AI is becoming more prevalent in daily life, with many homes using voice assistants and smart appliances. Healthcare providers use AI-driven software for diagnostic and treatment planning. By 2025, the global AI market is expected to have an annual revenue of $126 billion. However, privacy remains a concern with AI, as large amounts of data can make them targets for privacy breaches. Businesses must regulate AI programming and establish a clear governance framework to address these risks. This article discusses challenges and solutions for governing data privacy and AI systems.

How does AI affect data privacy?

Several industries, including health, financial services, fast food, and real estate, are rapidly adopting AI technologies. AI’s well-established algorithms exhibit and automate human intelligence, allowing for the innovation of business and operating models.

However, privacy has not been a priority in the development of many AI technologies. The processing of AI largely depends on using large amounts of data, which poses a risk of infringing on individuals’ privacy. Without appropriate safeguards and regulatory assurances, privacy concerns are growing among organizations.

As a result, AI is facing several data privacy challenges despite its enormous potential. These challenges include

Private data collection 

AI needs large data sets to produce accurate outcomes. The arrival of key technologies such as smartphones, surveillance cameras, and the internet has made it easier to collect data in multiple formats. However, this also increases the risk of tracking private information as users often transmit data to cloud computers. Additionally, AI systems and robots have sensors that can generate and collect data without the user’s knowledge or consent.

Give your website visitors the right to opt out of cookies

Set up a cookie consent banner for GDPR & CCPA compliance and allow users their right to give consent to use cookies.

Try free cookie consent

*Free 14-day trial. *Cancel anytime.

Even if users want to remain anonymous, knowledgeable individuals can use AI methods to identify them. One problem is that it is difficult to determine what is personal data and what is not, putting anyone’s private data at risk and making them easily identifiable.

Repurposing of data

AI technologies have the ability to uncover patterns and relationships that humans cannot detect. This means they can reveal new potential uses for the information they collect. However, users may not be aware that AI algorithms can repurpose the details they disclose on multiple occasions.

Even if the purpose of collecting personal data is clearly stated, there is still a risk of excessive data collection beyond what is necessary. The ability of AI to infer information and make predictions can pose an unintended threat to an individual’s privacy. Users often do not have complete control over how AI software uses their personal data, which can make them vulnerable to data exploitation.

Constant surveillance

The development of digital assistants has enabled constant observation of people, places, and things through the use of cameras and sensors in homes, offices, and public areas. While this technology can be beneficial, it can also violate an individual’s privacy. For example, the data collected by home security systems may be used to observe users in ways that are unknown to them.

Re-identification attacks

AI software masks direct identifiers to help protect an individual’s privacy, but it’s not enough. Users can identify individuals from masked data. They may assume their online behavior is anonymous, but alarmingly, AI systems can track their behavioral patterns across different devices.

Data-based discrimination

The output of AI can result in discrimination against particular groups or individuals. This is because developers train AI algorithms on existing data, replicating biased patterns and assumptions. AI systems automate and perpetuate those biased models, leading to misclassification and negative judgment of specific groups of people. Without the user’s knowledge, AI-generated data creates additional privacy concerns. 

Here are the types of biases commonly associated with AI technologies:

  • Implicit bias: This type of bias happens when AI assumptions are developed based on personal experiences that are not applicable to the general population. It’s dangerous because the user is unaware of the discrimination. 
  • Sampling bias: This occurs when the selected data doesn’t accurately reflect the distribution of the population. The sample training data may overrepresent or underrepresent certain groups of people.
  • Temporal bias: This type of bias occurs when the data doesn’t consider possible future events. This makes the model used may eventually become obsolete due to future changes not factored in while building the data set. 

Why do we need AI governance?

Organizations are using AI algorithms to make significant decisions, such as determining eligibility for housing or setting insurance prices. However, because AI relies on data from any source to generate insights, it is vulnerable to data privacy attacks. Therefore, it is more important than ever to govern or control the behavior of these algorithms. This is where AI governance comes into play.

AI governance is the process of developing a framework to ensure the responsible creation, use, and deployment of AI systems within an organization. Its goal is to allow businesses to take advantage of AI while minimizing its costs and risks. Without proper AI governance, AI projects are unlikely to succeed.

To incorporate ethics and values within AI systems, it is important to understand the various roles involved in the AI lifecycle and to clearly define the responsibilities of each stakeholder, including the business owner, data scientist, model validator, and AI operations engineer.

Proper AI governance can help organizations protect data privacy and security. In addition, stakeholders can benefit from the following aspects of AI governance:

Ensure AI transparency

Many AI algorithms are considered “black boxes”, meaning their process are obscure and unknown. This creates trust issues as users typically demand explanations and clarity about the data used. However, with strong AI governance, enterprises can establish controls to enforce governance principles and provide transparency into the inner workings of AI. This allows organizations to design external control mechanisms to modify the output generated by AI, ensuring greater transparency in AI processes and functions. 

Make risk-aware business decisions

When developers make decisions about AI systems, they often optimize them to optimize for a single metric, potentially ignoring other critical concerns such as the data used to inform those decisions. Organizations should adopt a risk-based approach to ensure appropriate decision-making by developing and establishing a framework for AI governance. This will allow for human involvement in AI-augmented decision-making and enable businesses to access data more effectively and make risk-aware decisions.

