Definition
Model Interpretability refers to how well a machine learning model’s prediction can be understood and explained. It's an important aspect of data science that enables professionals to understand the relationships between inputs and outputs. This insight is key to establishing trust and confidence in machine learning models, enabling stakeholders make informed decisions [1].
For example, lets imagine a scenario where a recruitment platform like jobberman is using screening algorithms to evaluate job applications for employers. When a qualified candidate gets rejected by the automated system, recruiters need to be able to explain why the model, and hence themselves, made that decision. A job seeker rejected for a marketing position needs to know the reasons such as missing required certifications, insufficient relevant work experience, geographical location constraints, or salary expectation misalignment, preventing qualified candidates from being unfairly excluded from opportunities.
The concept of model interpretability in AI originated from the need to understand complex computational processes. The development of interpretability has been particularly influenced by the growing concerns related to ethical use-cases of AI and the need for transparency in AI models.
Context and Usage
In today’s
AI-driven world, model interpretability is important for building trust,
ensuring compliance, and fostering effective decision-making in several
domains:
- Autonomous Vehicles: Due to safety and regulatory approval reasons, it is crucial to understand the decisions made by AI systems in self-driving cars.
- Legal Systems: In legal systems, AI is used to recommend sentences or assess risk. Interpretability guarantees that these decisions can be explained and looked at, avoiding possible biases from going unchecked.
- Finance: Financial institutions go through regulations that demand explanations for automated decisions, such as loan approvals. Interpretability helps in ensuring compliance and building trust with customers.
- Healthcare: Here, interpretability provides explanations to doctors and healthcare professionals for why a model predicts a particular condition. This helps them trust AI-driven diagnoses [3].
AI interpretability is important as the use of AI is expanding. Systems that use large language models (LLMs) are becoming parts of our everyday life. This can be seen in smart home devices, credit card fraud detection, use of ChatGPT and other generative AI tools, healthcare, finance and other industries that involve critical or life-altering decisions. With such high stakes, the public needs to be able to trust that the outcomes are fair and reliable. That trust depends on understanding how AI systems arrive at their predictions and make their decisions [4].
In Practice
Example of model interpretability in practice is Gemini AI’s Explainable AI (XAI) tools, which provide advanced features to make machine learning (ML) models more transparent and understandable, empowering data scientists, business leaders, and stakeholders alike [5].
See Also
- Model Monitoring: Ongoing tracking of model performance and behavior in production environments
- Model Training: Process of teaching an AI model to make predictions by learning from data
- Model Versioning: Practice of tracking and managing different iterations of AI models over time
- Prompt: Input text or instruction given to an AI model to generate a response
- Prompt Engineering: Craft of designing effective prompts to get desired AI responses
References
- Dremio. (2025). Model Interpretability
- Huang, A., Li, J., and Shankar, N. (2020). Interpretability.
- Geeksforgeeks. (2025). Model Interpretability in Deep Learning: A Comprehensive Overview
- Jonker, A., McGrath, A. (2025). What is AI interpretability?
- Malaviarachchi, U., T. (2024). Enhancing Model Interpretability with Gemini AI’s Explainable AI (XAI) Tools.