Source: analyticsinsight.net
Artificial Intelligence (AI) made leapfrogs of development and saw broader adoption across industry verticals when it introduced machine learning (ML). ML helps in learning the behavior of an entity using patterns detection and interpretation methods. However, despite its unlimited potential, the conundrum lies in how machine learning algorithms arrive at a decision in the first place. Questions like, “What are the processes they adopted, and at what speed? How did they make such autonomous decision?” often raises concern about reliability on ML models. Though it helps in parsing huge amounts of data into intelligent insights for applications ranging from fraud detection to weather forecasting, the human mind is constantly baffled how it achieves conclusions. Moreover, the recurrent need to comprehend the procedures behind the decisions becomes more crucial when there is a possibility that the ML model makes decisions based on incomplete, error-prone, or one-sided (biased) information that can put few gatherings inside the network at a disadvantage. Enter Explainable AI (XAI).
This discipline holds the key to unlocking the AI and ML black box. XAI is an AI model that is programmed to explain its goals, logic, and decision making so that the average human user can understand it. This user can be either a programmer, end-user, or person impacted by an AI model’s decisions. According to a research report in Science Direct, the earlier AI model systems were easily interpretable. For instance, decision trees, Bayesian classifiers, and other algorithms which possess certain amounts of traceability, visibility, and transparency in their decision making process. But since of late, AI saw the emergence of complex and opaque decision systems such as Deep Neural Networks (DNNs).
The empirical success of Deep Learning (DL) models such as DNNs stems from a combination of efficient ML algorithms and their huge parametric space. The latter space comprises hundreds of layers and millions of parameters, which makes DNNs be considered as complex black-box models. The opposite of black-box-ness is transparency, i.e., the search for a direct understanding of the mechanism by which a model works. And recently, the demand for transparency has gained more traction. As mentioned earlier, this demand rose due to ethical concerns like the data set used to train ML systems may not be justifiable, legitimate, or that do not allow obtaining detailed explanations of their behavior. Besides, opaque black box AI (and ML) decision making, XAI, also addresses bias inherent to AI systems. Bias in AI can prove detrimental, especially in recruitment, healthcare, and law enforcement sector.
According to the US Defense Advanced Research Project Agency (DARPA), XAI constitutes on three basic concepts: accurate predictions, inspection, and traceability. Here accuracy in prediction refers to how models will explain conclusions are reached to improve future decision making, decision understanding, and trust from human users and operators. And, traceability empowers humans to get into AI decision loops and have the ability to stop or control their tasks whenever the need arises. This is why XAI is gaining more importance in the past couple of years. In a recent forecast, Forrester predicts demand surge for transparent and explainable AI models, citing that 45% of AI decision-makers say trusting the AI system is either challenging or very challenging.
Last year, IBM researchers open-sourced AI Explainability 360 to help developers to gain more explainable insights on ML models and their predictions. Even Google, too, has announced its new set of XAI tools for developers. And with public interest growing in AI and ML that is explainable and adheres to regulations like GDPR, enterprises will have no choice but adopt XAI tools that will remove the black box in AI algorithms, focusing on enhancing explainability, mitigating bias and creating better outcomes for all.