How can we build AI systems we can trust?
Why transparency in AI decision-making matters and how it impacts trust, fairness, and usability.
AI seems to be everywhere these days.
Part of every online post and conversation.
Helping us shop smarter.
Improves innovation.
Helps businesses run more efficiently.
But here’s the catch: some of these AI systems are so complicated that even the people who built them don’t fully understand how they work, especially when it comes to Generative AI applications.
Crazy, right?
These are called “black box” systems because their decision-making process is a mystery.
And yet, people often trust these systems more than simpler, easier-to-understand ones.
But what happens when something goes wrong? Without knowing why an AI made a decision, it’s tough to fix the problem, explain it to others, or trust it again.
That’s why understanding how AI works—something called Explainable AI (XAI)—is kind of a big deal.
The Unexpected Trust in Black Boxes
A fascinating study involving the retail giant Tapestry—parent company of luxury brands like Coach and Kate Spade—revealed a paradoxical finding. They used a complex AI system to figure out how to distribute inventory. The system was highly accurate, and employees trusted it—even though they didn’t understand how it worked.
Let that sink in for a moment: Employees trusted the complex AI systems more than a simple to understand solution.
The key to building that trust was how accurate the system was. This "black box" model gave suggestions on inventory allocation more accurately and reliably than the human-understandable model, even though the recommendations were initially confusing.
This counterintuitive trust in less transparent systems raises an important question:
How do we ensure that AI systems are not just effective, but also trustworthy and understandable?
Are all AI systems “Black Boxes”?
Black box models in AI are complex systems whose internal workings are not visible or understandable to users. They can make predictions or decisions without explaining how they arrived at them. It's like having a magical cookbook that gives you a recipe based on the ingredients you have, without telling you why it chose that recipe. Or imagine using a GPS app that simply tells you "Take this route, trust me" without any explanation for why that route is optimal.
Not all AI systems are black boxes and this is exactly where Explainable AI comes fits in.
What Is Explainable AI?
To make these black boxes more user-friendly, scientists developed Explainable AI (XAI). XAI tries to peel back the layers of the AI black box to give us a glimpse of what's happening inside.
For instance, in a medical diagnosis tool, XAI might highlight the data points (like certain symptoms or test results) it considered most important when diagnosing a patient.
For AI-enabled GPS systems, XAI would allow the app to say "I recommend this route because it avoids heavy traffic on the highway at this time of day."
Explainable AI (XAI) is all about pulling back the curtain on how AI makes decisions. It doesn’t just tell you what the decision is—it shows you why. It aims to build trust and understanding, even if the full complexity of the model remains hidden.
For example:
In healthcare, an AI diagnosing a disease could highlight symptoms or test results it used to reach its conclusion.
In finance, a system approving a loan might explain that the decision was based on a person’s credit score, income, and payment history.
When people understand why an AI made a choice, they’re more likely to trust and use it effectively.
The Crucial Role of Explainable AI
Explainable AI isn't just a nice-to-have feature; it's a critical component in building trust. When users understand how decisions are made, especially in high-stakes situations, they are more likely to trust and effectively use AI systems. In domains like healthcare, finance, and criminal justice, where decisions can significantly impact lives, explainable AI becomes non-negotiable. Regulatory bodies in these industries are increasingly demanding transparency to prevent biases and ensure fairness.
A pivotal example of regulatory demand for explainability comes from the European Union's General Data Protection Regulation (GDPR), enacted in May 2018. GDPR is a comprehensive data protection law that imposes strict rules on how personal data is collected, stored, and processed. One of the notable aspects of GDPR is the "right to explanation," which mandates that individuals have the right to understand the decisions made by automated systems affecting them. This regulation effectively makes explainable AI a legal requirement in many cases, pushing organizations to adopt practices that allow for greater transparency in AI-driven decisions.
