Original article published in Forbes
As artificial intelligence systems become more powerful and widely adopted, they’re not becoming more clear—in fact, it’s often the reverse. Even AI developers can’t always explain how or why their complex models generate the outputs they do, leading to these tools being dubbed “black box” AI systems.
Without clear insight into how AI models make decisions, businesses risk deploying tools that are unaccountable, biased or poorly understood. Below, members of Forbes Technology Council share effective strategies to make black box AI systems more interpretable, explainable and trustworthy.
1. Break Down AI Micro-Decisions And Give End Users More Control
Organizations should employ a “show your work” strategy when trying to improve AI systems—especially neural networks and ensembles, which are complex and “black box” in nature. This means breaking down the micro-decisions of how AI functions as a co-pilot and giving customers and end users command over their AI usage, allowing them to scale it up and down as needed. – Sal Visca, Vertex Inc.
2. Leverage XAI And Human Oversight
An effective strategy is leveraging explainable AI (XAI): methods that provide humans with a better understanding of how AI makes decisions. While conversational AI can be effective in decision-making systems, transparency is a challenge. Human oversight is essential to ensure the accuracy of information and provide accountability in AI systems. – Fabio Sattolo, Covisian
3. Create Audit Trails
Audit trails have been an effective strategy to improve transparency in AI-powered systems. When a large language model makes a decision—especially one that affects real lives—we deserve to know how and why. Audit trails are like a digital paper trail, capturing inputs, logic and outcomes so we can trace, question and improve the system. It is accountability, not just code. – Vinodhini Ravikumar, Microsoft
4. Trace Neuron Activations In LLMs
When it comes to LLMs, which operate as “gray boxes,” we can observe their token prediction behavior based on probability distributions. Still, a deeper understanding requires tracing specific neuron activation pathways that lead to these predictions. This approach bridges the gap between surface-level observations and true interpretability of their decision-making processes. – Biraj Silwal, TensorStax
5. Design Dynamic, Transparent UIs
One strategy is to create dynamic, transparent user interfaces and experiences that don’t just provide the output or decisions. Interfaces should be developed to show the reasoning paths that the AI took, reveal areas where the AI has low confidence (AI may request human input), and enable users to explore alternative scenarios or paths and work back and forth with the AI on its reasoning and plan. – Nupur Jain, Google Deepmind
6. Offer Tiered Explanations For Different Users
One effective strategy is tiered explanations—offering different levels of model insights for different users. For example, end users see simple reasoning (“We recommended this because … ”), while technical teams can access deeper metrics like feature importance or SHAP values. This builds trust without overwhelming non-experts. – Raghav Venkata Kandarpa
7. Implement Model Auditing And Documentation Frameworks For Datasets
An effective strategy is to implement model auditing and documentation frameworks like model cards or data sheets for datasets. These provide structured, standardized information about an AI system’s design, training data, limitations and intended use. This can improve transparency for developers, regulators and end users without exposing proprietary algorithms. – Bhupendra Singh, Marriott International
8. Log AI Decision Processes In Real Time
Implementing comprehensive logging systems that record all AI decision processes is crucial for transparency. As we have learned from data recovery, maintaining detailed audit trails allows for retroactive analysis when outcomes are unexpected, providing insights into the AI’s reasoning while maintaining system performance. – Chongwei Chen, DataNumen, Inc.
