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Causal AI: The next frontier in transparent and accountable AI systems
As artificial intelligence evolves, causality has emerged as the cornerstone for ensuring transparency and trust, transforming industries such as advertising while redefining decision-making. Unlike gen AI, which focuses on predictions and patterns, causal AI explores the “why” behind outcomes. In doing so, causal AI has become a key linchpin in the emergence AI understandability.
Causal AI opens the “black box” of traditional AI, empowering organizations with insights into variable interactions and their influence on outcomes. This level of transparency fosters accountability and enhances explainability, qualities necessary for navigating increasingly stringent regulations.
In a causal AI use case, theCUBE Research’s Scott Hebner explored the real-world impact of causal AI, detailing how Scanbuy Inc., a mobile advertising company, and Howso Inc., a leader in AI understandability platforms, joined forces to reshape trust and transparency in AI systems.
Transforming ad tech with AI understandability
The partnership between Scanbuy and Howso showcases the transformative potential of causal AI in programmatic advertising. By leveraging causal methods, Scanbuy achieved a tenfold return on investment in mobile advertising campaigns without additional spending.
First, the company achieved real-time adaptability for ad models while maintaining regulatory compliance. Additionally, it boosted transparency and visibility into campaign effectiveness metrics and patterns.
The broader implications of causal AI are transformative, according to Hebner. With mounting regulatory pressures, such as Europe’s Digital Services Act, advertisers must demonstrate how AI decisions are made and provide mechanisms for user control. By embracing causal AI, industry players can meet these demands while optimizing performance.
A recent Databricks survey revealed that 56% of AI pioneers are already experimenting with causal AI, with adoption expected to surge by 2026. Experts project a 41% compound annual growth rate for the causal AI marketplace through 2030, potentially exceeding $1 billion in value.
The Scanbuy use case succinctly illustrates how causal AI understandability will reshape how businesses solve complex problems, Hebner concluded.
Read theCUBE Research’s full analysis here.
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