The Impact of AI on Privacy
As artificial intelligence (AI) and machine learning (ML) systems become more powerful, personal data may be misused, disclosed or abused. In our connected world where our personal information is sold for money, it is understandable to worry about how AI might affect our privacy.
To ensure that data remains secure, businesses must understand the implications of using AI and ML technology. They must also put rigid measures in place to protect sensitive information.
AI in Business: The Good and the Bad
AI is already being used to generate insights from large volumes of data. In the business world, it can making predictive analytics a more accurate and streamlined process through automating marketing emails or creating targeted ad campaigns.
Most organizations have the capability to gather massive amounts of data about their employees' activities, interactions with other employees (emails, Teams/Slack/Yammer messages), as well as content activities (create, update, edit, publish, etc.). As a result, AI can help us learn about each user’s knowledge, experience, and working network within and outside their organization, amongst other things.
Businesses can also utilize AI to better manage/promote customers by collecting insights from previously inaccessible databases. The more data an AI system has to process, the more it learns and "grows wiser." Customer relationship management, marketing strategy analysis and sales forecasting, to name a few, are essential areas where automation can be improved.
However, businesses must understand the implications of using AI technology and the potential risks associated with it. If our personal data is being processed by black box algorithms, problems might arise. For example, AI systems can easily disclose sensitive information, be vulnerable to malicious attacks such as deep fake or neural network hacking, and can draw inaccurate conclusions due to the bias of their programmers.
In addition, businesses should also consider how they are using customer data and if they are compliant with data privacy laws such as the General Data Protection Regulation (GDPR).
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In May 2018, the GDPR came into effect in the European Union. This new regulation set out strict rules about how personal data must be collected, used and protected. Businesses that process or store personal data must comply with GDPR — failure to do so can result in substantial fines. The data in question typically relates to the customer side, but the regulation extends to protect employee data as well.
To "harmonize AI regulations," the European Commission proposed an Artificial Intelligence Act in 2021. This rule aims to keep track of the manufacturing, sales and use of AI in the European Union.
This legislation would greatly impact how AI is regulated and used in the EU and how trading partners use AI (wherever data about EU people may be handled). It would allow to impose severe penalties for noncompliance, similar to the General Data Protection Regulation (GDPR).
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“The law assigns applications of AI to three risk categories. First, applications and systems that create an unacceptable risk, such as government-run social scoring of the type used in China, are banned. Second, high-risk applications, such as a CV-scanning tool that ranks job applicants, are subject to specific legal requirements. Lastly, applications not explicitly banned or listed as high-risk are largely left unregulated.” (Source: The Artificial Intelligence Act)
Mandating Explainable AI
While transparency informs consumers about when and how algorithms will be employed, explainability pertains to those algorithms were utilized. According to the GDPR, every automated decision-making process that produces legal or similarly significant effects upon an individual (such as decisions related to job applications, credit ratings and insurance premiums, etc.) must be subject to "human-in-the-loop" review. This protects against unexpected or unfair decisions by incorporating a fraction of due process. To better understand and explain AI, organizations should:
- Isolate the judgments it makes.
- Break down the intricacies of those decisions.
- Establish a mechanism for people to request an explanation.
As AI and ML become more sophisticated, reverse-engineering algorithms become more complex. As a result, the EU has done the right thing (at least in theory) by instituting a "legal effects or comparable significant implications" barrier to limit algorithmic decision-making. This emphasis on explainability should be replicated in the business world. Having someone "in the loop" about decisions that affect people's lives allows computer efficacy to combine with human judgment and empathy as it becomes evident how these sets of attributes compare.
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AI Isn't Going Anywhere: So What's Next?
Whether or not to incorporate AI is no longer the question to ask, it is already deeply woven into our lives. Although AI has both advantages and disadvantages, it is important to consider the implications of its use regarding privacy.
Privacy breaches are serious issues that need to be addressed within higher-level organizations. With businesses, governments and the military increasingly using AI, public concern regarding privacy has grown. In a world where third parties may easily acquire and exploit personal information, our personal information must be protected at all costs. This is especially pertinent as AI becomes more prevalent in our daily lives.
With AI on the rise, it is important to ensure that technological advances are made responsibly. We must ensure that data is collected and used ethically with full transparency into how algorithms are trained and utilized. As technologies like AI continue to evolve, governments and businesses must remain careful in upholding the right to privacy and ensuring that citizens are empowered to control their own data.
Privacy is a fundamental right, and AI should never be used to violate it.
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About the Author
Agnes Molnar is the CEO and managing consultant of Search Explained. Agnes is an internationally-recognized expert in the fields of modern search applications, information architecture, and Microsoft technologies.