A Framework for Implementing AI Projects Which Benefit Historically Disadvantaged Communities

Article Dedication: Isabella Faith Tichenor (2021)

This article is dedicated to 10 year old Isabella Faith Tichenor.

Prior to Isabella's suicide, Justice Department Investigators had found persistent failures by the Davis School district (Utah) to respond to reports of race-based harassment and violence targeting Black and Asian students.

Black students, who make up 1 percent of the student population, have been under attack by suspected white supremacists within the student body. Administrators are said to have failed to respond to hundreds of reports from students about being called slaves, the N-word, and threatened with lynchings if they didn’t go “pick cotton” as instructed by their white classmates."

Students are not born hating others. This is covertly and overtly taught.

https://news.yahoo.com/utah-girl-10-commits-suicide-001000669.html

HARMONIC ONE COMMUNITIES - COMMENTARY

November 2021

Article:

There is a gargantuan body of knowledge, research and information which depicts the complex challenges members of historically socially and economically disadvantaged communities have faced for hundreds of years.

Numerous statistical studies display how these communities are consistently and repeatedly disproportionately subjected to covert and overt forms of unnatural forces and phenomena. This has affected behaviors of all human activities to members of these communities. This includes economics, education, entertainment, health , business, technology, employment, environmental, law, politics, religion, sex and war.

The causes, reasons and situations for these problems are diverse and complex. Hence, from a holistic perspective, we must respect the Iceberg Effect of this situation: what we see and verbally articulate on the surface, may not fully include the larger hidden parts of the problem that is "under the surface" . Therefore, systems thinking allows us to have a healthy balance between the unknown and the known as we proceed with attempts to address or solve problems in historically disadvantaged communities.

But we must also remember that AI, with its inspiration from Mother Nature and her many examples of network-like phenomena, is just as complex as the community problems stated. While scientists did not (and still do not) fully understand the human brain, this incomplete view of the brain did not stop them from developing hardware and software to mimic reason and thought. This is why it’s called “Artificial” intelligence ..... it's not the same natural intelligence displayed by humans and animals. It's fake.

But synthetic cognition has a strength: the ability to do mathematical calculations on large amounts of data and instantly convert the raw data into information to make “a decision” (i.e neural networks). There are other advantages, but this basic attribute has made it a valuable tool in a society where information is becoming the new currency.

But, ironically, this widely used tool has another opportunity: to improve historically disadvantaged communities. The motivation scientists used to develop machines modeled after natural phenomena that they do not fully understand should also be used for communities such as those in the African American and African Diaspora.

Members of disadvantaged communities should be able to use AI to address problems they have personally experienced. Within this effort, there must be equal balance in the theory and application of AI to help members of these communities to solve their own problems. Too much or too little of either (theory vs application) would yield AI useless.

The question is where do we start?

There is no need to reinvent the wheel if methods exist already to address complexity (unless invention is needed upon application)

Enter systems engineering.

If systems thinking is about how you organize your “thinking”, systems engineering is about organizing what you “do”. While there are many fancy definitions of systems engineering, it is basically a structured approach to convert an idea into a product or solution within a fixed time period.

Like systems thinking, systems engineering is not new and has (and is continued to be) been utilized by companies and large institutions for years to build very complex machines and structures. It's an approach that has been used to build many complex products…. From the iphone to an electric car.

Hence when developing an AI project that can address complex problems in socially economically disadvantaged communities, we can use the same (or similar) tools and methods for building solutions for complex problems.

The major steps of this method can be tailored to develop solutions for disadvantaged communities. An outline of this adaptation is as follows:

STEP 1: DEFINE THE PROBLEM/NEED - clearly state what is the situation, person or thing that needs attention and needs to be addressed or solved.

STEP 2: FEASIBILITY STUDY – Determine if the project is worth doing and all of the resources needed exist for the effort to be a success.

STEP 3: PROJECT PLANNING- Plan out who are the major stakeholders in the project, the roles, titles and a functional timeline with risk mitigation plans to complete the project.

STEP 4: SOLUTION VISION – Provide a high level view of how the solution or product should work.

STEP 5: HIGH-LEVEL DESIGN – Create high level design from the solution vision.

STEP 6: DETAILED DESIGN – Create a detailed design as preparation for construction.

STEP 7: CONSTRUCTION – Use the design to build the solution. Some elements during construction of an AI solution

may include the following:

7a. Data Understanding

7b. Data Collection

7c. Data Preparation/Preprocessing (Standardize and Normalise the data)

7d. Exploratory Data Analysis

7e. Data Modeling/Algorithm Selection/Development

7f. Model Evaluation, feature engineering and model metrics

7g. Prep model for iterative testing.

7h. Model Final Evaluation

STEP 8: TEST – Test the solution/product in a controlled environment to confirm functionality or make adjustments.

(note in this step, you are testing the system as a whole, not the exclusively model)

STEP 9: DEPLOYMENT – Begin deploying and integrating the solution in the live environment.

STEP 10: OPERATION AND MAINTENANCE – Monitor and maintain as needed

STEP 11: CONTINUOUS REVIEW AND IMPROVEMENT – Conduct iterative agile reviews of the solution to see if goals are met OR anything needs to be improved to enhance results.