AI Auditability and Explainability: How Enterprise Teams Can Trust AI Decisions

AI auditability and explainability helps companies understand whether they should launch their pilot project into full production or hit “pause.”

Think about when an employee makes a radical proposal in a business meeting or submits a report filled with dubious claims. “How did you come up with that?” their manager might ask, and once they understand the employee’s research and thought process, they might realize more training and coaching is required.

What AI explainability and auditability actually mean (and why they’re different)

The same is true of AI. Whereas explainable AI (XAI) shows why the technology produced a certain output, AI audit trails break down exactly what happened. The latter means you can either stand behind what the tool produced or at least fine-tune it to prevent negative outcomes in the future.

These are complementary but equally important capabilities because they help tell the full story about how AI is performing. Together, they go way beyond superficial notions of AI transparency, which sometimes implies you need to publish all your code and training data, or that revealing biased or erroneous outputs will erode trust.

Building AI auditability and explainability into your strategy is an essential step in establishing enterprise AI governance, which will let your organization scale the technology without fear.   

Why black-box AI breaks down in enterprise environments

AI can make autonomous decisions, but people remain accountable for them. That could explain why 64% of U.S. adults believe the need for humans reviewing and checking AI outputs will increase. The same research found 42% have experienced AI outputs that missed important details or context.

At its worst, AI can resemble a black box whose inner workings are as difficult to discern as that of the human brain. This only exacerbates the fallout when AI introduces bias or defamatory claims into content, or where AI outputs simply fail to align with brand safety guidelines and use prohibited terms or phrases. Senior leaders will rightfully expect the ability to trace what happened and why.

Organizations may find themselves saddled with black box AI because they were trying to keep up with competitors. While 70% of executives say AI is now at the heart of their business strategy, 45% feel they’re falling behind competitors. There’s no real advantage in being first with an AI platform or tool if you’re unable to debug, justify, or improve the work it produces.    

A lack of AI auditability and explainability is also a big challenge for legal, compliance, and risk teams. Pointing the finger at an AI tool or platform when something goes wrong isn’t enough. AI compliance and risk management means being able to demonstrate that the technology adheres to accepted policies, approved sources, and industry-specific legislation.

If your organization is in enterprise AI governance catch-up mode, here’s what you need to educate stakeholders and begin procuring solutions with the necessary capabilities.

Explainability: trusting AI in the moment

You shouldn’t need to be an AI expert to interpret the technology’s actions. In 2024, 40% of business leaders called out explainability as a key risk in adopting generative AI. That sentiment might even be higher now that agentic AI is allowing platforms and tools to not only produce content but perform tasks on an organization’s behalf.

XAI doesn’t just help content team members work with greater confidence and build trust. It also educates them on how AI tools “think” so they can use better prompts and adjust the suggestions the technology makes in critical workflows. XAI should promote:

• A greater understanding of inputs, signals, and constraints: Some of the most common techniques for explaining AI outputs include SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). These methods help identify which aspects of a piece of content, such as a brief or source document, influences an AI tool’s decision-making.
• Human-in-the-loop decision-making: The conversational nature of AI platforms can help with explainability. For example, using natural language interfaces to ask for an output’s rationale could identify that an AI tool changed a phrase to reduce regulatory risk or align with voice and tone guidelines. This should become a part of employee training as they start using AI.
• Logic that can be retraced: Decision trees are a great example of how to visualize the paths AI tools should take when creating content. For instance, setting up “if/then” rules upfront can help ensure AI tools produce content for the right audience, with keywords, lengths, templates, and human review processes explicitly defined.

Building in explainability might seem like you’re adding extra steps, but it’s less about creating friction than enabling adoption.

Auditability: proving decisions after the fact

It’s the golden rule your teacher probably emphasized when you first learned how to write an essay: be prepared to back up every claim. We need to be just as diligent in using AI in content workflows. As its stands, 66% of business professionals admit they rely on AI output without evaluating accuracy, and 56% say it’s led to mistakes in their work.

Fortunately, AI auditability is achievable through:

• Logs, records, and permission dashboards: Platforms like WordPress VIP provide access to multiple logs and performance monitoring, as well as version controls that let you see post revisions and illustrate what happened to content throughout a workflow. These become part of the audit trail to see who did what (including AI tools), what got changed, and when.
• Supporting investigations, audits, and regulatory inquiries: Using automated, structured metadata can help you govern and verify AI-assisted content. This can begin with a content taxonomy that makes it easier to sort through content types, contributors, access levels, and subject matter. You can also use descriptive alt-text and assign tags automatically through integrations like ClassifAI. This makes it easier to respond to questions that may be asked.
• Confidence scoring: Either manually or with AI’s help, organizations can set up structured checklists for each content asset to be reviewed as needed. This would include source citations, bias flags, brand/tone/alignment, and factual accuracy. It can be a way to document what your human-in-the-loop process looks like and what earns final approval and sign-off.

Audit trails protect both organizations and individuals by promoting enterprise AI governance in a consistent, repeatable fashion.

How to evaluate AI tools for explainability and auditability

Developing responsible AI systems requires building on existing best practices for procuring enterprise-grade technology, with a greater focus on AI compliance and risk management. 

Here are some potential questions you could ask prospective vendors:

1. How are the algorithms/models tested to provide AI auditability and explainability?
2. How does the AI system assess the quality and lineage of the data it uses to produce outputs?
3. How reliably does the tool produce the same output if it’s using a consistent data set?
4. To what extent has the tool been engineered to align with privacy and security laws and regulations such as GDPR, and what kind of customization is available?
5. How have existing customers integrated audit trails and explainability into their workflows?
6. How can you demonstrate a representative example of using your solution to respond to an investigation, audit, or regulatory inquiry?
7. How does your solution integrate with an enterprise CMS to enhance XAI and auditing?

Selecting the best solution may require looking more closely at a vendor’s documentation to understand models or sources it draws upon, any missing policies, or vague promises of explainability and auditability. If there’s no ability to trace decision paths or a lack of automated tools to assist with compliance and versioning, you’re at increased risk of buying a black box.

AI auditability and explainability as an enterprise imperative

AI and AI audit trails need to be functional requirements, not optional extras. Without them, your organization faces increased legal exposure for compliance failures, the potential for costly rework and rollbacks, and erosion of brand trust.

You probably wouldn’t leave a new employee to fend for themselves once they start working with you, and AI tools require a similar level of observation and occasional intervention. Content intelligence tools like Parse.ly can play a big role here by providing transparent, inspectable decision signals and a dashboard that lets you see what’s going on with your content at a glance.

As responsible AI systems become the norm (and an expectation among senior leaders and customers alike), this is an opportune time to make explainability and auditability integral parts of using technology to enhance content creation and management.