How Small Data Impacts Explainable AI

Explainable AI

Decision-making is one of the most difficult things to do in the business world. It implies to make choices, sometimes hard, justify them and have the right explanations because you will be held accountable. Artificial Intelligence has become a great asset for decision-making.

However, business experts need to understand why and how AI makes recommendations. The main issue is that most AI systems are not meant to be understood. Their outcome delivery is very complex and hardly understandable even if you hold a PhD in Data Science. 

Explainable AI algorithms aim to overcome these obstacles with transparency, interpretability and ease-of-understanding to enlighten decision-making. Often overlooked, Small Data has a great part to play in making AI understandable and actionable for business experts.

Why Explainable AI matters

Explainable AI is a category of Artificial Intelligence that provides its users with methods and means to easily understand generated predictive models. Explainable AI benefits are quite clear for companies, both internally and externally. Easy-to-understand AI insights help them convince employees to trust and use AI-based tools. Business experts can then make more informed, better decisions knowing that these tools can justify the insights they provided. 

Transparency and Explainability become clearly required as Artificial Intelligence gets increasingly more used in customer-facing positions. For instance, banks use Explainable AI to provide credit officers with risk information on their customers. They get insights on why the credit is accepted or not, and advise customers on how to improve their application. Here, an opaque system would create frustration and discrimination suspicion among customers and, incidentally, potential major losses for the bank.

Furthermore, regulations such as the European AI act are now making Explainable AI a requirement in specific business cases. The movement will accelerate in the next few years. Organizations are now required to conduct AI audits, assess its risks and make sure it can be analyzed by external examiners. It requires Artificial Intelligence algorithms to be transparent, understandable and explainable enough by a human being.

What Small Data can bring

Most of the literature on Explainable AI focuses on easier-to-decipher algorithms. The logic behind it is double:

  • For business experts, understanding the algorithm means that they can understand why it recommends such decisions.
  • For IT professionals, understanding how the algorithm gets these results simplifies the debugging process and ensures its operational efficiency.

It’s a good approach, but it forgets one key aspect of Artificial Intelligence: data. Every Machine Learning or Deep Learning algorithm requires data to create models. If your algorithm is crystal clear but your business experts can’t comprehend data, they will still doubt AI outcomes and insights.

This is where Small Data solutions can help. Indeed, Small Data by itself is transparent, understandable and explainable. Hence, applying Explainable AI algorithms to Small Data creates predictive models that can be trusted. If you understand the data and the algorithm that analyzes it, you can understand the outcomes and make enlightened decisions, being right 75% of the time.

Explainable AI for Small Data: a use case

At MyDataModels, we recently worked on an Employee Retention use case with one of our customers. HR wanted to anticipate and prevent potential leaves, based on their HRIS data.They had a few thousand records and 15 characteristics per employee for the last three years – some data that they could easily comprehend as they work on it on a daily basis. 

However, there were still too many variables to correlate manually to identify patterns. Our AI algorithm applied to their data generated a predictive model to find accurate, explainable correlations and identify the employees more likely to leave, being right 75% of the time and understandable enough for them to trust it. HR teams were then able to find the right triggers to make employees stay and customize the HR track for each of them.

This kind of use case shows the power of combining Explainable AI with Small Data. They open a new way of working and augment Business experts with additional insights that they can fully comprehend and explain, breaking the barrier between humans and machines. So if you want to boost the power of your Small Data with Explainable AI, contact us now! We’ll be happy to help you uncover new insights towards a brighter AI future.

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