As the application of artificial intelligence and machine learning permeates life sciences organizations, the potential for business value is immense. However, as these technologies are increasingly integrated into business decision-making, it is important to understand not only the insights they provide, but also the underlying drivers behind these insights.

From drug discovery and development to clinical trial design to direct patient care recommendations, explainable AI can bring transparency and clarity to areas in which the intuition is as important as the recommendations themselves. In this short video we explore how explainable AI and machine learning methodologies help to combat the “black box” nature and perceptions of AI, to elevate our understanding of complex processes from whether something occurs to why it does, and to transform insights from interesting to actionable.

What to expect:

• An introduction to explainable AI
• Exploration of approaches to explainable AI and their benefits – SHAP, LIME, and more
• Consideration of ethics in ML and AI and the value of interpretable predictions in the context of increasing AI regulation
• Experience-based examples of how explainable AI can drive actional insights for life sciences business benefits

Is it for you?

• Are you a business leader exploring the strategic benefits of applying explainable AI methodologies for improved decision-making in life sciences?
• Are you building models and considering how model interpretability can help further drive adoption across use cases?
• Are you a life sciences professional seeking to better understand AI’s role in enhancing transparency?