EthonAI contributes article to United Nations Conference on Trade and Development

EthonAI Co-Founder Julian Senoner has recently contributed an article to the United Nations Conference on Trade and Development. The article highlights the role of modern data analytics in the context of organizational decision-making.

With the recent rise of AI models there have been impressive developments in both creative content generation as well as automation. However, the article of our Co-Founder Julian Senoner emphasizes that AI still faces two key challenges in decision-making. First, the complexity and lack of transparency of many AI models can hinder trust and adoption, as they often function as “black-boxes.” This lack of transparency leads to distrust among domain experts who cannot validate AI recommendations against their own expertise. Second, AI systems have difficulty with causal reasoning, a critical aspect of decision-making. Understanding the correlation between variables is useful, but grasping the causality behind events is essential.

To tackle these challenges, the article advocates for the design of AI systems that are explainable and can integrate domain knowledge. Such systems provide clarity into their recommendations, thereby allowing users to comprehend the reasoning behind them and gain trust in their outputs.

The article concludes that while data and AI can lead to better decision-making, human involvement remains crucial. Recent developments in Explainable AI and Causal AI provide a promising path forward. These methods help users understand how AI systems operate and enable them to incorporate their expertise when evaluating AI recommendations.

The full article is available on the United Nations Conference on Trade and Development’s website.