What Is AI Transparency?

What you'll learn about AI transparency:

• AI transparency addresses the impact of AI tools on businesses, users, customers, and society, focusing on explainability, interpretability, and accountability.
• AI transparency is crucial for building trust, ensuring responsibility, identifying biases, and achieving fair outcomes in the evolving digital labor market.
• To achieve trustworthy AI, businesses should develop clear information-sharing practices, create comprehensive documentation, and embed transparency goals from the start.

72% of consumers report that they trust AI less than they did a year ago. It’s a challenge that businesses can address with more AI transparency, clarifying how artificial intelligence (AI) makes decisions, why those decisions are made, and their impact on day-to-day operations. By prioritizing transparency, you build trust that AI tools will consistently make ethical, reasonable choices based on the available data.

With AI transparency, businesses benefit from auditable, repeatable processes, while customers are more likely to trust digital labor output. Here's all you need to know about the what, why, and how of AI transparency.

AI transparency means showing how your AI system operates — what data it uses, how it makes decisions, and why it delivers certain results — so people can understand and trust what it’s doing. As AI technologies have become increasingly central to business processes and decisions, the importance of AI transparency has grown.

Consider, for instance, that one of the earliest uses of AI was suggesting similar content for readers, watchers, shoppers, and other digital consumers. It’s a straightforward process where AI analyzes the user’s historical activity to find an element of sameness to make a new recommendation.

Now, consider a business that uses AI to help inform financial decisions. AI tools need access to multiple secure and public-facing databases and must combine and curate this data to discover key trends. This creates a "black box" effect: Users have questions — and can get accurate answers — but lack visibility into the inner workings of AI. Because they don't know what's happening behind the scenes, they're reluctant to trust the AI outputs.

Transparency helps solve the trustworthy AI problem. In practice, there are three levels of AI transparency:

• Algorithmic: Machine learning (ML) algorithms underpin AI operations. This is the "how" of AI: How does AI use data to make decisions? Algorithmic transparency reduces the risk of unexpected or unintended decisions by ensuring AI shows its work and demonstrates how it arrived at "X" conclusion using "Y" data.
• Interaction: Interactions help users understand the "why" of AI decision-making. Interaction transparency is the free exchange of knowledge between users and AI tools. When users ask, AI solutions should explain the rationale behind their decision-making and give data that supports these decisions.
• Social: Social transparency addresses the "what" of AI. What impact do AI tools have on businesses, users, customers, and society at large? Social transparency is often tied to legislation and regulation. For example, multiple states now require AI transparency for hiring practices to help reduce the risk of bias.

AI transparency has multiple parts. It consists of various factors and insights that help provide a comprehensive view of intelligent operations. There's no single path to trustworthy AI; instead, it's composed of three broad requirements.

This refers to the ability to explain how an AI model arrived at a specific decision or outcome. These descriptions must be clear, concise, and easily understood, not just by those with a technical background.

Consider two examples of the same conclusion described in different ways.

• Example 1: Using a combination of local and cloud-based sales datasets, the model ran a weighted statistical analysis and delivered three pricing recommendations based on current and future conditions.

This description is cumbersome and confusing. As a result, it does little to improve AI transparency.

• Example 2: The solution compared sales and customer demand data from Q1 to Q2. While demand increased by 10%, sales only rose by 5%. Based on this data, here are three sales suggestions.

This description is clearer. It specifies what data was used, what conclusions were drawn, and offers actionable suggestions.

Explainable AI (XAI) is a part of explainability in AI. XAI is the area of research and development that looks at how AI systems can be built to provide humans with transparency into its decisions.

Interpretability focuses on understanding the general operations and decision-making of AI systems. This includes the data used by AI to make decisions, the source of that data, and the type of decision-making applied by AI technologies to data sources.

Accountability in AI ensures that both AI systems and those responsible for them can be held accountable for their actions and decisions. While there are a number of frameworks currently in development by different countries or organizations, there are no universally-accepted frameworks yet.

If these requirements sound similar to the three levels of AI transparency, that's by design. The three requirements are simply the practical application of these levels.

AI transparency plays a key role in the evolving digital labor market. Digital labor is the use of technologies, such as AI automation and AI agents, that mimic human decision-making and cognitive abilities. Using what's known as agentic AI, these agents can perform tasks, learn from interactions, and map processes to achieve goals without step-by-step instructions.

