Privacy and Artificial Intelligence - 1.1 Excessive Data Collection and Lack of Minimization

1.1 Excessive Data Collection and Lack of Minimization

Introduction
Imagine that every time you played with your toys, someone quietly wrote down everything you did, even the parts that had nothing to do with playing. This is what happens when artificial intelligence (AI) programs gather too much information about people (Kiteworks, 2025). Data collection means gathering information, and when AI systems do this, they sometimes take far more than they actually need. Such over-collection hurts privacy, which is the right to keep personal details safe and secret, just like locking a diary so only the owner can read it.

When AI systems collect excessive data, they are taking more personal details than necessary to perform their tasks (GDPR Local, 2025). It is similar to a librarian asking for your name, address, birthday, favorite color, and last meal just to let you borrow a book, when only your name is needed. The rule of data minimization teaches that we should collect only the information truly required, nothing more (Information Commissioner’s Office, 2025).

Technical or Conceptual Background
AI systems learn from data much as children learn letters by seeing examples. Modern AI, however, often craves data far beyond what is essential for its stated purpose (Shaip, 2025). Machine-learning algorithms—sets of instructions that help computers learn from examples—can improve when they have many examples, tempting companies to scoop up every detail they can.

Data minimization is grounded in privacy laws such as the General Data Protection Regulation (GDPR). These laws require personal data to be adequate, relevant, and limited to what is necessary for the intended purpose (Restack, 2025). Personal data covers any information that can identify an individual, including names, addresses, photos, and browsing histories.

The problem grows when AI systems infer or guess new facts from data already collected (LibreTexts, 2024). For instance, by studying someone’s shopping list, an AI might guess a health condition even if no health records were collected. That means even harmless-looking data can lead to privacy risks.

Current Trends and Challenges
Studies report a 56.4 percent jump in AI-related privacy incidents during 2024, reaching 233 documented cases (Kiteworks, 2025). Many involved organizations gathering far more data than their AI tools actually required. At the same time, public trust in companies to protect personal information continues to fall.

Large language models and other generative AI tools are a major driver. They learn from enormous text collections scraped from the internet, which often include private emails, posts, and other sensitive content without permission (Research.AIMultiple, 2025). Firms argue the data makes their models smarter, yet critics note that the harvest plainly ignores minimization.

Behavior prediction systems add to the problem (HSE.AI, 2025). These tools look at patterns—like browsing habits, location pings, or social-media likes—to guess what a person may buy next or how they might vote. Even when the prediction is justified, much of the data is gathered “just in case,” not for a present need.

Internet-of-Things (IoT) devices provide another example (UC Berkeley CLTC, 2018). A smart thermostat only needs your preferred temperature, but some models record when family members arrive and leave home. Such constant monitoring creates an oversized trove of personal information.

Finally, researchers find that about two-thirds of companies use less than half of the data they collect for AI projects (Strategy Software, 2024). The unused half simply waits in storage, raising risks without delivering value.

Mitigation Challenges and Shortcomings
Although awareness is rising, real progress is slower. An internal study found many organizations list “privacy by design” as a priority, yet few have strict collection limits in place (Kiteworks, 2025).

First, AI developers often believe performance improves with more data (Converge TP, 2025). This belief clashes with minimization. Encouragingly, research shows that high-quality, carefully chosen data can match results obtained from massive, messy datasets (Ganesh, 2024).

Second, technical complexity blocks progress. Older systems, built before modern privacy laws, may lack switches that let administrators limit what the software collects (Archyde, 2025). Information is also scattered across separate databases, making it hard to apply one set of privacy rules everywhere.

Third, legal frameworks offer broad principles but limited practical detail for AI implementation (Jackson Lewis, 2025). Faced with uncertainty, organizations sometimes choose to over-collect rather than risk under-collecting data they might someday want.

Finally, data collection habits are woven into business models. Many firms earn money by combining and selling personal information, so shifting to minimal data collection requires cultural change and investment in new tools (LibreTexts, 2024).

Glossary

Term	Meaning and Example Sentence
Data Collection	When computers gather information. Example: “The app performs data collection when it asks for your age.”
Data Minimization	Gathering only what is needed. Example: “The library app uses data minimization by asking only for your name.”
Artificial Intelligence (AI)	Computer programs that can learn and decide. Example: “The AI helper on my tablet answers questions.”
Personal Data	Information that shows who you are. Example: “Your address is personal data.”
Privacy	Keeping personal details secret. Example: “Privacy means no one reads your diary without permission.”
Machine Learning	The way computers learn from examples. Example: “Photo software uses machine learning to spot cats.”
GDPR	European rules that protect personal data. Example: “GDPR says companies must ask before using your information.”
Algorithm	A recipe of steps for a computer to follow. Example: “The algorithm decides which game character appears next.”
Internet of Things (IoT)	Everyday objects that connect to the internet. Example: “My watch is part of the IoT because it sends my steps to my phone.”
Behavioral Analysis	Studying actions to predict what someone might do. Example: “The store’s website uses behavioral analysis to suggest toys.”

