Artificial intelligence (AI) and privacy: 2 key security practices

Before implementing an AI strategy, there are some techniques for considerations to protect privacy and ensure safety standards. The next-generation digital product engineering with Artificial Intelligence will help to develop new business models, experience, and revenue streams. Showing caution about possible problems of wild head-lines about cutting-edge AI breakthroughs. 

Let’s talk about Alpha Fold, It is an AI software program developed by Google’s deep mind to perform the predictions of protein structure. This software solves a 50 years old protein folding problem, there are groups of  less showiness but feasibly more embedded business 

Artificial Intelligence advances that are helping to make it more obligation and 

Secrecy-conscious. As algorithms indulge more and more data sets have been increasing in training and deployment, privacy of data related to AI and machine learning will grow in importance especially with new regulations. 

For future plan for AI investments,  two techniques will protect and ensure safety standards:

1) Federated Learning :

Federated learning is a machine learning technique that solves the biggest data privacy issues especially in healthcare. The long-established knowledge of the last decennary was to UNSILO data wherever possible.

            UNSILO is a technology that extracts most important concepts from a document using machine learning techniques. Nevertheless, the resulting data mixture necessary to train and deploy Machine Learning algorithms has created some problems in privacy and security, particularly when sharing data between organizations. 

This learning will give the insights of mixture data sets while maintaining the data safe and secure in a non-mixture environment. The basic assumption is that local machine learning models are trained on private data sets, and model updates spring between data sets to be mixtured centrally. Imperatively, the data never has to assent its local environment.

            In this way, the data remains secure while sharing it between organizations. So this learning reduces the risk of losing data or leak compromising the privacy of data because on the contrary of sitting in a single warehouse, the data has been spread among many. 

2) Explainable Artificial Intelligence (XAI) : 

Many models of Artificial Intelligence and Machine learning including neural networks have been trained on large number of data, these models are regularly unexplainable due to the frustration in discovering how and why they make some decisions. To make them more explainable and more crystal-clear, we need to make them more understandable. 

In the upcoming area of research called Explainability uses practical techniques for bringing clarity to simple and complex systems such as decision trees as well as neural networks. By explaining the system clearly researchers may be able to know why mistakes are made and how to fix them quickly.

Artificial Intelligence’s decision making cannot be trusted blindly by the field of health care, financial services and insurance. While approving bank loans, if someone got rejected we need to understand the reason, mainly while considering bias creeping into Artificial Intelligence’s systems. To overcome these problems XAI is the major focus for organizations to deploy AI systems in future. 

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