User:HobakJoah/Machine learning/Bibliography

You will be compiling your bibliography and creating an outline of the changes you will make in this sandbox.

Outline of proposed changes
Click on the edit button to draft your outline. First of all, I plan to credit scientists before the introduction of the first machine learning models whose works contribute to the modern development of machine learning. By listing scientists who have concentrated on human cognitive behaviors, the article would provide more information on how machine learning developed. For example, Donald Hebb, a Canadian psychologist, published a book named The Organization of Behavior where he introduces the Hebbian theory, discussing the neural structure or synapses between the nerve cells. Hebb’s model of neurons interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or artificial neurons used by computers to communicate data. Other researchers who have studied on human cognitive systems contributed to the modern machine learning technologies as well including Logician Walter Pitts and Warren McCulloch, who proposed the early mathematical models of neural networks to come up with algorithms that mirror human thought processes. These works demonstrate that the current developments of machine learning did not merely come from the first model of machine learning, but also from the effort to understand the human cognitive processes.

In the limitations section of the article, I plan to emphasize the black box theory, which is the situation where the algorithm or the process of producing an output is entirely opaque, meaning that even the coders of the algorithm cannot audit the pattern that the machine extracted out of the data. I will be adding more examples of machine learning models producing biased outcomes to add more reliability of the article.

On the ethics part, I will be adding a discussion of disproportionate demographics of diversity in the field of machine learning as well as the unequal access to the technology. Some scientists argue that such lack of equal and balanced representation of different demographic groups participating and accessing the technology is a major factor that results in current machine learning models’ vulnerability to biased outcomes.