9. Verify Training Data Quality And Sources
It is important to understand what kind of information is going into an AI system and verify it matches the generated output. You need transparency into how the system was trained and where the information originated. Even if there isn’t full transparency in terms of the workings within the black box calculations, you must understand the quality, origin and accuracy of the delivered data. – David Lareau, Medicomp Systems
10. Incorporate A Retrieval-Augmented Generation Layer
AI systems should be built with a RAG layer for transparency and the ability to provide enhanced explainability. A RAG layer ensures that AI responses are based on verified, authoritative sources and reduces bias and misinformation. The ability of a system to deliver enhanced explainability allows for more precise insights into how responses are generated, which improves system transparency. – Rohan Joshi, Wolken Software
11. Embed Shift-Left QA Into AI Engineering Practices
AI must be built with the same engineering rigor as enterprise software. Embedding shift-left quality assurance, trace audits, bias detection and explainability logs ensures trust at scale. OpenAI’s deep research shows some of this in action, revealing sources, reasoning and inner workings. Responsible AI must be architected in and transparency must be systemic, not surface, especially as POCs move to production. – Monica Hernandez, MAS Global
12. Leverage XAI Tools
One effective strategy for improving transparency in black-box AI systems is using explainable AI (XAI) tools, like feature importance analysis, to show which inputs most influence decisions. This makes the model’s reasoning clearer without changing it. Further, in AI-generated answers, make sure to cite the source of information. – Sandeep Giri, CloudxLab Inc.
13. Disclose The Source(s) Of Training Data
You’ll want to share the training data for the model with the consumers of the AI tool. Did you use Reddit to train the model or the New York Times? Knowing what the model is trained on will shed light on black box AI models. You should include understandable visualizations of the model with the training data and use a decision tree or flow chart to help the consumer trust the answers the model provides. – John Bruggeman, CBTS
14. Ensure Humans Are In The Loop By Design
You need to ensure that humans are in the loop by design. Consider how processes are currently quality controlled. How are high-stakes decisions handled? What information is needed to make effective decisions? What is the cost of making a mistake? How are escalations managed? Who is accountable? Unironically, this sounds a lot like human performance management. Consider—if you were to “hire” AI, how might you screen it? – Cal Al-Dhubaib, Further
15. Have The AI Explain Its Reasoning In Plain Language
One strategy is to have the AI break down its reasoning in plain language and make that thinking visible in the UI. You can have it reveal what data it analyzed, what patterns it identified and why it chose one option over another. Then, it can highlight potential root causes and walk through each step clearly. This turns the output into something users can understand, actively question and trust. – Karthik Sj, LogicMonitor
16. Track Training Inputs With A Content BoM
A bill of materials that tracks AI-ready content improves transparency by providing an auditable record of the document sets used for model training from all sources. This helps trace outputs, identify biases, validate relevance and ensure compliance. A well-managed BoM enhances oversight, making AI systems more accountable and reliable. – Sean Nathaniel, DryvIQ
17. Train Models To Flag Anomalies And Risk
Looking at the use of AI in cybersecurity and fraud prevention, transparency can be improved by what is known as “train and explain.” In other words, it’s avoiding automated decision-making by training models to analyze diverse data signals, provide clear and easy-to-understand reason codes when anomalies are spotted, and make intelligent recommendations when activity is flagged as a potential risk. – Christophe Van de Weyer, Telesign
18. Walk Users Through AI-Generated Outputs
In healthcare, an effective strategy is to walk clinicians through AI-generated outputs alongside the associated clinical case. By explaining the model’s reasoning, strengths and limitations, clinicians can better assess how AI supports their decision-making. This hands-on approach fosters transparency, allowing users to understand when to trust the AI and when to question its recommendations. – Heather Bassett, Xsolis
19. Integrate Explainability Tools Into Monitoring Workflows
One effective strategy is to incorporate explainability tools directly into model monitoring workflows. Instead of treating explainability as a post-hoc add-on, embedding model interpretability from the start provides stakeholders with insights into feature importance and decision rationale in real time, bridging the gap between output and understanding. – Chris Brown, Intelygenz, a VASS Company
20. Use Simpler Surrogate Models To Interpret AI Logic
One effective strategy to improve transparency in black box AI systems is to use interpretable surrogate models. These are simplified models—like decision trees or linear regressions—that mimic the complex model’s behavior. These surrogates help stakeholders understand decision logic without compromising the original model’s performance. – Sanjay Moolchandani, Citibank (USA)