AI systems are becoming increasingly capable of making complex decisions, but often, even their creators can’t fully explain how those decisions are made. When organizations can’t account for why AI made a particular choice, it becomes difficult to trust the outcome, especially when lives, jobs, or legal decisions are on the line. 

Explainable Artificial Intelligence (XAI) aims to make AI’s logic more understandable. XAI can help businesses and users interpret and leverage the systems they use while meeting legal or ethical needs for traceability. This is particularly important in the life sciences industry, where AI can have high-risk implications.  

However, explainable AI in the life sciences industry can be challenging to define, as there is no specific set of criteria that makes a system “explainable.” Critics have claimed these tools are overrated and stifle innovation. In this piece, we will explore why it is crucial for companies to understand their AI tools, the challenges to get there, and the ways AI can be explained, particularly in the life sciences industry. 

Why Do We Need Explainable AI in the Life Sciences Industry? 

Creates Trust 

Many complex AI systems function as a black box, where only the inputs and outputs are known. While progress is being made in understanding black box models, these systems still lack full transparency. As a result, it can be difficult for companies to audit, debug, and fully trust automated decisions, particularly in high-stakes or regulated environments. To trust a model, companies need to understand how it reached that decision.  

Creates Accountability 

If an AI system lacks transparency, then it lacks accountability. This lack of accountability can lead to legal or regulatory issues, including violations of anti-discrimination laws or patients being unable to give informed consent due to the influence of a biased AI model on their treatment. Potential bias in AI models used in healthcare can trigger FDA regulatory action, including recalls, especially when such models are classified as medical devices and their bias results in safety or effectiveness concerns.  

Improves the Science 

Interpretable AI models with scientific applications can also uncover new insights into the science itself. Interpretability in models for genome engineering or automated radiology report generation can lead to better disease treatments, increased access to healthcare, and improved overall human health. Even more, XAI has been used to identify existing drugs that can be repurposed for other diseases and to discover biomarkers for rare diseases, applications where both performance and transparency are crucial.  

Prevents Systemic Bias  

XAI can help fight algorithmic bias. Without understanding how a model makes a prediction, systemic bias can appear in AI models, creating legal and ethical issues. Within the life sciences industry, bias can skew results, including underrepresented groups in clinical trials, incorrect treatment recommendations, and unfair healthcare resource allocation. Once users can explain AI behavior, they can identify any bias within the tool and take steps to either fix the model or avoid making decisions based on its recommendation.  

Regulation and Legal Requirements 

Growing regulatory scrutiny is making XAI not just desirable but necessary. The European Union’s AI act, for example, mandates transparency and documentation across AI development, particularly for high-risk systems. These include models that profile individuals or process sensitive personal data (such as those used to assess health), placing them under stricter compliance obligations. As regulation continues to evolve, the ability to explain AI decisions will remain central to both legal compliance and scientific credibility. 

Challenges with Explainable AI 

Despite its growing importance, XAI has its detractors. Critics argue that the push for explainability can be overrated and may even hinder innovation. They contend that a lack of transparency does not inherently reduce an AI system’s reliability, especially in cases where models are so complex that even their creators struggle to understand them fully. From this perspective, the priority should be on rigorous testing and validation to ensure performance and fairness, rather than insisting on interpretability. 

Another commonly cited challenge is the accuracy-explainability trade-off, where more complex models tend to be more accurate but less explainable. Simpler models often have built-in interpretability, while complex models typically require additional methods to explain resulting decisions.  

For example, a deep learning model might more accurately predict whether a patient is at risk of post-treatment complications compared to a simple regression model but offer little insight into how it arrived at that conclusion. Critics of XAI argue that imposing explainability constraints could compromise model performance, especially in high-stakes applications where accuracy is paramount. 

Is Implementing Explainable AI Possible? 

Making AI explainable is essential but difficult, especially for large, complex models. Several techniques exist today, each with their own trade-offs. 

The most common approach involves feature attribution methods. Teams can analyze how shuffling around or removing specific inputs affects predictions, offering clues about feature importance and how the model works generally.  

For a more precise analysis, many leading organizations, including Microsoft, turn to SHAP. Based on game theory, SHAP provides a consistent and mathematically rigorous way to quantify each input’s contribution to a model’s output. For example, an AI model predicting a patient’s risk for complications from a medical procedure might identify blood pressure as the most influential factor and cholesterol as the second most influential for Patient A, while highlighting age as the primary driver of risk for Patient B. Feature attribution is most appropriate for classical, tree-based models like decision trees and gradient boosting trees, where inputs can be more directly traced to outcomes.  

Other AI leaders, such as Anthropic, are pioneering methods like circuits and attribution graphs to help explain large language models (LLMs), which are the engines powering generative AI. Anthropic uses circuits to map how different parts of the model collaborate and attribution graphs to show the influence of inputs. While these innovative techniques help us understand LLMs, they remain limited in fully explaining model behavior and are time- and resource-intensive to do. 

Lastly, organizations can build a simpler model that mimics the complex one and then explain the simpler model instead. However, creating this surrogate model requires expertise and can still be misleading if it does not approximate the original model well enough.  

Despite challenges, leveraging explainability tools and techniques remains a crucial first step toward developing more trustworthy and transparent AI systems. Ongoing research continues to refine and expand these methods. 

Looking Ahead 

As organizations gain visibility into how models generate their outputs, they can make more informed decisions, reduce bias, and increase transparency across operations. While XAI can introduce complexity and cost, it’s essential to build AI systems that are not only effective but also trustworthy. Prioritizing both model performance and explainability is key to avoiding critical failures and ensuring long-term success.  

Our AI strategy experts are ready to help you navigate this journey and realize the full value of responsible, explainable AI. 

Contributions by Joseph Malandra