ethics and Transparency will have to play a key role.

 

 

 

 

 

 

 

 

 

Filed under AI, Cognitive Computing, Contextual Computing Tagged with

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