Privacy and Artificial Intelligence - 2.3 Transparency and Explainability

2.3 Transparency and Explainability

Introduction

Transparency and explainability are essential for building trust in artificial intelligence (AI) systems, ensuring accountability, and meeting regulatory requirements. As AI becomes increasingly integrated into critical sectors such as healthcare, finance, and public safety, it is important for everyone—users, regulators, developers, and the public—to understand how AI makes decisions (SGS, 2024; SuperAGI, 2025). Transparency refers to how open and clear an AI system is about its operations, data use, and decision-making processes. Explainability means being able to provide understandable reasons for the decisions or actions taken by an AI system. Together, these concepts help people trust and verify AI outcomes, making sure that technology is used responsibly and fairly (SGS, 2024; Nitor Infotech, 2025).

Technical or Conceptual Background

Transparency in AI covers several important areas: model explainability, data transparency, documentation, risk disclosure, bias assessments, governance frameworks, and clear communication with stakeholders (OCEG, 2024; TechTarget, 2024). Model explainability is about making sure people can understand how an AI model reaches its conclusions. Data transparency means being clear about what data is used, where it comes from, and how it is processed. Documentation involves keeping records of how the AI system works, what decisions it makes, and why. Risk disclosure means being honest about the limitations and potential problems of the AI system. Bias assessments help check if the AI treats everyone fairly, and governance frameworks set rules for how the system should be managed and used (OCEG, 2024; TechTarget, 2024).

Explainability focuses on making AI decisions understandable to people who are not experts in technology. This means explaining the logic, process, or reasoning behind an AI’s actions in a way that is easy to follow (Larsson & Heintz, 2020). Interpretability is a related idea, focusing on understanding how the internal workings of an AI model connect inputs to outputs (TechTarget, 2024). Explainable AI (XAI) methods include both adapting existing AI systems to be more understandable and designing new systems from the start to be explainable. These methods help users, regulators, and developers understand, assess, and trust AI decisions, especially in high-risk applications (SGS, 2024; Nitor Infotech, 2025).

Current Trends and Challenges

The demand for explainable AI is growing quickly. Organizations are prioritizing transparency to build trust, comply with regulations like the EU AI Act and GDPR, and reduce risks of bias and errors (SuperAGI, 2025; C4G Enterprises, 2024). Without transparency, mistakes can happen, leading to legal challenges, financial losses, and damage to reputation. For example, financial companies have faced lawsuits when their AI-driven decisions were not explainable, resulting in regulatory scrutiny and fines (C4G Enterprises, 2024; PlainEnglish, 2025).

However, there are still many challenges. Some AI models, especially deep neural networks, are very complex and difficult to explain. These are sometimes called “black boxes” because it is hard to see inside and understand how they work (Shield, 2024; LinkedIn Learning, 2024). This lack of clarity can make it difficult for people to trust AI, especially when decisions are important or have a big impact on people’s lives. Ongoing research and new technologies are helping to make AI more transparent and explainable, but there is still a lot of work to be done (Shield, 2024; LinkedIn Learning, 2024).

Problems Being Solved or Best Practice Being Applied

This solution directly addresses the problem of lack of transparency and explainability in AI systems (Part 1.3). By making AI decisions clear and understandable, organizations can build trust, reduce bias, and ensure that AI is used responsibly. Transparency and explainability help people understand how decisions are made, check for mistakes or unfairness, and hold organizations accountable. This is especially important in areas like healthcare, finance, and public services, where AI decisions can have a big impact on people’s lives (SGS, 2024; PlainEnglish, 2025).

Role of Government and Regulatory Authorities

Governments and regulatory authorities play a central role in making sure that AI systems are transparent and explainable. They create and enforce laws that require organizations to provide clear, understandable information about how AI makes decisions, especially in high-risk sectors such as healthcare, finance, and public safety (TechPolicy, 2025; White House, 2025). For example, the EU AI Act requires that high-risk AI systems be transparent and explainable, and the US Office of Management and Budget (OMB) directs federal agencies to maintain AI transparency to build public trust (TechPolicy, 2025; White House, 2025).

