Transparent AI is responsible AI 

The promise of AI for making better, faster decisions based on a deep understanding of data is becoming clearer by the day.  

But in supply chain and logistics, even small errors can cascade into major, costly crises. As the CTO of project44, I firmly believe that pushing the envelope in AI must go hand-in-hand with an unrelenting focus on responsibility. Our decisions—rather, the decisions that AI makes based on how we set it in motion—can affect lives and livelihoods; responsible AI and innovation is the only viable path forward.  

Because responsible innovation is the only viable path forward, our approach to AI development at project44 focuses on two critical principles:  

• Interpretable Output: Ensuring users understand the AI-driven actions by building transparency into the interface.  

• System Redundancy: Ensuring AI-driven actions are backed by the most accurate data and models available.  

Building trust—both in end-users and in supply chain leadership—is foundational for realizing wider adoption and deeper value from AI. By adhering to these principles, we aim to deploy AI that is not only powerful but also trustworthy for global supply chain operations. 

Interpretable Output: Clarity in AI-Driven Actions 

Interpretable Output simply means that users can always understand what an AI agent is doing and why it’s doing it. In practice, this principle translates into building AI systems whose decisions and recommendations can be easily explained in human terms.  

If an AI suggests holding a shipment at a distribution center or re-routing a delivery truck, our users shouldn’t be left guessing about the rationale. They should immediately see the factors that influenced that decision—whether it was a traffic delay, a missed sensor ping, a weather alert, or some other trigger.  

By designing for interpretable output, we ensure our AI behaves less like a mysterious “black box” and more like a well-trained colleague that can articulate its reasoning. 

Let’s look at two examples of how this takes shape on the project44 platform:  

Transparent ETA Predictions 

Customers often seek clarity on the inputs we use for ETA predictions—the events we consider and the ones we disregard. To address this, we make the “ingredients” of each ETA visible.  

We categorize key factors driving an ETA into distinct groups: dwell times at stops, milestone completion status, GPS ping quality, route deviations, and even time-of-day traffic patterns. Our platform tracks these factors in real time and implements a rolling time window to flag any major deviation in the ETA, ensuring that significant shifts are recorded and explained.  

For example, if an unscheduled dwell event causes a delay—say the ETA moves from 2 PM to 6 PM—a user sees the update and an explanation like: “ETA adjusted due to an unplanned stop at Facility X.” Additionally, we will implement a rolling time window to track major deviations, ensuring that significant shifts in ETA estimates are tracked and reported. This saves the user from surprise ETA updates and gives them a specific reason for the change.  

By systematically tracking deviations and providing these contextual explanations, we build trust in our ETA predictions: users can make informed decisions because they understand the why behind the arrival times being forecasted. 

Explainable AI Assistants 

We apply the same transparency ethos to AI-powered digital assistants in our platform.  

Consider MO, our supply chain assistant designed to answer specific supply chain questions quickly and accurately (for example: retrieving shipment information). We’ve built an explainability module that “peeks under the hood” of the chatbot’s mind, so to speak, and exposes the reasoning process to the user. The module: 

1. Breaks down the SQL query formulation process, 

2. Shows which database tables and fields are being accessed, and  

3. Indicates how logic is applied to derive the answer.  

For example: if a user asks, “What are my delayed shipments today?” MO doesn’t just spit out an answer and leave the user wondering how it was determined. Behind the scenes, the AI assistant translates that natural language question into a database query, and our system will show the user a plain-language description of that query. In this case, the assistant might effectively look for shipments where the actual arrival time is later than the predicted arrival time. The explainability module then displays something like: “To determine delayed shipments, I checked the shipments database for any shipments where the actual_arrival is after the predicted_arrival.”  

This level of interpretability is invaluable in an enterprise context—users gain confidence that the AI isn’t making leaps of faith but rather following business rules and data just as a well-trained analyst would. 

System Redundancy: Ensuring Accuracy and Reliability 

The second pillar of responsible AI at project44 is System Redundancy. This principle recognizes that AI is only as good as the data and systems supporting it.  

To minimize risk, we build multiple layers of validation and backup into our AI workflows. In other words, we never want a single point of failure or a single source of truth that, if wrong, could lead to a poor decision going unnoticed. Every AI-driven action should be based on the most accurate, up-to-date information available. Wherever possible, it should be cross-checked by another model, dataset, or rule set before it affects the physical world of shipments and inventory. 

Data depth and redundancy  

Our platform integrates an unparalleled breadth of supply chain data in real time – no, really, project44 has the largest supply chain dataset in the world — which we leverage fully before an AI agent acts.  

With over 255 million shipments handled and two million carriers in our network, project44 has the largest supply chain dataset in the world. project44’s system processes around 3.3 million over-the-road shipment location pings per day in North America and about 66 million vessel position updates per day for global ocean freight. These millions of data points (ranging from truck GPS coordinates to port departure events, to weather feeds) serve as a live, robust data foundation for our AI. With this volume and diversity of incoming information, we can constantly cross-verify AI outputs against reality in real-time. If one signal is inaccurate or delayed, chances are another signal will catch it. This redundancy of data ensures our algorithms are basing decisions on a consistent, correct picture of the supply chain environment.  

