AI and Privacy: Key Concerns for Organizations and Users

Some of the world’s leading technology companies, including Google, OpenAI, and Microsoft, are fearlessly embracing AI. These platforms are creating generative content that’s difficult to distinguish from the real thing, including graduate-level essays and highly customized search engine results.

But, while AI models are clearly valuable—streamlining workflows and squashing writer’s block—regulatory bodies must establish parameters regarding data security and privacy issues for technology.

Read on to learn about the relationship between AI and privacy and discover how to safeguard sensitive information.

Where does AI get its data?

AI models continue to evolve with the help of extensive datasets broadly categorized into the following primary sources:

• Structured data: This includes information you can arrange into established categories that are easy to locate. Examples are spreadsheets, customer profiles, and transaction records, which are all clearly labeled.
• Unstructured data: Uncategorized information, like social media posts, voice recordings, and meeting notes, is unstructured. This source type doesn’t have predefined labels, making it more difficult for AI to interpret than structured sets.
• Semi-structured data: This includes information with tags or labels that still need to be organized into categories. Emails, logs, and JSON documents are all semi-structured and loosely sorted, putting them somewhere between structured and unstructured data. 
• Streaming data: Streaming data is information AI models collect from real-time sources, such as live streams, social media feeds, and internet-enabled devices.

How do AI technologies collect data? 

AI-powered tools collect data from the four above sources in two ways—directly from intentional sources and indirectly from unintentional sources:

• Direct data collection involves gathering data from people who intentionally provide it. For example, when someone fills out an online form, submits a survey, or enters login information, they directly contribute data by interacting with that channel. AI programs collect and study this information through natural language processing (NLP) and machine learning (ML).
• Indirect data collection occurs without explicit input or people realizing they’re providing data. AI programs track activities like facial recognition attempts, website cookies, and GPS locations—typically for improved ad targeting and more personalized marketing content.

The AI analytics process

After collecting raw data from various sources, AI models turn it into meaningful information humans can understand. This occurs in three stages:

1. Cleaning: Also called data pre-processing, cleaning is when AI scientists program their models to check large, raw data volumes for errors. The goal is to ensure the data is accurate and suitable for analysis before moving to the next stage.
2. Processing: After cleaning, the programmed algorithms manipulate the data to make it analysis-ready. These algorithms usually extract relevant information through the following techniques:
   • Normalization standardizes all data collected from various sources for straightforward analysis.
   • Feature engineering creates new features (or changes existing ones) within the AI model to improve its performance.
   • Dimensionality reduction makes data less complex by removing redundant information.
3. Analyzing: Finally, data scientists and engineers incorporate algorithms and statistical techniques like NLP and ML to help make sense of the data. AI programs look for patterns and trends, use ML for predictive modeling (predicting outcomes based on historical data), and study the relationship between different sources. In essence, these programs are breaking down complicated data and presenting it clearly for human comprehension.

Data privacy issues in AI

As valuable as AI-based technology is, here are a few privacy concerns that regulatory bodies and corporations must address to secure data.

Unauthorized use of personal information

AI model training involves processing large datasets to identify and learn from patterns. Programmers collect, curate, and then feed their dataset to the model. But it’s often collected from publicly available sources or through partnerships with data providers. 

While AI systems don’t autonomously navigate the web for information, the data used to train them might include sensitive information (if that information wasn’t properly anonymized or secured). This could lead to unauthorized access to or misuse of customer databases or proprietary information if this data falls into the wrong hands.

Overlooking copyright and IP laws

AI models comb through massive datasets that often include copyrighted information. Training an AI model on this information might not be an issue if you’re in a jurisdiction that allows for fair use. This becomes a problem if an AI-powered platform reproduces or closely mimics copyrighted content without permission.

A lack of built-in security features

Many cybersecurity-focused organizations use AI to bolster existing security systems, with one study finding a 15% decrease in the median dwell time (the time between a hacker infiltrating a system and AI detecting it).

But there are no globally accepted and implemented built-in security features for every AI application. Without these guardrails, it’s easier for criminals to exploit AI programs to steal sensitive or proprietary data, automate cyber attack-related tasks, and manipulate training algorithms for unethical reasons.

What does responsible AI look like?

When authoritative bodies like research development organizations, governments, and AI-focused tech companies take AI-related privacy concerns seriously, everyone benefits.

Here’s what ethical AI looks like.

1Guidelines for privacy and security

Governments must put data privacy and security first by defining clear safe-use policies and then regularly auditing companies for compliance. This step is in the works—for instance, the EU Artificial Intelligence Act is in the final drafting stages, endorsed by all 27 member states.

While we wait for generalized policy-making, AI-focused organizations can define their own (ideally comprehensive and consumer-focused) standards. For instance, Fellow establishes extensive security standards and ensures compliance with GDPR and SOC II.

2Transparency and accountability

Governments and organizations must be transparent about how their AI uses personal data. If an AI-powered tool misuses information or it ends up in the wrong hands, citizens should be able to hold these responsible bodies accountable.

Opt for platforms with thorough and easily accessible documentation. This documentation should outline how each feature works, the data it collects, where that data comes from, and where it’s stored. And any updates or changes regarding data acquisition, storage, and privacy should be clearly laid out.

If a privacy-related issue occurs, transparent and privacy-focused organizations will hold themselves accountable and immediately address the problem. Even minor hiccups offer these companies a chance to correct the issue and rebuild trust.

3Positive contributions to society

AI’s purpose is to simplify our lives. But a tool that causes more problems than it solves may not align with ethical AI practices.

Governments and organizations must consider whether their use of AI hinders social progress regarding safety and equity, looking closely at whether a tool might:

• Discriminate against specific groups
• Promote marginalization
• Violate human and social rights
• Worsen existing biases

4Bias-free data collection and processing

Governments should enforce fairness laws and fund research that uncovers and fixes bias in AI programs. This high-level approach encourages companies to develop and apply AI technologies justly, safeguarding against discrimination.

Companies can contribute by diversifying their data, including a broad range of human experiences when training AI systems. And your team can look for platforms that are transparent about actively detecting and correcting biases. Together, following high-level guidelines and popularizing more equitable platforms leads to fairer outcomes for all.

5Updated policies and regular testing

Governments should regularly update guidelines by staying in tune with new tech developments and running checks on AI systems to make sure they’re secure.

And AI-powered platforms should follow these rules closely and keep testing their AI to catch and fix safety and ethical issues. Keeping things current means fairer AI for everyone.

To be proactive, create an AI-auditing team and testing plan that outlines how and when to audit your tech stack for issues and outdated security practices.

Final tips for easing AI-focused privacy concerns

For the most part, AI tools are designed to help. Here are a few ways to improve your team’s experience:

• Use data governance tools: Tools like Atlan, Alation, and Collibra are all trusted AI-powered platforms that secure your data, scrubbing it for personally identifiable information and maintaining it in an accessible database for your whole organization.
• Don’t input sensitive information: Any information your employees offer an AI program becomes part of its database. Establish a zero-tolerance policy for inputting personal data into any platform not fully vetted by your legal team.
• Read the documentation: Most reputable AI tools offer detailed online documentation. Read it thoroughly and have your legal team search for vulnerabilities and privacy concerns.

Improve privacy and productivity with Fellow

Incorporating responsible principles when developing AI can help organizations feel more comfortable integrating it into daily operations. Start by using an AI-powered meeting solution like Fellow for recordings, summaries, and note-taking to improve productivity and data security.Fellow ensures your sensitive discussions remain confidential without missing essential details. Try it today and enjoy privacy-optimized meetings.