AI and Data Privacy: Compatible, or at Odds?

Yinglian Xie Yinglian Xie
May 21, 2020 AI & Machine Learning

Data privacy is a controversial topic today, and as new regulations emerge globally to protect consumer data, that’s not likely to change. The GDPR in Europe and the CCPA in California are setting new standards for protecting consumer data and giving consumers more rights over the collection and use of their personal information.

At the same time, they’re raising awareness among consumers about how their data is being used, sometimes without their knowledge.

A parallel trend that’s some believe is at odds with data privacy is Artificial Intelligence (AI). AI is increasingly being used to in our daily lives, and its usefulness depends on the three “Vs” of big data—volume, variety and velocity. Curtailing access to data can impact AI’s effectiveness. 

Influential leaders such as Alphabet CEO Sundar Pichai have expressed their belief that AI must be regulated, but it’s important to take into consideration not only the concerns around AI but the benefits it offers. The ultimate question we have to address is, how do we leverage the best of what AI has to offer without compromising data privacy? 

Why Is Consumer Trust Broken?

In 2020, there will be 40 trillion gigabytes of data, and every person will generate 1.7 megabytes in just one second. Privacy advocates often clash with technology vendors and marketers. The vast majority of businesses — 97.2%—are spending money to use AI to hone marketing outreach, segment target audiences, tailor advertising and offers, and generate relevant content and experiences that lead to high conversion rates. Consumers know this, and they’re reacting with mixed emotions. These emotions evolve around one core requirement: Trust.

Consumers also know that organizations may share or sell data without consumers’ knowledge, and high-profile violations of trust such as the Cambridge Analytica scandal exacerbate the mistrust that consumers feel toward the organizations acquiring and using their data. 

Building Trust with Value

When it comes to providing our personal data, consumers usually have two fundamental prerequisites for trusting the process: 

  1. They receive meaningful value in exchange for sharing their personal data.
  2. They understand how their data will be protected and that it will only be used for the stated purpose. 

The level of understanding around the value exchange varies depending on demographics. A consumer’s age, nationality, culture or circumstance all impact on how they view privacy and the boundaries they set around sharing data. But the majority of consumers understand that to access the full functionality of their apps and services requires sharing some amount of personal information. 

For example, without access to location data, the GPS functionality on a smartphone won’t work. If you don’t provide your routing and account numbers to PayPal or Venmo, you can’t electronically transfer money. Ordering goods on Amazon requires a delivery address, phone number and other personal information. Consumers are willing to provide this data, because of the perceived value of the exchange and because they trust the organization will deliver on the promise of that value.

With AI, the value exchange isn’t always obvious, and when it’s not, trust can break down. Are there ways to leverage datasets to inform AI-powered technologies without compromising data privacy—or consumer trust?

Data for AI: Less is More

Several emerging technologies enable the sharing of large datasets with anonymity:

  • Differential privacy systems focus on ways to share datasets of personal information without exposing recognizable details about the individuals to which the data belongs. In this way, it’s possible to derive valuable insights from data without compromising data privacy. 
  • Multi-party computation is another advanced approach to using personal data in a way that keeps certain aspects of that data concealed. 
  • Zero-factor authorization offers a solution that involves building a digital DNA for an individual using data from online behaviors. This new authentication paradigm can be highly effective and secure without compromising privacy, by adhering to robust data collection, continuous data analysis and total transparency.
  • Unsupervised machine learning can accurately monitor user account behavior, without the need to acquire extensive personal data about individual users. By deploying sophisticated graphing and clustering techniques, UML enables the observation and detection of known and unknown patterns, uncovering connections that help determine with a high degree of accuracy whether a given action or set of actions is legitimate or fraudulent. UML is similar to the differential privacy approach, in that it can draw conclusions from data about aggregated user actions and behaviors, without having to violate the privacy of individual users.  

Such technologies are making it possible to embrace a “less is more” approach when it comes to collecting and using data, yielding powerful results for organizations without having the adverse effect of eroding consumer trust.

Restoring Trust in the Digital Age

The path forward for restoring consumer trust requires full transparency as to what data organizations collect and how they use it. Using transformational technologies and techniques such as unsupervised machine learning, we can leverage AI to a greater extent for the benefit of consumers and the world at large, while keeping data privacy intact.

Data privacy is a controversial topic today, and as new regulations emerge globally to protect consumer data, that’s not likely to change. The GDPR in Europe and the CCPA in California are setting new standards for protecting consumer data and giving consumers more rights over the collection and use of their personal information.

At the same time, they’re raising awareness among consumers about how their data is being used, sometimes without their knowledge.

A parallel trend that’s some believe is at odds with data privacy is Artificial Intelligence (AI). AI is increasingly being used to in our daily lives, and its usefulness depends on the three “Vs” of big data—volume, variety and velocity. Curtailing access to data can impact AI’s effectiveness. 

Influential leaders such as Alphabet CEO Sundar Pichai have expressed their belief that AI must be regulated, but it’s important to take into consideration not only the concerns around AI but the benefits it offers. The ultimate question we have to address is, how do we leverage the best of what AI has to offer without compromising data privacy? 

Why Is Consumer Trust Broken?

