How to Handle AI in Protecting Customer Privacy

Customer Privacy

Customer data is important for customer and business. With the help of the data available, companies can offer seamless services to its customers and increase their experience in the long run. They love it when they get personalized recommendations by AI. They love it when something is automated, and that makes their job quicker and easier. Customers also love it when companies respect their privacy.

When companies operate with shared values and work on taking measures to protect customer privacy, product teams and researchers can provide customers with information about privacy and personalization. Several companies practice collaborative work to ensure that customer service is delivered right, and every designing professional understands the importance of customer privacy.

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Here is how AI is helping businesses protect customer privacy:

Gathering Right Data

The more features a dataset holds, the more effort is necessary to ensure that the privacy of the same is maintained. To collect and use the data that aligns with your company’s values and your customer’s demands is very important.

Therefore, when you are developing an AI that works on customer data, you need to ask yourself what features are responsible for the model’s performance and how important are these features. One must also have a clear justification for the requirement of the information.

Here are a few other questions that are important in protecting customer privacy:

  • Does obtaining customer information increase the risks of compromising their anonymity?
  • Are the customers informed about using their data?
  • Is the company collecting and using the data to reflect their customers’ values?
  • Are the customers notified about the purpose of using the data?

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Exploring the features of data without hampering the content or individuals:

AI systems don’t need much information about individuals to make useful predictions for them. Current methods allow data to be aggregated, featurized, and work on anonymity without hampering the computations’ ability.

The important patterns can be retained by adding noise to the data. This noise makes it impossible to trace it back to content or individuals. There are several approaches that make sure queries and AI models give statistical or aggregate results, and not just raw or individuating results.

To make sure that your AI models return statistical results, and you need to ask yourself the following questions:
  • If you run a query on a particular data, is it possible that the results are aligned with a small subset of individual data?
  • Is there a way to ensure your queries are returned statistically and as de-identified results?
  • Can you determine whose data was used in the training set initially?
  • How do you protect the anonymity and encode the data to make sure it can’t be re-traced?

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Managing Customer Data

Today’s technology permits us to address several concerns about where and how the data is being handled. There is no need for customer data to travel to the cloud for AI to work. Several technological advancements have made it easier to get sophisticated models onto a customer’s device without using much of the device’s memory or processing power. Once the AI is deployed in the device, it can function offline, without being constantly connected to the cloud.

With the help of these approaches on providing customers with an AI-driven experience. Lead yourself and your team to provide better user privacy experience that reflects your company’s value. The knowledge acquired with the help of an AI model empowers us, in turn, to help our users navigate privacy in AI-driven products.

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