The evolving capabilities of these agents raise questions about how they arrive at their answers and what data they use to reach them. AI transparency helps answer these questions and offers five key benefits for your business.

Transparency leads to trust. Consider a company experimenting with new AI tools to analyze sales data. After some trial and error, AI returns demand predictions and pricing recommendations.

The more transparent the model is, the more likely users are to trust the results. If teams know exactly which data sources were used and what decision-making processes were followed, they can replicate both the process and the outcomes. Trust comes over time as results consistently meet expectations.

Transparency promotes responsibility by ensuring that both systems and people are held accountable for results.

AI tools aren't perfect. They make mistakes, sometimes due to poor data, other times because AI algorithms aren't up to the task, and in some instances due to unintentional bias. Trustworthy AI operations provide a way to identify the root cause of inaccurate results and ensure that the right parties are held accountable.

Bias remains a challenge for AI systems. When tools are trained on data that is inaccurate or incomplete, the results may seem accurate but can carry substantial bias. Transparency helps identify and correct these biases before they cause harm.

For example, an analysis of an AI-enabled recruiting tool might discover gender or socioeconomic hiring biases. With this information, businesses can update and retrain their models for better results.

Achieving fair and reliable results is the goal of any AI technology, but it is especially critical in high-impact sectors such as finance, healthcare, recruitment, or law enforcement. If AI uses inaccurate financial data to model potential investment outcomes, businesses could lose millions. Transparency helps reduce the risk of poor decision-making.

Many industries and organizations are creating rules for the use of AI. For example, the European Union's Artificial Intelligence Act prohibits the use of social scoring systems and manipulative AI tools. Any systems classified as "high risk" under the act must establish a risk management system, conduct data governance, and create technical documentation.

Transparency makes all these tasks easier. The more businesses know about how their AI tools work, the better prepared they are to navigate new compliance challenges.

AI transparency is about balance. If models aren't transparent enough, teams may struggle to trust their results. However, too much transparency can also pose challenges. Some of the most common include:

Data protection and user consent are essential for security due diligence, and transparency can put these at risk. For example, if businesses fail to anonymize data, AI models may accidentally reveal protected information during analysis. Failing to secure model training data and AI

Intellectual property (IP) and AI give your business a competitive edge. IP sets your products and services apart from competitors, while AI tools can help you stay ahead of the curve.

To make the most of both, balance is critical. Providing AI with complete IP details risks public exposure and the emergence of copycat products. Meanwhile, giving AI only the minimum amount of IP data will lead to a generic analysis and response.

The more advanced an AI model, the more complex it becomes. For example, while a traditional chatbot can ask and answer questions using available product or service data, digital workers can take targeted actions to resolve customer issues. The decision-making skills needed for agents to complete these tasks require the use of complex, interconnected algorithms.

This makes it hard to quantify AI actions in simple terms, which can reduce user confidence in model outputs.

Accurate AI models require multilayered decision-making skills, which limits their transparency. Simpler models provide improved visibility but may demand human oversight to ensure reliability. Businesses need to find a balance that works — do you prefer better outcomes at the cost of less transparency, or is visibility worth the extra effort of double-checking results?

From the addition of new machine learning algorithms to the use of new data sources and applications and the overall advancement of agentic systems, AI technologies are continually evolving. As models expand, connections may become obscured, in turn lowering visibility. Meanwhile, as models learn, old decision-making processes may be replaced by new frameworks that were not part of the original design.

As AI makes international inroads, countries are looking to standardize operations. While individual nations may have AI-specific or AI-adjacent policies, there are no global standards for the deployment and use of AI. This means that what qualifies as transparency in one country may fall short of expectations in another.

AI transparency plays a key role in AI openness, which relates to a broader understanding of AI operations, intentions, and outputs. In simpler terms, transparency is a part of the broader openness concept, informed by several components.

First, explain the results of specific AI tools: How did AI reach a specific decision? This should be clear to non-experts. Next is the interpretation of system functions, which focuses on the general operations and internal mechanics of the model. Common aspects of interpretation include the data sources used, ML algorithms applied, and key metrics measured.

System and user responsibilities are also key components. Both human staff and AI agents must be held accountable for their actions and learn from their mistakes. Finally, companies must ensure data visibility. This includes clarity on the origin, handling, and characteristics of data used to train, run, and retrain models.

While it's impossible to achieve complete transparency into any practice — AI or not — there are steps your business can take to improve transparency and reduce the risk of accidental errors. Here are eight best practices to boost visibility and accountability.

