What is Explainable AI (XAI)?
Explainable AI (XAI) is a set of techniques and approaches designed to enable human users to understand and have confidence in the outcomes and outputs generated by machine learning algorithms.
In 2022, the global market size for Explainable AI reached a significant valuation of USD 5.10 billion and is predicted to reach USD 24.58 billion by 2030, with an anticipated Compound Annual Growth Rate (CAGR) of 21.5%, according to Research and Markets.
While regular AI models often operate as ‘black boxes’, concealing the intricate internal mechanisms that drive their decisions, explainable AI offers a vital solution to this opacity issue, ensuring that AI decision-making becomes interpretable and accountable.
By doing so, explainable AI helps bridge the gap between advanced AI capabilities and the need for transparency and comprehensibility in various applications.
Regular AI Versus Explainable AI: How They Work
Regular AI, also known as traditional AI or “black box” AI, often employs machine learning algorithms to perform tasks and make decisions. While these AI systems can achieve remarkable results, they typically lack transparency and interpretability.
In contrast, XAI implements specific techniques and methods to ensure that each decision made by machine learning algorithms can be traced and explained.
The table below provides a clear comparison between regular AI and XAI:
|Aspect||Regular AI (Traditional AI)||Explainable AI (XAI)|
|Transparency and Interpretability||Often operates as a “black box”, making it challenging to understand the rationale behind decisions.||Prioritizes transparency and interpretability, allowing users to comprehend decision-making processes and factors.|
|Traceability||Lacks a clear path to trace decision-making, making it difficult to pinpoint factors influencing decisions.||Enables tracing of decision paths, showing data points, features, or rules that influenced the final output.|
|Control||Offers limited control, as decision-making is often opaque, and developers and users may not understand AI behavior.||Provides more control over AI systems. Users and developers can understand and influence AI behavior, allowing adjustments when issues arise.|
|Accountability||Accountability can be challenging to establish, especially in high-stakes domains, due to a lack of transparency.||Enhances accountability by making decision-making transparent and auditable, establishing clear responsibility.|
|Auditability||Auditing AI systems for fairness, bias, or compliance with regulations can be challenging due to a lack of transparency.||Facilitates auditing and monitoring of AI systems, ensuring compliance with ethical and legal standards.|
Why is Explainable AI so demanding?
Explainable AI (XAI) plays a pivotal role in providing data driven decision making for the Machine Learning Operations (MLOps), businesses and organizations across various sectors.
Explainable machine learning enhances transparency and trust. In an era where machine learning algorithms are increasingly involved in data driven decision making, understanding why and how an AI arrived at a particular decision is essential.
XAI provides comprehensible explanations, making it easier for data scientists and MLOps teams to comprehend model behavior, diagnose issues, and fine-tune models for optimal performance.
Explainable AI also fosters collaboration and learning. In the deployment phase of MLOps, XAI ensures that models are not just accurate but also interpretable, facilitating real-time applications where human intervention may be necessary. It enhances communication across multidisciplinary teams, fostering collaboration between data scientists, engineers, and business stakeholders.
In the operational aspects of MLOps, explainable AI aids in debugging and monitoring machine learning models. Continuous performance monitoring and issue identification are central to maintaining model accuracy. XAI’s insights into model behavior empower teams to pinpoint the root causes of problems and swiftly implement corrective actions.
Invest in the Right Tools, Unleash Explainable AI with ActivML
As AI and machine learning technologies continue to proliferate across diverse industries, the need for comprehending the ‘how’ and ‘why’ behind AI-related decision making becomes increasingly crucial, especially when it comes to fraud detection operations.
The emergence of explainable AI has effectively addressed this concern, offering the ability to provide rationale for its actions and facilitate human understanding. Leveraging the capabilities of XAI, Neural Technologies’ ActivML solution not only identifies anomalies but also offers in-depth comprehension of these occurrences.
ActivML solution is a cutting-edge solution designed to revolutionize the way businesses make decisions by providing profound data structural insights and accurate predictions. At its core, ActivML is a dynamic platform that seamlessly integrates machine learning into your business operations, ensuring you have the tools you need to stay ahead in today’s data-driven world.
Important features of ActivML solution:
- Self Learning Structured Analytical Profiling: Autonomously recognize changing trends and anomalies within the data without the need for constant manual adjustments
- Unconstrained Anomaly Detection: Identifies unusual or irregular patterns within data without predefining specific rules or thresholds
- Structured Classification: Categorize and classify data, enabling organizations to make informed decisions based on the structured information derived from the data
- In-depth Explainable Analytics: Deep into the underlying reasons and causes for the observed patterns, providing a comprehensive and interpretable explanation for the AI’s decisions and actions
Backed by more than 25 years of machine learning expertise, our ActivML solution offers near real-time risk detection accuracy over 98%, end-to-end MLOps automation, and a 50% faster time-to-market for business solutions.