Why AI design must prioritize data privacy

• Artificial intelligence is integral to the development of health care, technology and other fields, but there are concerns about how data privacy will be regulated.
• Data privacy is critical to gaining public trust in technological advancement.

Data privacy is often associated with artificial intelligence (AI) models based on consumer data. Users are understandably wary of automated technologies that capture and use their data, which may include sensitive information. Because AI

models rely on data quality to deliver significant results, their continued existence depends on privacy protection being an integral part of their design.

Good privacy and data management practices are more than just a way to allay customer fears and concerns. They have a lot to do with a company's core organizational values, business processes, and security management. Privacy issues have been widely researched and publicized, and privacy perception survey data show that privacy protection is an important issue for consumers.

It’s critical to address these issues in context, and for companies using consumer-facing AI, there are several methods and techniques that can help address the privacy concerns commonly associated with AI.

Some products and services require data, but they don’t need to invade anyone’s privacy

Businesses using artificial intelligence are already facing public doubts about privacy. According to a 2020 survey by the European Consumer Organization, 45-60% of Europeans agree that AI will lead to more misuse of personal data.

There are many popular online services and products that rely on large data sets to learn and improve their AI

algorithms. Some of the data in these datasets may be considered private to even the least privacy-conscious user. Streams of data from the web, social media pages, mobile phones and other devices increase the amount of information businesses use to train machine learning systems. Privacy protection is becoming a public policy issue around the world due to the overuse and mismanagement of personal data by some businesses.

Most of the sensitive data we collect is used to improve AI-enabled processes. Much of the data analyzed is also driven by machine learning adoption, as complex algorithms are required to make decisions in real time based on these data sets. Search algorithms, voice assistants, and recommendation engines are just a few of the solutions that leverage

AI based on large datasets of real-world user data.

Massive databases may contain a wide range of data, and one of the most pressing issues is that this data may be personally identifiable and sensitive. In fact, teaching an algorithm to make decisions does not rely on knowing who the data relates to. Companies behind such products should therefore focus on privatizing their datasets with few ways to identify users in the source data, and developing measures to remove edge cases from their algorithms to avoid reverse engineering and identification.

The relationship between data privacy and artificial intelligence is very delicate. While some algorithms may inevitably require private data, there are ways to use it in a more secure and non-intrusive way. The following methods are just some of the ways companies working with private data can become part of the solution.

AI Design with Privacy in Mind

We have discussed the problem of reverse engineering, in which bad actors discover vulnerabilities in an AI

model and extract from the model’s output Identify potentially critical information. Reverse engineering is why changing and improving databases and learning data in the face of this challenge is critical for AI use.

For example, combining conflicting data sets in a machine learning process (adversarial learning) is a good option for distinguishing flaws and biases in the output of an AI

algorithm. There are also options for using synthetic datasets that do not use actual personal data, but there are still questions about their effectiveness.

Healthcare is a pioneer in artificial intelligence and data privacy governance, especially when dealing with sensitive private data. It also does a lot of work on consent, whether for medical procedures or the processing of their data - the stakes are high and it's enforced by law.

For the overall design of AI products and algorithms, decoupling data from users through anonymization and aggregation is key for any enterprise that uses user data to train its AI models.

There are many considerations that can enhance privacy protection for AI companies:

• Put privacy at the core: Put privacy protection on developers’ radar and find ways to effectively strengthen security.
• Anonymize and aggregate data sets, removing all personal identifiers and unique data points.
• Strictly control who in the company has access to specific data sets and continually audit how this data is accessed, as this has been the reason behind some data breaches in the past.
• More data is not always the best solution. Test your algorithm with minimal data to understand the minimum amount of data you need to collect and process to make your use case feasible.
• A simplified method must be provided to eliminate personal data upon user request. Companies that only pseudo-anonymize user data should continually retrain their models using the latest data.
• Leverage powerful de-identification strategies, such as aggregated and synthetic datasets with fully anonymized, irreversible identifiers for algorithm training, auditing, quality assurance, and more.
• Protect user autonomy and privacy by rethinking how critical information is obtained and used from third parties - scrutinize data sources and only use those that collect data with the user’s explicit and informed consent .
• Consider the risks: Could an attack compromise user privacy from your AI system output?

What is the future of data privacy and artificial intelligence?

Artificial Intelligence Systems require massive amounts of data, and some of the top online services and products cannot function without the personal data used to train AI algorithms. However, there are many ways to improve the acquisition, management and use of data, including the algorithms themselves and overall data management. Privacy-respecting AI requires privacy-respecting companies.

The author of this article: Einaras von Gravrock, CEO and founder of CUJO AI

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