Regulatory authorities also promote transparency through public AI inventories and risk assessments. In California, the AB 302 law requires a comprehensive inventory of high-risk AI systems used by state agencies, and the city of San Jose has its own AI inventory initiative. These efforts help ensure that AI systems are documented, evaluated for risks, and held to high standards of fairness and accountability (TechPolicy, 2025). International forums like the Internet Governance Forum 2025 emphasize the importance of working together across different groups to foster ethical and transparent AI globally (Digital Watch Observatory, 2025).

Regulatory bodies provide guidance, monitor compliance, and enforce penalties for organizations that do not meet transparency and explainability requirements. They also support public awareness campaigns and educational initiatives to help people understand their rights and how to use AI safely (Quantexa, 2024; ICO, 2024). For example, they might publish guides, hold workshops, or create online resources to explain how AI works and what rights people have when interacting with AI systems.

Governments also work with other countries to develop international standards and best practices for AI transparency and explainability. Organizations like the OECD, UNESCO, and ITU help coordinate these efforts, making sure that AI is used responsibly around the world (OECD, 2023; UNESCO, 2021; ITU, 2024). By setting clear rules and supporting education, governments and regulatory authorities help build trust and ensure that AI is used for the benefit of everyone.

Role of Organizations and Businesses

Organizations and businesses are responsible for making sure that their AI systems are transparent and explainable. This means integrating transparency and explainability into every stage of AI development and deployment (CoreWave, 2025; PlainEnglish, 2025). Organizations should use explainable AI (XAI) techniques that provide clear reasons for AI decisions, so that users and regulators can understand and trust the outputs. Transparency helps reduce bias, meet regulatory requirements, and improve decision-making efficiency (CoreWave, 2025; PlainEnglish, 2025).

To maintain transparency over time, organizations should implement robust model monitoring and change management. This means regularly checking AI systems for bias, updating models with new data, and making sure that explanations are clear and accurate (LinkedIn Learning, 2024). Training employees to communicate AI decisions effectively is also important, as they are often the ones who explain how the system works to users and other stakeholders (LinkedIn Learning, 2024).

Providing clear documentation and user-friendly explanations helps people understand how decisions are made and what data is used. This builds trust and ensures that AI is used responsibly (PlainEnglish, 2025; ICO, 2024). Organizations should also be open about the limitations and potential risks of their AI systems, so that people know what to expect and can make informed decisions.

Organizations can also benefit from transparency by improving their reputation and building stronger relationships with customers and partners. When people trust that an organization’s AI systems are fair and understandable, they are more likely to use its products and services. Transparency also helps organizations identify and fix problems quickly, reducing the risk of costly mistakes or legal issues (PlainEnglish, 2025; LinkedIn Learning, 2024).

Role of Vendors and Third Parties

Vendors and third-party providers play a critical role in ensuring that AI systems are transparent and explainable. They must provide detailed documentation on how their models were developed, what data was used, and how the models perform (Shield, 2024). Collaboration with clients to clarify model behavior and address any discrepancies is essential for maintaining trust and compliance (Shield, 2024).

Vendors should also support ongoing model monitoring and updates to adapt to changing data and regulatory requirements. This includes providing tools for bias detection, model explainability, and audit trails (Shield, 2024; CoreWave, 2025). By working closely with organizations, vendors can help ensure that AI systems remain transparent, fair, and accountable over time.

Vendors must also be transparent about their own data handling practices. This means being clear about what data is collected, how it is used, and who has access to it. Clear contractual agreements should define responsibilities and compliance obligations, so that everyone knows what is expected (Shield, 2024; CoreWave, 2025). Regular audits and assessments of third-party vendors help organizations verify that their partners maintain adequate security controls and comply with relevant regulations.

By providing transparent and explainable AI solutions, vendors can help organizations build trust with their users and meet regulatory requirements. This is especially important in sectors like healthcare, finance, and public services, where trust and accountability are essential (Shield, 2024; CoreWave, 2025).

Role of Employees and Internal Teams

Employees and internal teams are essential for making sure that AI systems are transparent and explainable. Employees, especially those who interact with users, need support and training to understand how AI makes decisions. This enables them to explain AI outcomes clearly to users, fostering trust and satisfaction (LinkedIn Learning, 2024).

Internal teams, including developers and data scientists, must prioritize explainability during model design and testing. This means choosing methods that make it easier to understand how the AI works and why it makes certain decisions (LinkedIn Learning, 2024). Teams should also document their work and provide clear explanations for their choices, so that others can review and verify the results.

