Demystifying AI: Explainable AI for Stakeholders

Bridging the Gap Between Data Scientists and Stakeholders

Image generated using DALL·E 3 (OpenAI)

The world of data science and artificial intelligence (AI) is continuing to evolve, with that said, the significance of Explainable AI goes beyond technical circles. As AI becomes more prevalent in decision-making across various industries, it becomes essential to bridge the gap between data scientists and non-technical stakeholders.

In this article, we will discuss potential approaches companies can make to demystify Explainable AI for non-technical audiences.

Understanding the Need for Explainable AI

Picture a scenario where a stakeholder is presented with insights derived from a complex machine-learning model. While the results may add company value, the lack of understanding behind the model’s decisions can create a barrier.

Stakeholders are not as technical as data scientists, nor should they be.

Explainable AI bridges this gap by providing a more transparent and understanding explanation as to why models are making decisions. Providing stakeholders with these insights is more likely to enhance company buy-in and speed up the process of launching models into a production environment.

Applications of Explainable AI Across Industries

Explainable AI is not just a buzzword; it has practical applications across various industries. For example:

1. Healthcare: AI can assist in diagnosing diseases by analyzing medical data. However, medical professionals need to understand how these AI systems arrive at their conclusions to trust and act upon them.
2. Finance: Banks utilize AI for credit scoring, fraud detection, and risk management. Transparent AI models ensure these decisions are fair and comply with regulatory standards, thus maintaining trust with customers and regulators.
3. Retail: Recommendation engines suggest products to customers based on their past behavior. Explainable AI helps clarify why certain products are recommended, therefore improving customer experience and trust.
4. Manufacturing: Predictive maintenance…