In the intricate world of artificial intelligence, the demand for transparency and understanding has grown exponentially. The challenge lies not only in the technical realm but extends into the broader domains of business and society. 

Imagine encountering a decision made by an AI system that affects you directly. Whether it's a loan application rejection, a hiring decision, or even a medical diagnosis, the 'why' behind these outcomes is crucial. 

While the ability to explain the reasoning behind a system's decision is crucial, it alone is insufficient. We must take it a step further, ensuring the explanation is also easily understood. When users understand how and why the model came to a decision, they are more likely to trust it. Say your loan application was rejected, but you received an explanation clarifying what factors were used to make the decision, wouldn’t you trust that the system’s reasoning was fair and unbiased?

Addressing the transparency and comprehensibility of AI poses a shared challenge for all stakeholders, ranging from data scientists and business decision-makers to consumers. So the question that remains is why are current interpretation methods not enough and how can we fix the problem?

It’s a Problem for Everyone

When data scientists construct AI/ML models, they typically utilize common interpretation methods. However, depending solely on these methods might not offer sufficient depth or flexibility to comprehend the data fully, potentially resulting in overlooked insights crucial for decision-making. Moreover, lacking robust explainability tools, data scientists may face challenges in effectively communicating their rationale to non-tech stakeholders, thus impeding transparency and trust in the AI/ML process.

The problem doesn’t end with the data scientists. The business implications of unclear AI explainability are substantial. When non-tech stakeholders fail to comprehend the outcomes, decisions are made without transparency regarding the model's reasoning. Business decision-makers need to understand the model to ensure it is aligned with business KPIs and rationalities. 

And then there are the issues faced by the risk officers and Model Risk Managers due to a lack of clear explanations. Explainable AI (XAI) is part of the validation and approval process, making it important for risk officers to understand the model’s decisions, so they can verify the model’s performance, reliability, and alignment with organizational objectives. Without a comprehensive understanding of how the model operates and makes decisions, risk officers may struggle to identify vulnerabilities or anticipate adverse outcomes, leaving the organization exposed to unforeseen liabilities. XAI should also be available in real-time during production to be certain that the decisions driving the business are accurate.

Additionally, Regulations require models to be explainable. On the compliance end, simplifying and articulating complex technical details to regulatory bodies can be challenging without clear explanations, potentially leading to misunderstandings or delays in regulatory approvals.

What Can We Do About It?

While common explainability methods serve specific purposes, they only provide an approximation of the decision-making process of a black-box model, rather than fully revealing the internal workings of the black box itself. Adding to this challenge is the fact that various model types need distinct explanation techniques, amplifying the burden of managing them alongside their corresponding models.

The reality is that the majority of common "Explainable" AI tools are only comprehensible to individuals with a robust technical background and intimate knowledge of the model's inner workings. While XAI is a valuable component of a technologist's arsenal, it falls short as a practical or scalable method for explaining AI and ML systems' decisions.

Instead, the pathway to achieving trust and confidence in a model’s decisions requires an expansion of the explanatory domain and a wider audience reach. This is where "Understandable AI" enters the chat—a system that not only caters to the requirements of non-technical stakeholders but also complements explainability tools tailored for technical teams.

Transparency forms the bedrock of understandability. Non-technical stakeholders must have complete access to every decision made by the models they oversee. They should be able to search through records based on key parameters, assess decisions individually and collectively, and conduct counterfactual analyses by adjusting variables to test expected outcomes.

To ensure AI is understandable, it's important to consider the broader context of model operations. Business owners should have visibility into the human decision-making that occurred alongside the model throughout its life cycle to build trust.

Explainability Is a Necessary Piece in Achieving Understandable AI

The necessity for explainability in AI extends beyond technical nuances. As we navigate toward a future increasingly driven by AI, considerations of regulations, consumer safety, and informed decision-making become paramount. Complete transparency in AI operations instills more confidence in stakeholders and ensures that the societal impact of AI is both positive and understood. 

By pushing for understandable AI, we pave the way for a future where AI systems are not only explainable but comprehensible to all. This is not just a technological evolution but a societal transformation that hinges on our ability to demystify the algorithms that shape our world.