Training and awareness programs are important to help employees understand the importance of transparency and explainability. This includes learning how to recognize and address bias, how to communicate AI decisions to users, and how to respond to questions or concerns (LinkedIn Learning, 2024). By investing in training and support, organizations can ensure that their employees are prepared to use AI responsibly and explain its workings to others.

Internal teams should also monitor AI systems over time to make sure they continue to make fair and accurate decisions. This includes checking for bias, updating models with new data, and making sure that explanations remain clear and accurate. Change management is important because AI systems can change as they learn from new information, and these changes need to be tracked and explained (LinkedIn Learning, 2024).

Role of Industry Groups and Professional Bodies

Industry groups and professional bodies develop standards, best practices, and certifications to promote AI transparency and explainability. They facilitate knowledge sharing and advocate for ethical AI development. Examples include IEEE standards on AI ethics and privacy, and certifications like CIPP that train professionals in responsible AI practices (IEEE, 2022; IAPP, 2024).

These organizations provide guidance, training, and resources to help organizations implement transparent and explainable AI. They also advocate for strong regulations and public awareness campaigns to promote trust and accountability in AI (IEEE, 2022; IAPP, 2024). By setting industry-wide benchmarks and promoting ethical conduct, these groups help build public trust in AI technologies and encourage widespread adoption of best practices.

Industry groups also organize events, workshops, and conferences where experts can share their experiences and learn from each other. This helps spread knowledge about new technologies, best practices, and emerging challenges in AI transparency and explainability (IEEE, 2022; IAPP, 2024).

Role of International and Multilateral Organizations

International and multilateral organizations foster global cooperation on AI transparency. They develop guidelines, provide technical assistance, and promote harmonized standards. The OECD, UNESCO, and ITU are key players in advancing ethical AI governance and transparency worldwide (OECD, 2023; UNESCO, 2021; ITU, 2024).

These organizations support public awareness, education, and capacity-building initiatives to help countries implement transparent and explainable AI. They also facilitate international collaboration and knowledge sharing to address global challenges and promote responsible AI use (Digital Watch Observatory, 2025; OECD, 2023).

By setting global standards and supporting education, international organizations help ensure that AI is used responsibly and fairly around the world. This is especially important as AI systems are increasingly used across borders, and people need to trust that their information is being handled safely and ethically (OECD, 2023; UNESCO, 2021; ITU, 2024).

Role of Consumers and Users

Consumers and users drive demand for transparent and explainable AI by preferring products that explain AI decisions and protect privacy. They can exercise rights under data protection laws to access explanations and challenge automated decisions. Consumer feedback helps organizations improve AI transparency and trustworthiness (IAPP, 2024; ENISA, 2021).

When people understand how AI makes decisions, they are more likely to trust and use AI products and services. This is especially important in areas like finance, healthcare, and public services, where decisions can have a big impact on people’s lives. By demanding transparency and explainability, consumers can help ensure that AI is used responsibly and fairly.

Feedback mechanisms, such as user surveys, complaint channels, and public forums, provide valuable insights into user concerns and experiences. Organizations can use this input to improve their AI systems and address privacy risks (ENISA, 2021; IAPP, 2024).

Educational initiatives aimed at consumers help raise awareness about privacy risks and best practices for safe technology use. This includes understanding the implications of data sharing, recognizing phishing or social engineering attacks, and using privacy-enhancing tools (IAPP, 2024).

Role of Members of the Public

Members of the public influence AI transparency through advocacy, education, and participation in policymaking. Civil society groups like the Electronic Frontier Foundation raise awareness and push for stronger transparency laws. Public consultations ensure regulations reflect societal values and protect vulnerable groups (EFF, 2023; OECD, 2023).

By participating in public discussions and advocacy, members of the public can help shape the future of AI and ensure that it is used for everyone’s benefit. Public opinion and activism can influence lawmakers and organizations to prioritize privacy and security in AI systems.

Educational programs and media coverage inform the public about the importance of transparency and explainability, helping people make informed choices and demand accountability (IAPP, 2024). By fostering a culture of transparency and trust, members of the public help create an environment where organizations are motivated to adopt best practices and where AI technologies are developed responsibly.

