Thwarting facial recognition

Ethical AI Frameworks vary

EY global study finds disconnect on ethical artificial intelligence (AI) priorities between public and private sector

Part 2: Ethics in AI

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 !

Part 1: A Journey into Practical Ethics and AI

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.

AI Use Case 1: HR Hiring and Staff Attrition Step 1 Knowing Current State

In this new set of series I will post and reflect on engagements that I have had with organizations around the world.  I will discuss what they are attempting to do with various AI related technologies and then provide updates on the projects as they progress.  Like all AI projects they tend to start smaller in scale and more importantly with a specific use case or business outcome needed.  For reasons of confidentiality I will not name or describe the actual organizations in any detail.  What is important is to learn from their approach and what succeeds.

Step 1: Understanding their current state

The organization is a large law firm in the US.  They are currently attempting to improve their hiring process to ensure they can hire the most talented people for the various law practices.  Currently they use standard HRMS technology to track skills assessment and performance metrics; but only once a person is hired into the firm.  They currently use descriptive analytics to run standardized reports on historical data only.   They now want to improve how they hire lawyers.

To hire the best talent is a highly competitive process.   Each year the number of law students is limited, and the process is controlled by the law societies.  Therefore, this firm wants to be better at quickly identifying the top talent so that they can interview and potentially offer them a summer placement before their competition does.  The current process is extremely manual in nature and therefore they wish to automate the process which can help reduce the costs of hiring.

Strategic alignment of the use case to the organizations corporate strategy is key in ensuring successful deployment of the AI technology.  The external strategy of the organization is to differentiate themselves with their industry experience and team focus.  Internally the strategy is to optimize cost in the recruitment and retention process. The investment that the law firm makes in hiring and training a lawyer can be high; therefore, they wish to maximize their investment in a person that is hired and employed with them.

Current state is a highly manual process.  The hiring team has to read thousands of resumes and supporting material and summarize and review each candidate.  Then; setting up interviews of the top candidates with the view of hiring a certain number of summer students each year.

On the initial assessment of current state it was felt that targeting the internal strategy of lower cost would be the best area to focus on.  This strategy of lowering cost to gain the best legal resources is where AI can assist. 

The HR and hiring teams have a clear understanding who makes an ideal candidate and having an AI system that can read resumes and other sources of information on each candidate would speed up the candidate identification process and reduce the risk and cost of attrition over time.  This then supports their external strategy of having the best industry focused legal teams for the clients they serve.

Certain questions and concerns around data use and data privacy will need to be discussed as well as ethical considerations.  The HR team that participated in this workshop highlighted the fact that they have a pretty good ‘sense’ on how to identify strong candidates.   However; they were also hoping that as the AI technology learns from assessing candidates that the HR team would learn of candidate dynamics that they may not have considered in the past.  As an example a student applying from overseas may not have the standard experience and educational background that typically is reviewed.  

One other observation in running this workshop which will be echoed across all of the use cases I describe is that having line of business as well as IT in the room at the same time is extremely beneficial.

Next posting will be on future state and where AI can assist.

AI and Ethical use of data: Data Validity Part 1

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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