What Managers Should Ask About AI Models and Data Sets

This article, published on December 18, 2023, by Roger W. Hoerl and sourced from MIT Sloan Management Review, addresses the critical role of senior business managers in preventing Artificial Intelligence (AI) project failures. It emphasizes that managers possess the power and responsibility to ensure AI initiatives succeed, but this requires a fundamental understanding of how to evaluate the data sets and AI models being utilized.

The Manager's Crucial Role in AI Success

AI projects often fail due to a lack of proper oversight and understanding at the management level. Managers are tasked with making strategic decisions, and when it comes to AI, this includes scrutinizing the foundational elements: the data and the models. The article posits that by asking the right questions, managers can proactively identify potential issues, align AI solutions with business objectives, and ultimately drive successful adoption.

A Framework for Data Set Evaluation

One of the core contributions of this article is the provision of a framework designed to help managers identify the most suitable data sets for their specific business problems. This involves understanding the context, relevance, and quality of the data. Key considerations include:

Relevance: Does the data directly address the business problem at hand?
Representativeness: Does the data accurately reflect the real-world scenarios the AI will encounter?
Quality: What are the potential biases, errors, or limitations within the data?

Six Essential Questions for AI Developers

Beyond data evaluation, the article outlines six critical questions that managers should pose to AI developers. These questions serve as a guide for discussions both before and during the deployment of AI models, ensuring transparency, accountability, and alignment.

Data Suitability: Is this data set appropriate for the business problem we are trying to solve? Does it accurately represent the real-world scenarios the AI will encounter?
Data Quality: What are the known limitations, biases, or errors in this data set? How have these been addressed?
Model Performance: How does the AI model perform on key metrics relevant to our business goals? What is its accuracy, precision, recall, etc., and in what contexts?
Model Explainability: Can the model's decisions be explained or understood? Is there a way to audit or debug its behavior?
Ethical Considerations: Have potential ethical implications, such as bias or fairness, been assessed? What measures are in place to ensure responsible AI deployment?
Deployment Readiness: What are the requirements for deploying and maintaining this AI model in our production environment? What are the ongoing monitoring and update strategies?

Mitigating Risks and Driving Adoption

By engaging with these questions, managers can foster a more informed approach to AI implementation. This proactive stance helps in:

Preventing Project Failures: Identifying and addressing issues early on.
Ensuring Alignment: Making sure AI solutions support strategic business goals.
Promoting Responsible AI: Considering ethical implications and fairness.
Improving Decision-Making: Empowering managers with the knowledge to make sound choices.

Conclusion

In essence, the article empowers managers to be active participants in the AI development lifecycle, rather than passive observers. By understanding and asking the right questions about data sets and AI models, managers can significantly improve the likelihood of successful AI adoption, mitigate risks, and harness the full potential of AI for their organizations.

Product Details:

Product #: SR0166
Pages: 7
Publication Date: December 18, 2023
Source: MIT Sloan Management Review
Price: $8.95 (USD)

Related Topics: Algorithms, AI and machine learning.