Role of Artificial Intelligence Itself

AI itself can support transparency by automating explainability features, detecting anomalies, and generating audit trails. For example, AI systems can provide explanations for their decisions, highlight potential biases, and track changes over time. However, human oversight is essential to ensure AI explanations are accurate and ethical (Veale, 2022).

By building explainability into AI systems, developers can help ensure that technology is safe, fair, and accountable. AI can also help monitor its own performance, identify errors, and provide feedback to users and developers. This makes it easier to detect and fix problems, and to maintain trust in AI systems.

However, AI systems must be carefully designed to avoid introducing new risks, such as bias or over-reliance on automated decisions. Human oversight remains essential to ensure that AI supports transparency and explainability goals effectively and ethically (Veale, 2022).

Role of Bad Actors

Bad actors, including hackers, cybercriminals, and malicious insiders, pose significant challenges to the implementation of transparency and explainability in AI systems. Their activities aim to exploit vulnerabilities, steal sensitive data, or manipulate AI outputs for financial gain, political motives, or other malicious purposes.

Cyberattacks such as data breaches, ransomware, and supply chain attacks can compromise AI systems and the personal information they process. For example, adversaries may target AI training data to introduce bias or manipulate outcomes, a tactic known as data poisoning (Biggio & Roli, 2018). If a bad person changes how an AI learns, it might start making wrong decisions or treating people unfairly.

Insider threats, where employees or contractors misuse access privileges, can also undermine transparency and explainability efforts. These individuals may exfiltrate data or sabotage AI models, causing significant harm (Greitzer & Frincke, 2010).

Bad actors continuously evolve their techniques, leveraging AI themselves to automate attacks, evade detection, and exploit new vulnerabilities. This arms race necessitates robust, adaptive security measures embedded in AI systems from the design phase (ISACA, 2025; Symantec, 2024).

Understanding the tactics, techniques, and procedures of bad actors helps organizations anticipate threats and develop effective countermeasures. Collaboration between industry, government, and academia is essential to share threat intelligence and improve resilience against malicious activities (ISACA, 2025).

Glossary

Term	Meaning and Example Sentence
Transparency	How well we understand how an AI system works. Example: “Transparency means knowing what data the AI used to make a decision.”
Explainability	The ability to give clear reasons for AI decisions. Example: “Explainability means the AI can tell you why it made a certain choice.”
Black Box	An AI system that is hard to understand or explain. Example: “A black box AI makes decisions without showing how it works.”
XAI (Explainable AI)	AI designed to provide clear explanations for its decisions. Example: “XAI helps people understand why the computer recommended a certain action.”
Audit Trail	A record of all steps and changes in an AI system. Example: “An audit trail helps track how the AI made a decision over time.”

Questions

1. What is transparency in AI, and why is it important for building trust?

2. How does explainability help users and regulators understand AI decisions?

3. What role do governments and regulatory authorities play in ensuring AI transparency and explainability?

4. How can organizations and businesses support transparency and explainability in their AI systems?

5. Why is it important for employees and internal teams to understand and communicate AI decisions?

Answer Key

1. Suggested Answer: Transparency in AI means being able to understand how an AI system works and what data it uses to make decisions. It is important for building trust because it helps people feel safe and confident using AI, especially when decisions have a big impact (SGS, 2024; PlainEnglish, 2025).

2. Suggested Answer: Explainability means providing clear, understandable reasons for AI decisions. This helps users and regulators check for mistakes, unfairness, or bias, and ensures that AI is used responsibly (Nitor Infotech, 2025; CoreWave, 2025).

3. Suggested Answer: Governments and regulatory authorities enact and enforce laws requiring organizations to provide clear information about AI systems. They also promote transparency through public inventories, risk assessments, and international cooperation (TechPolicy, 2025; White House, 2025).

4. Suggested Answer: Organizations and businesses can support transparency and explainability by adopting explainable AI techniques, providing clear documentation, training employees, and monitoring AI systems for bias and errors (PlainEnglish, 2025; LinkedIn Learning, 2024).

5. Suggested Answer: Employees and internal teams need to understand and communicate AI decisions to users and other stakeholders, so everyone can trust and use AI safely. This requires training, support, and clear communication (LinkedIn Learning, 2024; ICO, 2024).

References

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