From Black Boxes to Clear Insights: The Role of Causal AI and Geminos Causeway in Explainable AI
Introduction
In the evolving landscape of artificial intelligence (AI), the need for transparency and understandability in AI models has led us to a pivotal innovation: Causal AI for business. The Geminos Causeway software platform is built to revolutionize the way businesses make decisions, while also making AI-backed decisions understandable for a wide range of users – from data teams to executives. In this blog, I'll explore how Causal AI, particularly through Geminos Causeway, enhances Explainable AI (XAI), followed by real-world use cases in oil and gas, AgTech, and marketing.
The Power of Visual Causal Models in Explainable AI
One of the key ways Causal AI supports Explainable AI is through visual causal models -- one of the features of Geminos Causeway that customers appreciate. These models offer a user-friendly interface that makes complex systems and AI-driven decisions understandable for both data teams and business stakeholders. By translating intricate data relationships into a visual representation, these models form a rallying point for collaboration. In addition, the Geminos Causeway approach to displaying cause-and-effect relationships between variables and desired outcomes, enables informed decision-making, ensuring that every stakeholder, regardless of their technical background, can understand the logic of AI-driven decisions.
Balancing Decision Tradeoffs with Geminos Causeway
A key strength of the Geminos Causeway approach is helping decision teams easily manage the complexities of different trade-offs in their choices. Just like pilots managing a complex aircraft, decision teams are presented with numerous 'levers' that represent different variables affecting their desired business outcomes. This approach allows teams to easily understand that changing one variable might yield substantial benefits but could also be costly or impractical. However, the Geminos Causeway platform allows teams to assess alternative approaches. For example, modifying a combination of variables may give a similar result with fewer tradeoffs. This approach allows businesses to rethink their strategy around decision making, leading to improved business outcomes.
Enhanced Trust and Transparency
Causal AI goes beyond traditional correlation-based analysis by establishing cause-and-effect relationships. This approach enables Geminos Causeway clients to understand not just what is happening, but why it's happening, thereby enhancing the trust and transparency of AI systems. In a world where AI decisions can have significant consequences, this level of understanding is crucial for accountability and ethical AI practices.
Use Case Samples: Real-world Applications of Explainable Causal AI
Oil and Gas
In the oil and gas industry, predictive maintenance of equipment is critical. While current AI and ML models can predict equipment failures, can’t actually uncover the reasons why assets might be prematurely failing. The Geminos Causeway platform adds the why to AI by helping teams uncover the variables that are leading to equipment failure. Maintenance teams can not only take equipment offline before unplanned failures, but they can also address the reasons why equipment fails.
Agriculture and AgTech
Causal AI is playing an important role in crop yield optimization. By analyzing variables like soil conditions, weather patterns, and crop types, the Geminos Causeway platform can not only forecasts yields but will also explains the contributing factors. These insights are valuable for not only large-scale farmers that are planning resource allocations, but other upstream and downstream stakeholders in the agriculture value chain.
Marketing
For marketers, understanding customer behavior is critical. Causal AI is helping marketing teams better understand complex consumer data and the variables that influence purchasing decisions. This understanding enables marketers to more finely tune their strategy and ensure better ROI on marketing campaigns while avoiding activities that do not lead to a consumer becoming a customer.
Getting Started with Causal AI
In an era where AI is no longer a choice but a necessity, ensuring that it's responsible, transparent, and explainable is not just an option—it's an imperative.
Embracing Causal AI and its ability to support your explainable AI strategy isn’t about new algorithms, instead it is about a new approach to uncovering relationships in your data. This approach is centered upon cause-and-effect and identifying factors that have a positive or negative impact on your desired business outcome.
To understand how the power of Causal AI can impact your business, we encourage you to view a demo or schedule a quick chat to discuss how Causal AI can help your organization revolutionize decision making processes.
