Part 1: A Journey into Practical Ethics and AI

February 5, 2020 Leave a comment

Hello World,

 

I have not posted in a while since I have been a little pre-occupied doing my job.  I read daily and keep so many articles on AI and I see the tide come in and go out on opinion on where AI is in being used and how advanced or how simplistic it currently is.

But one thing seems to be ringing true and that ethical practises in AI need to be addressed.  I here about some companies calling for regulation or “laser” regulation which sounds to be a bit concerning.  

When I work with clients the discussion of data and the ethical use are discussed because without clean and ethically clean data the AI algorithm or digital platform will fail.  Once you get into the AI side of the discussion then you are into areas of bias, variance and transparency.  

So from my perspective I see ethical practices to being fundamental in all aspects of implementing AI.  I have said this many times but regardless of industry or technology you can reduce the discussion to a strategy around People, Process and Policy.

So with the rubric in hand of those three principles I am going to set off on a journey step by step to look at how practical ethics can be applied to people, process and policy in the realm of using AI technology. 

Wish me luck and may the trade winds be at my back.

BTW the images may be a little random but they are mine and they just give you something nice to look at while thinking about applying ethical practices to your data and AI journey.

Filed under AI, Ethics, Explainability, Information Lifecycle Governance, Transparency Tagged with , ,

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.

IMG 1823

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 , ,

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