Part 2: Ethics in AI

February 12, 2020 Leave a comment

In attempting to come to grips with where ethics is being applied to AI I have found through formal research approaches that both private and public sector organizations and companies have created frameworks or operating principles for ethical use of data and algorithms.

That is the good news.  The bad news is they are not being enforced or really being used.  It appears as though lip service is being paid to the recommendations.

All of this got me to start to think of fundamentals of ethics and how and why it can be applied for AI or cognitive enterprises and that it would work.  

One of the challenges is if you look into the field of Ethics you are delving into a subject that has been discussed, argued and loosely defined over many years.  So ethical theory is not new yet we are attempting to apply it to an area that did not exist.

But that should not be a concern for us. If you look at how ethics as be applied and re-applied to all aspects of humanity over the years.  And in recent decades areas such as equality, abortion, class differences, hiring practices etc.  So AI will be nothing new to ethical theory.

The challenge that is raised is defining what really are ethical principles we can apply that make the most sense to ensure people are protected.

One of the guiding principles I was trained in was the area of equal consideration of interests.  So I will delve into what that means in greater detail in AI in the next post.

Have a great day !

Filed under AI, Cognitive Computing, Ethics, Explainability, Information Lifecycle Governance Tagged with ,

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.  

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AI and Ethics

March 12, 2019 Leave a comment

IMG 1954

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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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