Minimize unintended bias

AI algorithms being equipped with pre-existing patterns and biases may exhibit ethics-unaware and data-induced behavior that is partly beyond the organization’s control. AI governance will enable businesses to set boundaries and build responsibility in the system to ensure that the algorithm is as unbiased and representative as possible.

Enforce consistent policies

Consistent principles are critical in developing a trustworthy AI system. Governed AI makes businesses establish and follow an ethical framework to provide clarity on policies, standards, and individual roles. This makes them ensure that the datasets used are responsibly labeled and refined. 

AI governance implementation challenges

Developing an ethical framework is the best way to address AI and data privacy issues. However, implementing an AI governance framework comes with critical challenges that organizations must know and address to attain the benefits of AI governance. Below are a few common challenges various industries encounter while 9implementing AI governance.

Regulations and expectations

Appropriate regulation is key to maximizing the benefits and minimizing the privacy risks of AI technologies. Organizations must deploy specific governance protocols when adopting AI technologies. However, varying AI-related regulations across countries is a challenge. 

The growing number of disparate AI regulations makes implementing and scaling AI more challenging. This is particularly true for global entities governed by diverse requirements. Such rules set by policymakers can affect how businesses respond to ethical issues, such as bias, safety, privacy, and transparency. 

How do GDPR and CCPA govern the use of data for AI?

The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) regulate the use of artificial intelligence (AI) in the following ways:

  • Data protection: Both GDPR and CCPA require companies to protect personal data collected through AI systems and ensure that it is not used in a way that infringes on individuals’ privacy rights.
  • Transparency: Companies must be transparent about how they use AI, including how data is collected, processed, and used. This includes providing clear and concise information about the purpose of the AI system and the rights of individuals to access, correct, or delete their data.
  • Data minimization: Companies must only collect and process data that is necessary for the intended purpose of the AI system, and must delete or destroy data when it is no longer needed.
  • Fairness: Companies must ensure that their AI systems do not discriminate against individuals based on protected characteristics such as race, gender, or age.

The Artificial Intelligence Act is a proposed legislation in the European Union that aims to regulate the use of AI in the EU. The Act aims to establish a framework for the development and use of AI, including requirements for transparency, accountability, and ethics. The Act is still in the development stage and is not yet in effect.

Third-party technology risks

The dependence on third-party applications for AI deployment is critical for scalability, but it also means sharing valuable company data. This can pose security threats to the privacy of confidential and security information and also create challenges in relationship-lifecycle risk monitoring. 

Three lines of defense

The three lines of defense in AI pertain to health, performance, and safety, which are crucial for ensuring the overall performance of the system. Unfortunately, training and awareness often only focus on the first line of defense, which allows data scientists to detect ethical issues. However, in order to ensure responsible AI, it is necessary to also implement the second and third lines of defense. Without these, the AI system may not meet sufficient oversight, risk, and compliance requirements, and may lead to unnecessary exposure to privacy risk and impulsive decision-making.

Human-AI collaboration

People are central to the development of AI systems, but determining their role can be challenging. While human involvement helps ensure good decision-making, there are concerns about bias and errors that come with traditional human decision-makers. It is also unclear at what point people should intervene and what their role should be in the collaboration process.

Solutions for data privacy concerns in AI governance

Implementing governance in the entire process, from development to deployment of AI systems, is challenging. Fortunately, organizations can resolve data privacy and AI governance concerns by taking the following steps: 

Proactively manage and monitor AI systems

Proactive management and monitoring of AI systems are critical for early detection of data privacy and other ethical issues, enabling remediation actions to speed up troubleshooting and minimize costly network downtime. To adequately address potential AI risks, it is important to have monitoring and oversight procedures in place. Creating an inventory of all AI systems employed and the specific uses of such systems is an excellent first step. 

Employ compliance, fair, and system governance teams

Automated systems cannot fully replace the knowledge and experience of humans, and a certain degree of manual review may be necessary to ensure that AI systems are unbiased and trustworthy. This manual review can serve as the first line of defense against potential discrimination and bias in AI systems.

Organizations can employ compliance, fairness, and system governance teams to evaluate input variables. If working with an internal tech team, education and training can help ensure the responsible use of AI. If working with an external IT team, a stringent screening process must be in place to ensure compliance.

Pay particular attention to regulatory development

Besides internal organizational policies and standards, you should also consider external regulatory requirements when governing AI. Industries, particularly those that are highly regulated, such as financial institutions, should pay special attention to regulatory developments. Otherwise, they risk fines, penalties, and reputational harm.

Leverage data governance tools to mitigate risks

A strategy for managing AI-based data is essential in order to mitigate potential privacy risks. The good news is that organizations can leverage data governance tools to exercise control over their most valuable asset: data. Some data governance platforms and software have features that automatically preserve privacy and comply with the laws and regulations of the area in which they operate.

Continuously improve data privacy and AI practices

It is possible to realize the technological benefits of AI without infringing on user privacy. If your business decides to adopt AI technologies, it is important to take data privacy seriously. Ensure compliance with current and future rules and regulations. As you move forward, you will likely need to address new risks and ethical considerations.

Disclaimer: This article is for general informational purposes only and should not be taken as legal or professional advice. The views and opinions expressed in this article are solely those of the author and do not necessarily reflect the views of our organization. We do not endorse any products or services mentioned in the article.