For example: if our predictive model forecasts a delay, we corroborate it with real-world updates from the field. Is there actually a traffic jam reported on that route? Did the truck’s last ping confirm that it’s stationary? We verify through these redundant data channels before the AI’s decision (like re-routing a shipment) is fully trusted. 

Algorithmic breadth and redundancy  

We also embed redundancy at the algorithmic level. Rather than relying on a single model’s output, our architecture often involves ensemble approaches or fallback rules. If Model A flags an anomaly that would trigger a drastic action, we have Model B (or a set of business rules) double-check that anomaly.  

In scenarios where an AI action could have severe consequences (for example, automatically reordering inventory or re-routing high-value goods), we ensure there’s a safeguard: this could be another model cross-validating the decision, or even a human-in-the-loop approval for the most critical decisions. The goal is that no single AI insight is ever blindly trusted without verification. 

An eye for data quality  

To further strengthen data reliability (a core part of redundancy), we’ve introduced AI-driven data quality agents into our platform.  

These agents work continuously to identify and fix data issues so that our AI isn’t operating on faulty or missing information. For instance, if a particular carrier’s tracking feed goes silent or a piece of shipment information looks inconsistent, an AI Data Quality Agent will autonomously resolve that gap – often before anyone even notices the problem. This has led to significant improvements in the usefulness of our customers’ data, reducing data gaps by up to 50% and eliminating many manual follow-ups to carriers. By proactively ensuring data completeness and accuracy, we reduce the risk of an AI making a bad call due to incomplete data.  

System redundancy, in summary, is all about robustness: having multiple “eyes” on the problem so that even if one element of the AI system falters, another compensates to keep decisions correct and reliable. 

Why Transparent, Responsible AI Matters for Supply Chain Leaders 

By employing the techniques covered above, we greatly improve the explainability and transparency of our AI systems—from prompt breakdown to redundant data. But explainable AI isn’t just an academic nicety; it’s a strategic imperative for industries like supply chain and logistics.  

As AI becomes more embedded in decision-making, leaders must ensure these systems are transparent and accountable. Here are a few reasons why:  

• Building Trust and Adoption: To fully embrace AI-driven solutions, organizations need to trust them. Explainability builds that trust by illuminating the AI’s decision-making process. When users see why an AI made a recommendation, they are far more likely to accept and adopt it.  

• Ensuring Fairness and Ethics: AI models can inadvertently pick up biases from historical data, which might lead to unfair or suboptimal decisions (e.g., systematically favoring one carrier over another for the wrong reasons). Explainable AI techniques help detect and correct such biases by allowing us to examine how the AI is making choices—lining up with moral priorities, but often legal ones as well.  

• Regulatory Compliance: Policymakers, both in and out of the logistics industry, have started to require transparency in automated decision-making. Whether it’s GDPR’s provisions on algorithmic transparency in Europe or industry-specific guidelines, being able to explain AI decisions is quickly moving from a “nice-to-have” to a legal necessity.  

• Risk Management and Error Reduction: Even the best AI will make mistakes or face novel situations. Explainability is like an early warning system for these issues. By understanding a model’s weaknesses or the scenarios where it struggles (something we often discover through analyzing its decision logic), we can mitigate risks preemptively. If an AI’s explanation for a decision doesn’t make sense, it alerts us to investigate further before any harm is done. In supply chain, this proactive approach can prevent costly errors—such as misrouting a batch of products and missing a customer deadline.  

• Improved Innovation and Productivity: When AI systems are transparent, our product development and data science teams can iterate and improve them faster. Explainability shines light on why a model performed well or poorly, guiding better tweaks and innovations. This leads to more efficient AI systems that do their jobs more effectively, reducing the time and cost associated with debugging AI issues along the way.  

In sum, explainable AI increases trust, ensures fairness, supports compliance, and accelerates the adoption of AI solutions across the supply chain. It transforms AI from a magic box to a tool everyone can understand and benefit from.  

For executive leaders overseeing supply chain operations, investing in explainability is investing in the long-term success and acceptance of AI in the organization. 

Leading the Way in Responsible AI 

Project44 is committed to leading by example in the realm of AI for logistics. We are investing heavily not just in what AI can do but also in making sure we understand what it’s doing at every step and have safeguards when it decides to do something unexpected.  

By emphasizing Interpretable Output and System Redundancy in our AI development, we ensure that our solutions are also safe, transparent, and reliable. We’ve seen firsthand that this approach fosters greater trust with our customers and partners — they can innovate faster with us because they trust the intelligence we provide. 

This balanced, risk-aware strategy enables us to deploy advanced AI (from real-time predictive analytics to generative supply chain assistants) in mission-critical operations like vaccine distribution, production line scheduling, and disaster recovery logistics with confidence. 

Responsible innovation in AI isn’t a one-time achievement; it’s a continuous journey of improvement. As we develop ever more sophisticated capabilities, you can expect project44 to remain at the forefront of marrying AI advancement with accountability. We will continue to share our learnings and approaches with the industry, because raising the bar for responsible AI benefits everyone — making global supply chains more efficient, resilient, and trustworthy.