The Fair Play Algorithm: Setting up a Global Standard Procedure.
What is an Algorithm?
“An algorithm is a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.”
What is Machine Learning?
“Machine Learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (Progressively improve performance on a specific task) with data, without being explicitly programmed.”
What are Machine Learning Algorithms?
“Machine Learning uses programmed algorithms that receive and analyse input data to predict output values within an acceptable range. As new data is fed to these algorithms, they learn and optimise their operations to improve performance, developing “intelligence” over time.”
What is a Supervised Machine Learning Algorithm?
“In Supervised Learning, the machine is taught by example. The operator provides the machine learning algorithm with a known dataset that includes desired inputs and outputs, and the algorithm must find a method to determine how to arrive at those inputs and outputs. While the operator knows the correct answers to the problem, the algorithm identifies patterns in data, learns from observations and makes predictions. The algorithm makes predictions and is corrected by the operator – and this process continues until the algorithm achieves a high level of accuracy/performance.”
What is a Classification Supervised Machine Learning Algorithm?
“In Classification tasks, the machine learning program must draw a conclusion from observed values and determine to what category new observations belong.”
Government Programmes and Initiatives to Promote and Encourage Jobs Creation:
Every government across the world has its own labour market policies. And in order to promote and encourage job creation and economic development, each country has several “schemes” to provide job training and hence achieving full employment for experienced as well as un-experienced job seekers.
There are a clear worldwide increased tendency of these types of schemes for governments to promote and encourage jobs creation and hence economic development.
Reality Check: The Problem.
In despite of a clear worldwide increased tendency of these types of schemes for governments to promote and encourage jobs creation, my research clearly shows that there is not any standard procedure nor at national nor international level for making the process of selecting people (job seekers) to those worldwide programmes and initiatives to promote and encourage jobs creation, within government public administrations (public sector); more efficient, transparent and, above all, fairer.
Let’s say that the central government of a particular country decides to create a new initiative to promote and encourage jobs creation and hence decreasing the overall unemployment rate of that country.
This central government employment initiative or programme is run and managed by a local government such as for example: The Development Agency of a Town Hall of a particular major City.
Such employment scheme consists on getting 50 jobs seekers into work by offering them a paid job opportunity for six months as Forest Guards.
Let’s say that there are 1000 job seekers (candidates) that are applying for those very same 50 government job vacancies.
How would then the Development Agency of a Town Hall of a particular major City of a particular Country in the world, select those 1000 job seekers (candidates) that are applying for these very same 50 government job vacancies in a way more efficient, transparent and, above all, fairer?
The Solution: The Fair Play Algorithm.
I am in the process of creating and designing a New Open Source Classification Supervised Machine Learning Algorithm to make the process of selecting people (job seekers) for different worldwide programmes and initiatives to promote and encourage jobs creation and hence decreasing the overall unemployment rate, within government public administrations (public sector); more efficient, transparent and, above all, fairer.