In recent years, artificial intelligence (AI) has shown great promise, with machine learning (ML) standing at the forefront of its most common applications. Simply think of Siri and Alexa, navigation systems, or product recommendations. Not to mention the omnipresent Google search algorithm and the automated translation tools that we so often use.
However, despite all their obvious benefits, there’s a general problem with machine learning model adoption:
Their lack of transparency.
Machine learning models are usually regarded as black box algorithms that are impossible to interpret. We get to see the prediction, but not what’s going on behind the scenes. So, if the decision-making process is not something we can understand, then how could we fully trust it?
That’s where Explainable AI (XAI) comes in.
Explainable AI: Knowledge is power
Human trust in technology is born from our knowledge of how it works. We either understand it, trust it, or highly doubt it. Take this real-world case study published in McKinsey’s The State of AI in 2020, for example:
A global materials manufacturer worked towards increasing workplace safety for frontline workers by implementing an AI tool. The AI would help by recommending certain ways dangerous equipment should be handled. However, although it was meant to be helpful, the machine learning model wasn’t met with wholehearted enthusiasm, but mistrust. In the end, adopting it was only possible once the workers gained the necessary insights that allowed them to understand how it works.
Once they could understand the reasoning behind the black box algorithm, the workers started trusting the AI.
The takeaway? Explainable AI promotes adoption. Why? Because human-interpretable explanations of machine learning models help the end-users trust that it’s making good decisions on their behalf.
In short, we trust what we can understand.
Understanding explainable AI
By definition, explainable AI (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning models.
Explainable AI techniques are used to produce in-depth analyses of a model’s performance, fairness, transparency, reliability, resilience and, thus, help to promote user trust, model accountability, and the productive use of AI. Moreover, it mitigates compliance, legal, security, and reputational risks of using AI models in production.
Explainable AI is all about making the decisions of ML models easier to understand, especially those of complex ones such as deep neural networks. It’s about escaping the algorithmic black box and getting to know the ‘how’ and ‘why’ of their decision-making process.
The benefits of explainable AI
Explainable AI allows companies and organizations across all industries to:
- Build end-user trust in the AI decision-making process by opening up the algorithmic black box
- Answer stakeholder questions about the use of AI systems
- Address rising ethical and legal concerns regarding the use of machine learning models
- Support system monitoring and auditability
- Implement a responsible AI methodology focused on fairness and debiasing
- Mitigate model drift
Explainable AI bridges the gap between data and business, between data scientists, engineers, and non-technical executives.
Types of explainable AI techniques
Various types of explainable AI techniques have been developed to be used across all steps of a machine learning model’s lifecycle. They can either be applied to a system or baked into it, and can generally fit into one of the stages listed below.
Pre-modeling
Employs techniques to understand the data before using it to train models. Uses:
- Detecting biases in the data and making sure they are not influencing subsequently trained models.
- Engineering explainable features.
During modeling
This stage involves creating models which are inherently interpretable. Uses:
- Easy for simple models, like Decision Trees, k-Nearest Neighbors, and linear models.
Post-modeling
Focuses on developing tools that can explain decisions of already existing black box algorithms. Uses:
- Explaining already existing models. As such, they are the most useful explainability techniques.
While the classic explainable AI techniques usually involve methods such as:
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-Agnostic Explanations)
- PDP and ALE (Partial Dependence Plots and Accumulate Local Effect)
- Permutation Feature Importance
AI explainability is a territory of ongoing research and continuous development.
Key takeaways
AI has become part of our lives in many ways and its presence keeps growing as we advance toward the future. But, as machine learning models evolve and automated systems become more complex, the element of trust is still, in many ways, fraught, due to the black box nature of algorithms.
Often enough, AI explainability is sacrificed on behalf of accuracy, but organizations can no longer afford to leave transparency out of the equation, both from a human-adoption perspective and also from a legal compliance standpoint.
Lumenova AI can help your company open up the algorithmic black box and make Explainable AI a key pillar of your machine learning strategy. Feel free to get in touch with our team of experts, if you wish to schedule a demo or have any questions.