Companies must create clear policies that outline the type of AI information shared, who has access to this information, and how it is reported. One secure approach to information sharing is known as zero copy. This allows AI and other tools to access data without copying, moving, or reformatting the information.

Comprehensive documentation also enhances AI transparency. While documentation will be specific to a business and its model, there are several common components. These include the model's purpose, data sources, evaluation methods, known limitations, and any connected applications. Documentation should also categorize data sources as public, private, sensitive, and/or confidential.

It's easier to start with transparency than to add policies later. To achieve this goal, transparency should be prioritized during the initial stages of project development. By ensuring that even small decisions are fully transparent, businesses lay the foundation for reliable insights, even when tools use multiple data sources and complex AI algorithms.

AI systems evolve as the amount of available data increases. Robust monitoring systems help flag decisions that are inconsistent with past results. Comprehensive auditing tools provide visibility from consideration to decision. Together, these tools help companies spot potential problems and address their root causes.

Explainability tools and fairness toolkits aim to clarify predictions, assess decision fairness, and track data origins. When choosing a tool, companies should consider its integration with existing systems, the data used for evaluation, and the transparency of the tool in its decision-making process. It's also important to use tools that adhere to trusted AI principles. For example, Salesforce’s Einstein Trust Layer offers a robust set of features and guardrails that protect the privacy and security of your data.

Clear and consistent reporting practices help track transparency over time. To simplify this process, start by building a transparency report template. Combine this with simple-to-use reporting interfaces that help staff identify issues or ask for help.

It's easy to prioritize speed over specificity. While this may work in the short term, ignoring small AI issues upfront can lead to significant problems down the line. A dedicated system for user feedback combined with a communication-first approach encourages continual improvement.

Currently, there are no global AI regulations governing adoption, auditing, and transparency. However, several developing regulations may impact business operations. These include:

• The European Union's Artificial Intelligence Act: The EU AI Act places the bulk of transparency obligations on the developers of AI systems. For example, all general-purpose AI (GPAI) model providers must include technical documentation, instructions for use, and publish a summary of the content used to train the model.
• The General Data Protection Regulation (GDPR): While GDPR does not specifically deal with the use of AI, the European Data Protection Board (EDPB) has issued guidance on how companies can implement GDPR principles in AI deployments and ensure compliance.
• The Blueprint for an AI Bill of Rights: In the United States, the Blueprint for an AI Bill of Rights says users should be protected from unsafe and ineffective systems, they should not face discrimination from AI tools, and they should have control over how their data is used.
• State-specific regulations: Several states have adopted AI regulations. For example, the California AI Transparency Act will, in part, require covered providers to ensure users have access to AI detection tools that help identify the use of AI in creating audio, video, or image content. These rules are set to begin on January 1, 2026. Meanwhile, in Colorado, the Consumer Protections for Artificial Intelligence bill requires developers of "high-risk" AI systems to protect consumers from any foreseeable risks of algorithmic discrimination. The bill goes into effect on February 1, 2026.

Much like AI itself, AI transparency is evolving.

Businesses should anticipate the development of more sophisticated tools that are both increasingly automated and integrated into existing AI workflows. This will not only enhance AI outputs but also highlight the necessity for complete process transparency. Companies should also be ready for a potential shift toward evaluating the safety, quality, and biases of AI outputs.

In addition, organizations should expect more influence from regulatory bodies as both governments and industry oversight agencies look to codify the use and impact of AI. This will result in the need for more adaptive AI auditing, monitoring, and feedback frameworks that can keep pace with new technologies and changing regulations.

AI technology is quickly becoming a human interest, not simply a business concern. Consider the rise of tools such as ChatGPT, Dall-E, and other generative AI applications capable of creating everything from marketing and sales content to stories, images, and ideas.

This democratized development shifts the emphasis of transparency from organizational oversight to ethical and societal implications. Consider what role AI plays in supporting human staff by creating new content or leveraging human-created works to inform new images or text.

Achieving AI transparency is a complex journey. It is not a set-and-forget process — organizations must put time and effort into building AI systems that are open yet secure, accurate yet explainable. As tools evolve, these processes must also adapt, as societal impact and regulatory oversight of AI become key factors in the widespread adoption and ethical use of these technologies.

The bottom line is that AI transparency isn't simply beneficial. It's a cornerstone in building and deploying AI systems that are responsible, accountable, and trustworthy.