Explainable AI tackles the “black box” problem by showing how algorithms make decisions—enhancing trust, accuracy, and validation across industries.
As more business leaders learn about artificial intelligence’s potential and how to use the technology in their work, they also discover some of its current limitations. One of the most discussed is what researchers term the “black box problem.” It relates to how most algorithms cannot clarify how they reach specific decisions. That challenge makes them potentially risky to use in impactful applications, such as those related to health, finance or legal matters.
Some professionals familiar with the topic believe explainable AI could improve data validation, helping people trust algorithms’ results, even in high-stakes situations. As the name suggests, this type can detail how it reached decisions, letting users follow a digital trail before trusting the information. Why does this matter, and what progress has occurred in the area so far?
Bringing Trust to Autonomous Mobility
What if executives could get into their cars and use the time to put the finishing touches on a presentation or meeting notes without hiring drivers? That is already possible in cities with autonomous taxis and similar consumer-facing offerings. Some logistics companies have also experimented with the viability of letting a heavy-duty truck do a long-distance route with a human only sitting in the vehicle to intervene in an emergency.
The widespread availability of autonomous vehicles could also enhance freedom for people who cannot or would prefer not to drive, such as people with disabilities, older adults and individuals who drink alcohol and do not have designated drivers. Despite these positives, overall trust remains low. A 2025 study of drivers in the United States found that only 13% would trust autonomous vehicles enough to ride in them.
That is understandable given the number of variables encountered during a typical drive. Wild animals, pedestrians and bicyclists are just a few of the numerous things to recognize, not to mention the other cars themselves. Self-driving cars process incredible amounts of data, but how do they reach their decisions?
Communicating to Passengers With Explainable AI
Representatives from some companies hope that validating the incoming information with explainable AI will increase people’s openness toward these vehicles. One is iMerit, a company present across the autonomous mobility spectrum.
Its leaders plan to build explainable AI with a multipronged process that includes data annotation, human validation and transparent communication. AI can only recognize real-world variations if it has millions of labeled examples in its training set. iMerit’s human-in-the-loop process helps autonomous vehicle algorithms make more accurate generalizations, plus recognize the more unusual situations a car may encounter.
The company’s continuous feedback and data validation loops help algorithms learn safely and effectively. The overall goal is to develop an explainable AI system that lets an autonomous vehicle recognize a stop sign and use generative AI to tell passengers the reason for its slowed speed. Achieving that goal should increase consumer confidence in autonomous cars because it removes much of the mystery behind how they act.
Shaping E-Commerce Decision-Making Through Confidence
Analysts anticipate the e-commerce market will show 56% growth by 2026, resulting in several billion dollars in revenue. Despite the overall success of online shopping, business executives quickly understand consumer fickleness. Consumers purchase some items as soon as they arrive in inventory, while others sell slowly or not at all, forcing some e-commerce companies to price them at a loss.
AI can already predict which items will sell and how quickly. That capability supports those operating online storefronts, plus related industries, such as logistics. What if it could also explain why it concluded such things? Executives would trust the results more, believing they can follow algorithm-driven suggestions and not worry about that decision causing significant amounts of wasted money.
Decision-makers can also use AI to influence specific sales-driving tactics, such as scarcity marketing. That approach involves briefly discounting a product or offering a limited-time perk, convincing people they cannot afford to ignore it. If the target market shows more enthusiasm for the campaign than expected, e-commerce sites might run out of the item too quickly, resulting in disappointed customers. Well-trained algorithms could prevent that, but only if online shopping leaders deem them trustworthy.
Building Models for E-Commerce Decision-Makers to Question and Verify
Data validation centers on whether people can and should trust the outcomes suggested to them. Although it spans beyond AI, the recent boom has spotlighted this technology. A doctoral student addressed the reality by building an easily understandable machine learning model for e-commerce executives.
Her work centers on what she terms “lag-aware” machine learning. This technology reveals anticipated online market growth and analyzes when it will occur. The algorithms analyze numerous economic factors when making calculations. They also include time-shifted features and can indicate the strength and timing of each economic aspect’s influence.
Because this student applies various explainability tools to her models, they reveal the overall impact of each variable on a forecast, and whether people should expect immediate or delayed effects. E-commerce executives should appreciate those details because they remove much of the guesswork from determining how customers will act and why. The greater insights could affect what they buy and when, resulting in fewer overstocks or product unavailability issues.
Viewing Explainable AI as Viable and Essential
Despite explainable AI being in the relatively early stages, these fascinating examples highlight why it will be instrumental for validating data and elevating trust. Business leaders and others can spend more time applying it to address pressing challenges and have fewer concerns about whether the algorithms got things right.
