When we published a post on contextualize trust, several business officials started asking a question from our support executives that – Does a security analysts trust the analytical solutions that are powered by the Machine Learning technology?
As per our experts’ opinion, the answer to this question is NO (in majority cases). Now definitely your mind might be prepared to ask next question i.e., ‘why the answer is no?’ In order to give answer to this, we have to move a little bit back. We believe that it will work to go a moment back and understand some basics involved in machine learning technology and analytics. This will help in getting the best answer to this query.
Before we begin with the major technical part first, you have to revise some important terminologies. So let’s get started!
If machine learning algorithms have this capability, why don’t we believe its observations? On basis of the research, we believe that following issues are the causes:
It seems to be a perverse pleasure in describing on how self-driving cars could crash, automatic recognition of images could racist errors, and neural networks are coded for cracking passwords, with much less focus located on all the advantages available for users from machine learning technology.
Even in dreams where machine learning algorithms have gained a near-perfect status, it is secure to predict that being human, security analysts will still have reservations about the output of machine learning technology. Following listed are six fields, which needs to be looked at to detect it:
all renders the confidence for which we are confident and have the ability to ignore false alarms. Most importantly, the algorithms are helping users in making decisions – they are not for replacing them.
Machine learning technology is all about data analyzes and detection of a useful patter. Assuming that the technology will work perfectly on its own is not good assumption. It requires security analysts to describe a better scenario and come with the actual correct outcome. No only analysts but, software developers also show their active participation in the same because this helps in recognizing the false-positives.
In either situation, we are profiting – if its an algorithm that is improper, we have a chance to upgrade it. Also, this will simultaneously help in improving users’ mechanism of visualization and alert.
Is it possible to build a trust in between analytics and its end users by adding data, case-studies, investigations, following social norms, and educating about models? The answer to this question is simply Yes! Integration of the explanation, power for controlling the final output, false-negative investigation, and ML made up by social norms, is an approach to fill the gap between security analysts and machine learning technology powered analytical solutions.