The Role of Synthetic Data

byTRSCHUSTER

187 followers

3 years ago

Computer technology and its rapid development is nothing new. There have been several blogs this week addressing various technological advances and much appreciated advice for utilizing changes currently underway.

While doing research for blogs, I came across a few articles that were about synthetic data. In summation, the articles pointed out the diverse and controversial aspects of “fake” data.

What is synthetic data?

Synthetic data is artificially manufactured information used to advance the ability of a computer system to behave within given parameters. Simulated data is extracted from algorithms and mathematics, then used as a substitute for testing as opposed to using real-world data that may not be readily available. Models and processes interact to create completely new data to mimic data taken from the real world.

Most of us are familiar with synthetic data by way of Google, Alexa, Siri, Sims, self-driving cars, etc. These programs started out using synthetic data and is now “learning” from real world input, to expand its knowledge base. We joke about how our devices are “listening” to us – well, they are!

What is synthetic data used for?

The answer to that is, just about everything saintly to sinister.

• Health Insurance companies create data for the purposes of teaching their AI models not to share, store or collect private, government protected, nor patient data. However, do collect information regarding disease in order to expand medical data bases overall.
• Financial companies use synthetic data to improve fraud detection.
• Automobile manufacturers utilize it for expanding the knowledge base of self-driving cars.

It isn’t new knowledge that Amazon is using synthetic data to train Alexa's language system, knowledge, and response base.

Google has been teaching itself translation for years, and now you can speak on a device to your foreign friend in English and it is translated in real time to their language and vice versa.

Why is synthetic data necessary?

As mentioned, synthetic data is implemented when data that might take years to collect, is not available.

A recent example was when testing immunization amalgams for COVID-19. There was very little to no real-world data to use to test the compounds. Researchers could only gather information as it was happening and kept expanding on hypothetical scenarios using authentic data as time went on. Gathering real-world data is the most time-consuming element of any project.

When real-world data cannot be used due to privacy concerns or compliance risks such as military or HEPA, synthetic versions are compiled which can then be safely shared without fear of breaching those privacy protocols.

Entities such as hospitals allocate data for medical research through shareable synthetic data. Confidentiality of live patient data should not be an issue because no actual patient data is intended for utilization.

Isn’t Artificial Intelligence and Synthetic Data the Same Thing?

Synthetic data and artificial intelligence are separate processes.

Synthetic data is information used in place of real-world data to train AI models.

Synthetic data produces a smarter AI because it is constructed around perfect parameters through algorithms and mathematics.

Major events in our lives are increasingly affected and controlled by AI models.

• In many cases, actual humans do not review applications for jobs. Rather, the application and resume are scanned and read by AI programs searching for keywords and phrases the hiring company desires in its applicants.
• Financial institutions sometimes use AI to determine lending approvals. If fed unbiased synthetic data, AI can determine approvals based on an equitable profile system. Two people, man, and woman, apply. They have the same risk factors, income, and collateral parameters. Historically, the man gets the loan and not the woman. But with AI, the machine doesn’t make emotional decisions based on gender, race, age, etc. Only pure data is utilized.
• Diagnosis for health conditions or diseases based on the data the AI machine is supplied with. This data is much more encompassing about symptoms, than that which a human knows or can recall.

What Role Does Synthetic Data Play in Affiliate Marketing

Synthetic Data can provide AI with any information it needs to meet a desired result. With its incredible ability to gather data, it can produce exact results related to trends, what customers think, want, and feel. In turn, companies can gather valuable data through text and sentiment analysis tools by processing thousands of words. This information helps companies make informed decisions.

The scope of synthetic data in affiliate marketing is much more broad than explained here. Suffice it to say it plays a big role in consumer proceedings.

Synthetic data is revolutionizing the AI industry applications wherein they help solve the data shortage problems.

The goal of synthetic datasets is to replicate organic data and establish a baseline.

The advantage of synthetic data is in the exact parameters it can represent. It allows data that would otherwise be dangerous to collect or formulate, to be replicate safely. It is able to generate data which would normally house sensitive information, to be shared without the possibility of revealing personal information in organic data.

In What Way Is Synthetic Data Sinister?

It has its place, but it can also be abused. AI can only function based on the data it is fed.

• Synthetic data is formulated by humans, and that data can be manipulated to serve in nefarious rolls as well as beneficial.
• Data can be manipulated to perform on a bias. Jobs, loans, etc., can be denied based on undetectably manipulated data.
• Data breaches can occur when badly anonymized data is used to train AI. Health information infractions can cause denial of insurance if personal data is accessed, thereby sparking the possible revelation of prior conditions a person may have.

While the world needs synthetic data, we should also be wary of it. The concept of true data equity is still on the board. As with data privacy expect to see government regulations addressing this issue very soon.

Tami