Artificial Intelligence (AI) is transforming life as we know it, and modern medicine is no exception. In the evolving world of biotechnology, understanding the decision-making processes of AI models has become crucial. Two emerging fields, Causal Machine Learning (Causal ML) and Explainable AI (XAI), aim to address the challenge of making AI systems more transparent and interpretable. In this article, we will delve into the differences between Causal AI and Explainable AI, exploring their significance, how they contribute to the evolution of machine learning, and what that means for the future of precision medicine.
Why Does Explainable AI Matter?
Explainable AI focuses on creating AI models that humans can understand. The need for explainability arises in different scenarios. Take a bank’s use of AI to vet mortgage applications for example: stakeholders, including end-users, domain experts, data scientists, regulators, managers, and board members, all have vested interests in understanding and trusting the decisions made by AI systems. In most cases, explanations are legally required, so AI explainability is crucial for regulatory reasons.
The above relates specifically to mortgage applications, but explainability matters in almost every AI use case, especially in verticals where there is an element of risk involved. Ultimately, explainability enables users to better audit, trust, and gain insights from AI systems.
In the world of precision medicine, Explainable AI leverages computation and inference to generate insights, empowering clinicians and scientists in their decision-making.
How Do Machine Learning Algorithms Provide Explanations?
Today’s AI systems, based on machine learning, often function as black boxes, making it challenging for humans to comprehend their decision-making logic. XAI solutions, such as LIME (local interpretable model-agnostic explanations), and SHAP (SHapley Additive exPlanations), employ post hoc explainability techniques. LIME perturbs input data to create a synthetic dataset, training a white-box model to explain the original black box model. SHAP, on the other hand, identifies the marginal contribution of each feature to a given prediction.
Challenges with Post Hoc Explainability
While post hoc explanations like LIME and SHAP offer insights into black box models, they have limitations. These explanations don’t automatically make the original model trustworthy, and building a second model for explanation introduces uncertainty. Post hoc explanations also come too late in the machine learning pipeline, lack actionability, and fail in dynamic systems.
What is Causal AI and Why Does Causal AI Produce Better Explanations?
Causal AI is a novel approach to machine intelligence that focuses on understanding cause-and-effect relationships. Causal models are inherently interpretable white-box models, reinforcing trust. These models generate explanations earlier in the AI pipeline, allowing domain experts to impose fairness criteria and restrictions before full model building. Causal explanations work for dynamic systems, providing guarantees on model behavior in all circumstances.
Causal AI vs Explainable AI:
- Trust and Transparency:
- Explainable AI: Relies on post hoc explanations, introducing doubts about model trustworthiness.
- Causal AI: Inherently transparent with qualitative components describing cause-and-effect relationships.
- Timing of Explanations:
- Explainable AI: Provides explanations after the model is built and in production.
- Causal AI: Offers ante hoc explanations, allowing domain experts to intervene before model completion.
- Adaptability to Dynamic Systems:
- Explainable AI: Fails in dynamic systems and doesn’t guarantee future model behavior.
- Causal AI: Ensures guarantees on model behavior even in unprecedented circumstances.
- Actionable Insights:
- Explainable AI: Limited actionability and challenging to apply to business decision-making.
- Causal AI: Enables easy tweaking of models based on explanations, facilitating proactive interventions.
Causal AI is revolutionizing precision medicine by unraveling the intricacies of disease causality, facilitating the development of personalized treatment plans, and addressing challenges associated with high-dimensional data. The integration of Causal AI in drug discovery and clinical development holds promise for improving patient outcomes and advancing our understanding of the causal factors behind diseases.
At BioAI, we’re accelerating the discovery of novel biomarkers and drug targets tailored to specific patient profiles, ushering in a new era of precision medicine. BioAI’s PredictX Platform intakes your data and generates novel insights. PredictX integrates world-leading AI methodologies, including In-silico phenotype projection and integrated deep learning to map the causal biology of disease, develop digital biomarkers, and identify drug targets.
Causal AI and Explainable AI each represent distinct approaches to addressing the challenge of making machine learning models more interpretable. While Explainable AI relies on post hoc explanations that may lack trust and transparency, Causal AI provides inherently interpretable models with guarantees on behavior and actionable insights. The future of AI may involve a synergy between Causal ML and Explainable AI, combining the strengths of both to build accurate, transparent, and interpretable models. Learn more about how BioAI is using multimodal AI to transform precision medicine.