This regulatory environment underscores the importance of explainable AI not just for ethical reasons but also for compliance. As businesses globally interact with European citizens' data, understanding and implementing explainable AI systems becomes essential. These systems ensure that all stakeholders, including users, regulators, and decision-makers, can trust and verify AI decisions, promoting a more accountable and transparent digital ecosystem.
Categories of XAI Techniques
XAI can be segmented into several categories, each addressing different aspects of the AI decision-making process:
Data Attributes: Explaining AI decisions based on the input data used.
Model Behavior: Understanding the internal workings and logic of the AI model.
Outcome Explanation: Providing reasons for the decisions in a user-friendly manner.
Model Assessment: Evaluating the AI’s performance and its decision-making process.
These categories help developers and end-users alike to dissect and comprehend the AI systems at different levels, from the origin of the data inputs to the logic behind the outputs.
Importantly, not all of these features have to necessarily be human-facing through user interfaces. Some of these techniques enable technical teams to better understand the underlying models used in the solution to improve them.
Two Ways to Make AI Clearer
1. Explainable AI
Explainable AI includes methods and techniques used to explain the decisions of complex, "black box" models after the fact. It often involves creating additional models or tools to elucidate the black box decisions.
2. Interpretable AI
This is simpler. Instead of needing explanations, the system’s decision-making process is built to be understandable by humans from the start. In other words, AI’s process is understandable without the need for additional explanation tools.
Both approaches aim to make AI decisions more transparent and trustworthy, but they do so in fundamentally different ways. The right choice depends on what you’re using AI for.
What is Interpretable AI exactly?
Interpretable AI is designed from the ground up to be understandable and straightforward, with no hidden mechanisms. These models make decisions based on rules and patterns that humans can easily follow. Considering that the emphasis of most applications in business is on Generative AI enabled applications, you won’t have control of implementing Interpretable AI strategies. Reason being, most of those models are provided by large organizations like Microsoft and OpenAI that poured millions in building and training those models.
Implementing Explainable AI vs. Interpretable AI
Choosing between explainable and interpretable AI depends largely on the context and the specific needs of the application:
When to Implement Explainable AI: This approach is suitable when using complex models that provide high accuracy but are difficult to interpret, such as generative AI systems or natural language processing. Explainable AI helps to bridge the gap between these powerful models and the need for transparency.
When to Implement Interpretable AI: This is ideal for high-stakes environments where decisions directly impact human lives, such as in medical diagnostics or criminal justice. In these cases, simpler models that are easy to understand and audit are preferable, even if they sacrifice some level of sophistication.
We need AI systems to be more Transparent
The implications of XAI and Interpretable AI are vast, touching every aspect of our lives where AI has a role. Let's consider two examples: health care and criminal justice.
In health care, imagine an AI system designed to predict patient risks, such as the likelihood of developing heart disease. An explainable AI model might analyze a patient's medical history and highlight the factors influencing its prediction, such as age, weight, and cholesterol levels, without revealing its internal logic. On the flip side, an interpretable AI model in this context would make its prediction based on a transparent set of criteria and rules that a doctor can easily understand and explain to the patient, fostering trust and enabling informed decision-making.
In the realm of criminal justice, AI can assist in predicting reoffending risk to inform parole decisions. A black box AI might generate a risk score without explaining its rationale, leading to decisions that could potentially be based on errors or biases in the data. However, an interpretable model would use clear and justifiable criteria, such as the nature of the crime and the individual's history of rehabilitation, to make its recommendations, allowing for decisions that are both fair and transparent.
Conclusion
AI is powerful, but it’s not magic.
To get the most out of it, people need to trust it—and trust comes from understanding.
By making AI more explainable or interpretable, you can build systems that don’t just work well but are also clear, fair, and reliable.
Incorporating XAI into the development and deployment of AI systems is more than a regulatory requirement; it's a commitment to ethical technology use, ensuring that AI serves humanity transparently and justly.
The best AI systems of the future won’t just be accurate—they’ll be transparent. And that’s what will make all the difference.