In 2020, there will be 40 trillion gigabytes of data, and every person will generate 1.7 megabytes in just one second. Privacy advocates often clash with technology vendors and marketers. The vast majority of businesses — 97.2%—are spending money to use AI to hone marketing outreach, segment target audiences, tailor advertising and offers, and generate relevant content and experiences that lead to high conversion rates. Consumers know this, and they’re reacting with mixed emotions. These emotions evolve around one core requirement: Trust.

Consumers also know that organizations may share or sell data without consumers’ knowledge, and high-profile violations of trust such as the Cambridge Analytica scandal exacerbate the mistrust that consumers feel toward the organizations acquiring and using their data. 

Building Trust with Value

When it comes to providing our personal data, consumers usually have two fundamental prerequisites for trusting the process: 

  1. They receive meaningful value in exchange for sharing their personal data.
  2. They understand how their data will be protected and that it will only be used for the stated purpose. 

The level of understanding around the value exchange varies depending on demographics. A consumer’s age, nationality, culture or circumstance all impact on how they view privacy and the boundaries they set around sharing data. But the majority of consumers understand that to access the full functionality of their apps and services requires sharing some amount of personal information. 

For example, without access to location data, the GPS functionality on a smartphone won’t work. If you don’t provide your routing and account numbers to PayPal or Venmo, you can’t electronically transfer money. Ordering goods on Amazon requires a delivery address, phone number and other personal information. Consumers are willing to provide this data, because of the perceived value of the exchange and because they trust the organization will deliver on the promise of that value.

With AI, the value exchange isn’t always obvious, and when it’s not, trust can break down. Are there ways to leverage datasets to inform AI-powered technologies without compromising data privacy—or consumer trust?

Data for AI: Less is More

Several emerging technologies enable the sharing of large datasets with anonymity:

  • Differential privacy systems focus on ways to share datasets of personal information without exposing recognizable details about the individuals to which the data belongs. In this way, it’s possible to derive valuable insights from data without compromising data privacy. 
  • Multi-party computation is another advanced approach to using personal data in a way that keeps certain aspects of that data concealed. 
  • Zero-factor authorization offers a solution that involves building a digital DNA for an individual using data from online behaviors. This new authentication paradigm can be highly effective and secure without compromising privacy, by adhering to robust data collection, continuous data analysis and total transparency.
  • Unsupervised machine learning can accurately monitor user account behavior, without the need to acquire extensive personal data about individual users. By deploying sophisticated graphing and clustering techniques, UML enables the observation and detection of known and unknown patterns, uncovering connections that help determine with a high degree of accuracy whether a given action or set of actions is legitimate or fraudulent. UML is similar to the differential privacy approach, in that it can draw conclusions from data about aggregated user actions and behaviors, without having to violate the privacy of individual users.  

Such technologies are making it possible to embrace a “less is more” approach when it comes to collecting and using data, yielding powerful results for organizations without having the adverse effect of eroding consumer trust.

Restoring Trust in the Digital Age

The path forward for restoring consumer trust requires full transparency as to what data organizations collect and how they use it. Using transformational technologies and techniques such as unsupervised machine learning, we can leverage AI to a greater extent for the benefit of consumers and the world at large, while keeping data privacy intact.

  • Experfy Insights

    Top articles, research, podcasts, webinars and more delivered to you monthly.

  • Yinglian Xie

    Tags
    Data PrivacyUnsupervised Machine Learning
    Leave a Comment
    Next Post

    Data Security in the Cloud

    Leave a Reply Cancel reply

    Your email address will not be published. Required fields are marked *

    More in AI & Machine Learning
    AI & Machine Learning,Future of Work
    AI’s Role in the Future of Work

    Artificial intelligence is shaping the future of work around the world in virtually every field. The role AI will play in employment in the years ahead is dynamic and collaborative. Rather than eliminating jobs altogether, AI will augment the capabilities and resources of employees and businesses, allowing them to do more with less. In more

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    How Can AI Help Improve Legal Services Delivery?

    Everybody is discussing Artificial Intelligence (AI) and machine learning, and some legal professionals are already leveraging these technological capabilities.  AI is not the future expectation; it is the present reality.  Aside from law, AI is widely used in various fields such as transportation and manufacturing, education, employment, defense, health care, business intelligence, robotics, and so

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    5 AI Applications Changing the Energy Industry

    The energy industry faces some significant challenges, but AI applications could help. Increasing demand, population expansion, and climate change necessitate creative solutions that could fundamentally alter how businesses generate and utilize electricity. Industry researchers looking for ways to solve these problems have turned to data and new data-processing technology. Artificial intelligence, in particular — and

    3 MINUTES READ Continue Reading »

    About Us

    Incubated in Harvard Innovation Lab, Experfy specializes in pipelining and deploying the world's best AI and engineering talent at breakneck speed, with exceptional focus on quality and compliance. Enterprises and governments also leverage our award-winning SaaS platform to build their own customized future of work solutions such as talent clouds.

    Join Us At

    Contact Us

    1700 West Park Drive, Suite 190
    Westborough, MA 01581

    Email: support@experfy.com

    Toll Free: (844) EXPERFY or
    (844) 397-3739

    © 2023, Experfy Inc. All rights reserved.