XAI and its impact on creating a data-driven culture
Over the years, the field of AI and ML has evolved by leaps and bounds. Despite the progress, AI/ML models suffer from a few challenges, including:
- Lack of explainability and trust.
- Rules of security, confidentiality and ethics.
- Bias in AI systems.
These challenges can make or break AI systems.
With the rapid evolution of ML, various accuracy-related metrics have gained prominence, calling for AI Explainability (XAI). As the scatter plot below shows, the accuracy and explainability of machine learning models is suspect. For example, deep learning techniques are more accurate, while decision trees are poor in performance and good in explainability.
Enter Explainable AI (XAI)
As the model becomes complex, developers often fail to understand why the system arrived at a specific decision. This is where Explainable AI comes in.
Explainable AI (XAI) aims to explain how black box decisions of AI systems are made. According to ResearchandMarkets, the global XAI market size is estimated to reach USD 21.03 billion by 2030 growing at a CAGR of 19% (2021-2030).
XAI is a catch-all term for movements, initiatives, and efforts in response to issues of transparency and trust in AI.
According to the Defense Advanced Research Projects Agency (DARPA), XAI aims to produce more explainable ML models with a high level of prediction accuracy.
Explainable AI (XAI) is a hot topic across all industries today, including retail, healthcare, media and entertainment, aerospace and defense. For example, in retail, XAI helps predict upcoming trends, with logical reasoning to boot, allowing the retailer to better manage inventory. In e-commerce, AI’s explainable help makes sense of recommender system suggestions based on customers’ search history and consumption habits.
In general, the need to explain the AI system arises from four reasons:
Explain to justify: XAI guarantees a verifiable and demonstrable way to defend fair and ethical algorithmic decisions, which leads to building trust.
Explain to control: Understanding system behavior provides greater visibility into unknown vulnerabilities and flaws. This allows errors to be identified and corrected, allowing for quick checking.
Explain to improve: As users know why the system produced a specific output, they will also know how to make it smarter. Thus, XAI could serve as a basis for further iterations and improvements.
Explain to discover: Asking for explanations can help users learn new facts and gain actionable insights from the data.
Interpretable ML is a core concept of XAI and helps ingrain trust in AI systems, and brings fairness (making predictions without discernible bias), accountability (tracing predictions reliably back to some something or someone) and transparency (explaining how and why predictions are made).
More importantly, once you understand how decisions are made by the AI system, it leads to better AI governance within the organization and improves model performance.
Additionally, knowing why and how the model works and why it fails allows ML engineers and data scientists to optimize the model and helps create a data-driven culture. For example, understanding model behavior for various distributions of input data helps explain biases in input data that ML engineers can use to make adjustments and generate a more accurate and robust model.
This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives from the data science and analytics industry. To check if you are eligible for membership, please complete the form here.