Privacy and Artificial Intelligence - 1.3 Lack of Transparency and Explainability

1.3 Lack of Transparency and Explainability

Introduction

Imagine if you asked a magic box to help you with your homework, but the box never told you how it got the answers. You might wonder if you can trust the answers, or if you could ever learn from them. In the world of artificial intelligence (AI), this is what happens when systems lack transparency and explainability. Transparency means being able to see and understand how an AI system works, while explainability means being able to get clear reasons for why the AI made a certain decision (Zendesk, 2024). Without these, it is hard for people to trust or use AI safely and fairly.

Technical or Conceptual Background

AI systems, especially those using deep learning and neural networks, are often called “black boxes” because their inner workings are not easy to see or understand (Plainconcepts, 2025). These systems take in lots of data, process it through many layers, and produce results—sometimes without any clear explanation of how they reached their conclusions. This makes it difficult for users, even experts, to know why an AI made a certain choice or prediction.

Transparency in AI is not just about understanding the technology, but also about knowing what data was used, how it was collected, and how the AI was trained (TechTarget, 2024). Explainability is about making sure that everyone affected by an AI’s decision can get a simple and clear explanation of why it happened. For example, if an AI helps doctors decide which treatment to give, patients and doctors both need to know why the AI suggested that option (Sanofi, 2024).

However, as AI models become more complex and powerful, it becomes harder to make them transparent and explainable. Generative AI, like large language models, is especially tricky because it uses vast amounts of data and can produce unexpected results (TechTarget, 2024). This makes it even more important to have good documentation and clear explanations, but also more difficult to achieve.

Current Trends and Challenges

There is growing pressure on companies and organizations to make their AI systems more transparent and explainable (Plainconcepts, 2025). Many laws, like the GDPR in Europe and the CCPA in California, require that people have the right to know how automated decisions are made and to challenge them if needed (AI Competence, 2025). This is especially important in areas like healthcare, finance, and education, where AI decisions can have a big impact on people’s lives.

Despite these requirements, many organizations struggle to make their AI transparent and explainable. One reason is that complex AI models are difficult to explain, even for experts. For example, a survey found that over 65% of organizations cited “lack of explainability” as the main barrier to using AI (Nitor Infotech, 2025). Another challenge is that making AI more transparent can sometimes reveal private or sensitive information, creating a conflict between transparency and privacy (AI Competence, 2025).

Another trend is the use of “explainable AI” (XAI) techniques, which try to make AI decisions easier to understand. These techniques include using simple language, visual aids, and interactive tools to help people see how the AI works (Nitor Infotech, 2025). However, even with these tools, it is still hard to fully explain the most advanced AI systems, especially those that act autonomously or make decisions in real time.

Mitigation Challenges and Shortcomings

Even though there are many tools and techniques to make AI more transparent and explainable, there are still big challenges. One challenge is the trade-off between performance and explainability. The most accurate AI models are often the hardest to explain, and making them simpler to understand can sometimes make them less accurate (Sanofi, 2024). This means organizations have to choose between having a very powerful AI and having one that people can trust and understand.

Another challenge is keeping up with changes in AI models. As AI systems are updated and retrained, their behavior can change in ways that are hard to predict or explain (Zendesk, 2024). This makes it difficult to maintain transparency and explainability over time. Regular documentation and transparency reports can help, but they require ongoing effort and attention.

There is also the risk that too much transparency can expose private information or make it easier for bad actors to find and exploit weaknesses in AI systems (Plainconcepts, 2025). For example, if an AI model is too open about how it works, someone might be able to figure out private details about the people whose data was used to train it (AI Competence, 2025). This is called re-identification risk.

Finally, some companies are reluctant to share too much information about their AI because it could give away trade secrets or competitive advantages (Plainconcepts, 2025). This creates a tension between the need for transparency and the desire to protect intellectual property.

Glossary

Term	Meaning and Example Sentence
Transparency	Being able to see and understand how an AI system works. Example: “The company published a transparency report to show how its AI makes decisions.”
Explainability	Being able to get clear reasons for why an AI made a certain decision. Example: “The doctor asked for an explanation of why the AI recommended a certain treatment.”
Black Box	An AI system whose inner workings are not easy to see or understand. Example: “The deep learning model is a black box because no one knows exactly how it works.”
Neural Network	A type of AI model that mimics the way the human brain works. Example: “The neural network learned to recognize faces from thousands of photos.”
Generative AI	AI that can create new content, like text, images, or music. Example: “The generative AI wrote a story based on the prompt.”
Explainable AI (XAI)	AI designed to provide clear explanations for its decisions. Example: “The new XAI system shows users why it made each recommendation.”
Re-identification Risk	The risk that private information can be figured out from anonymized data. Example: “Too much transparency can increase re-identification risk.”

Questions

1. What is the difference between transparency and explainability in AI systems?

2. Why are complex AI models often called “black boxes,” and why is this a problem?

3. What are some laws that require AI systems to be transparent and explainable?

4. What are two challenges organizations face when trying to make their AI systems more transparent and explainable?

5. How can too much transparency in AI systems create risks for privacy and security?

Answer Key

1. Suggested Answer: Transparency means being able to see and understand how an AI system works, including what data it uses and how it is trained. Explainability means being able to get clear reasons for why the AI made a certain decision (Zendesk, 2024; TechTarget, 2024).

2. Suggested Answer: Complex AI models, especially those using deep learning and neural networks, are called “black boxes” because their inner workings are not easy to see or understand. This is a problem because it makes it hard for users to trust or challenge the AI’s decisions (Plainconcepts, 2025; Sanofi, 2024).

3. Suggested Answer: Laws like the GDPR in Europe and the CCPA in California require that people have the right to know how automated decisions are made and to challenge them if needed (AI Competence, 2025).

4. Suggested Answer: Two challenges are that complex AI models are difficult to explain, even for experts, and that making AI more transparent can sometimes reveal private or sensitive information, creating a conflict between transparency and privacy (Nitor Infotech, 2025; AI Competence, 2025).

5. Suggested Answer: Too much transparency can expose private information or make it easier for bad actors to find and exploit weaknesses in AI systems. For example, someone might be able to figure out private details about the people whose data was used to train the AI (AI Competence, 2025; Plainconcepts, 2025).

References

AI Competence. (2025, March 22). Can We Trust Transparent AI With Our Privacy? https://aicompetence.org/can-we-trust-transparent-ai-with-our-privacy/
Nitor Infotech. (2025, May 28). Explainable AI in 2025: Navigating Trust and Agency in a Dynamic Landscape. https://www.nitorinfotech.com/blog/explainable-ai-in-2025-navigating-trust-and-agency-in-a-dynamic-landscape/
Plainconcepts. (2025, June 13). AI Transparency: Fundamental pillar for ethical and safe AI. https://www.plainconcepts.com/ai-transparency/
Sanofi. (2024, November 26). All in on AI, Transparent & Explainable. https://www.sanofi.com/en/magazine/our-science/ai-transparent-and-explainable
TechTarget. (2024, September 10). AI transparency: What is it and why do we need it? https://www.techtarget.com/searchcio/tip/AI-transparency-What-is-it-and-why-do-we-need-it
Zendesk. (2024, January 18). What is AI transparency? A comprehensive guide. https://www.zendesk.hk/blog/ai-transparency/