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Data scientists explaining business better with explainable artificial intelligence

One of the most common questions that people go through when developing predictive or prescriptive models is needing to understand how the machine chooses to work. This issue isn’t limited to business users, as even ML engineers face difficulties explaining how their models work. In fact, a Mxnet poll conducted in 2017 revealed that 35% of AI professionals were uncertain about how their models were created, and 47% felt uneasy reporting the results to their colleagues.

Machine learning’s meteoric success has spawned a flood of Artificial Intelligence (AI) applications. Continued progress promises to generate autonomous systems capable of perceiving, learning, deciding, and acting independently.

Almost every corporation has AI ambitions, is actively employing it, or is rebranding its old rule-based engines as AI-enabled solutions. As more businesses integrate AI and advanced analytics into business processes and automate choices, there must be greater transparency into how these models make judgements. Explainable AI (XAI) is the solution-provider at this stage. XAI can help achieve transparency while still leveraging the benefits of AI.

We live in a time where machine learning models or weak AI drives most of the technology that encircles us. Adopting machine learning has been difficult because firms have needed help comprehending and explaining the outcomes to their customers. The most challenging component is explaining and convincing corporate stakeholders and customers about how the model works. Furthermore, with the explosion of data generated by Instagram, Facebook, tweets, Google searches, information in cookies, online payments, and other means, businesses must recognize and comprehend the influencing micro-factors and evaluate what works for a micro-segment of their consumer base.

Why Explainability Matters? 

Understanding how AI makes decisions is crucial, much like selecting decision-makers in business. AI can automate decision-making, and these decisions have both positive and negative commercial consequences. Many businesses want to deploy AI but are hesitant to allow the model or AI to make more critical decisions because they still need to trust the model. Explainability can contribute to this by providing insights into how models make decisions.


A continual problem for data scientists when developing models is determining if the features used are sufficient, or whether the model is reliable and unbiased from a commercial viewpoint. Furthermore, compliance, fair lending, ethical AI, and GDPR rules require model output explanations to ensure that no biases exist within the model structure. The goal is to use AI to supplement humans at scale, with model outputs being generalized to various commercial use cases.

Explainable AI (XAI) enables us to create a model with local or global ranking characteristics that explains a model’s outputs and transfers the less interpretable models to the more interpretable side, bridging the gap in the field of data science.

Micro-Segmentation: A New Perspective

In today’s business landscape, all companies have a customer segmentation structure that is supported by customer relationship management (CRM) systems or customer data platforms (CDPs). However, these solutions often fall short in terms of being able to effectively target narrow customer categories or produce customized communications beyond basic transactional qualities. This limitation can lead to a reduction in the effectiveness of marketing initiatives and customer engagement.


By enabling micro-segmentation techniques, companies can discover and target highly specific customer categories, resulting in more effective and efficient marketing strategies. Micro-segmentation allows firms to gain deeper insights into consumer behavior and discover patterns, enabling them to design strategies tailored to each specific customer group. This can help increase customer satisfaction and loyalty by providing more personalized customer experiences, ultimately leading to greater customer lifetime value and reducing churn.


From an implementation perspective, marketing strategies often focus on improving customer lifetime value, reducing churn, increasing membership enrollment, promoting active engagement, and optimizing marketing spend. However, traditional implementation methods often fail to capture features that are centered around business actions.

Consumer clusters are usually used in conjunction with a churn model, but this may only partially leverage and answer global explanations of consumer behavior, such as key-driver or feature importance analysis. When it comes to deciding on specific actions, global explanations may prove ineffective, and local (specific) explanations may be needed.

To address this issue, we can extend the use of churn models and enable model explanations. One widely used method for model explanations is SHAP (SHapley Additive exPlanations), which allows us to understand the impact of a variable compared to a baseline value. By using SHAP, we can identify the most important features and determine how they impact the model’s output. This information can be used to develop more effective and targeted marketing strategies that take into account specific customer behaviors and preferences. Ultimately, this can help businesses to improve customer satisfaction and retention, increase revenue, and optimize marketing investments.

In conclusion

Adopting Explainable AI is about recognizing model biases and discovering rogue models or loopholes that a model is trained over time, not merely comprehending and describing why a model provides the way it does. These static rules may be changed with varying consumer journeys by employing machine learning and XAI as technology, CRM systems, and bespoke apps develop. This would further discourage CRM design constraints by mapping a marketing campaign to the best-fit micro-segment and treating each segment individually.

H are other additional sectors where XAI might assist:

  • One may effectively rate client complaints based on severity by emphasizing statements in the complaints that indicate the pain locations.
  • Businesses can explicitly show each characteristic utilized in credit scoring models in a financial sector and confirm that no inherent model biases exist.
  • Insurance firms may guarantee that insurance premiums are not determined by a consumer’s ethnicity, gender, or area.
  • Businesses can detect machine breakdowns that aren’t usually caused by faulty core components.

XAI is becoming increasingly important in assisting organizations in understanding the rationale for an outcome and justifying the following line of action. As a result, it is critical to explain your AI choice to your business operations rather than focusing just on constructing models automatically.

The article has been written by Vikram Raju – AVP, Corporate Strategy, Genpact

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