Questions

1. What is data minimization, and why does it matter for privacy in AI systems?

2. How have reports of excessive data collection changed recently, and what trend does this show?

3. Name three challenges organizations face when they try to practice data minimization.

4. How can AI systems infer extra personal information from harmless data? Give one example.

5. Why might engineers resist limiting data collection, and how could their concerns be eased?

Answer Key

1. Suggested Answer: Data minimization is collecting only the personal information needed for a specific task, nothing extra (Information Commissioner’s Office, 2025). It matters because it lowers privacy risks, reduces harm from breaches, and respects people’s control over their data (GDPR Local, 2025).

2. Suggested Answer: AI-related privacy incidents rose by 56.4 percent in 2024, reaching 233 cases (Kiteworks, 2025). This shows that despite greater awareness, excessive collection is growing, indicating that current safeguards are not keeping pace with new AI uses (Shaip, 2025).

3. Suggested Answer: First, technical complexity makes it hard to see what data AI truly gathers, especially in older systems (Archyde, 2025). Second, many engineers believe more data means better AI performance, so they hesitate to limit collection (Converge TP, 2025). Third, business models that rely on selling or using large data sets create cultural resistance to minimization (LibreTexts, 2024).

4. Suggested Answer: AI looks for patterns that reveal new facts. For instance, an algorithm that tracks grocery purchases might infer that a shopper is pregnant from buying vitamins and certain clothing even if no health data was provided (LibreTexts, 2024).

5. Suggested Answer: Engineers worry that less data will weaken AI results (Converge TP, 2025). Showing evidence that well-chosen, smaller data sets can deliver equal performance (Ganesh, 2024), teaching privacy-by-design principles, and adopting clear governance rules can reduce these worries (Strategy Software, 2024).

References

Archyde. (2025, June 22). Legacy data: Roadblock to AI. Archyde. https://www.archyde.com/legacy-data-roadblock-to-ai/

Converge TP. (2025, March 25). Top 5 AI adoption challenges for 2025: Overcoming barriers to success. Converge TP. https://convergetp.com/2025/03/25/top-5-ai-adoption-challenges-for-2025-overcoming-barriers-to-success/

Ganesh, P. (2024, May 29). The data minimization principle in machine learning. arXiv. https://arxiv.org/abs/2405.19471

GDPR Local. (2025, January 29). How AI GDPR will shape privacy trends in 2025. GDPR Local. https://gdprlocal.com/ga/how-ai-gdpr-will-shape-privacy-trends-in-2025/

HSE.AI. (2025). AI behavior analysis for workplace safety. HSE.AI. https://hse.ai/en/blog-detail/ai-behavior-analysis/

Information Commissioner’s Office. (2025, April 22). How should we assess security and data minimisation in AI? ICO Guidance. https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/how-should-we-assess-security-and-data-minimisation-in-ai/

Jackson Lewis. (2025, January 16). The year ahead 2025: Tech talk — AI regulations + data privacy. Jackson Lewis. https://www.jacksonlewis.com/insights/year-ahead-2025-tech-talk-ai-regulations-data-privacy

Kiteworks. (2025, April 24). AI data privacy wake-up call: Findings from Stanford’s 2025 AI index report. Kiteworks Blog. https://www.kiteworks.com/cybersecurity-risk-management/ai-data-privacy-risks-stanford-index-report-2025/

LibreTexts. (2024, June 1). Where does all the data go? LibreTexts. https://socialsci.libretexts.org/Bookshelves/Education_and_Professional_Development/Teaching_AI_Ethics:_Practical_Strategies_for_Discussing_AI_Ethics_in_K-12_and_Tertiary_Education_(Furze)/07:_Teaching_AI_Ethics-_Datafication/7.02:_Where_does_all_the_Data_go

Research.AIMultiple. (2025, June 27). AI data collection: Risks, challenges & tools in 2025. AIMultiple. https://research.aimultiple.com/ai-data-collection/

Restack. (2025, January 21). Understanding GDPR in AI applications. Restack.io. https://www.restack.io/p/gdpr-compliance-answer-ai-applications

Shaip. (2025, February 10). AI training data: Benefits, challenges, example [2025]. Shaip. https://www.shaip.com/blog/the-only-guide-on-ai-training-data-you-will-need-in/

Strategy Software. (2024, May 29). Bridging the AI data gap: How to optimize underutilized data. Strategy Software. https://www.strategysoftware.com/zh/blog/bridging-the-ai-data-gap-how-to-optimize-underutilized-data

UC Berkeley Center for Long-Term Cybersecurity. (2018, June 7). Privacy and the Internet of Things: Emerging frameworks for policy and design. UC Berkeley CLTC. https://cltc.berkeley.edu/iotprivacy/