Artificial Intelligence or AI has already forayed into our lives in more ways that we recognize. We are not far from using AI as a verb soon, exactly the way we use ‘Google’ as one for most of our search practices. However, AI models do not always exactly follow an explainable pathway, or algorithm, known as the ‘black box’ which even the engineers and data scientists fail to comprehend or explain–the pathway, then, becomes an arbitrary model where one is not sure of the reasoning or rational footprint of the result that the AI model churns out. This often deters the larger organizations, dealing massive data sets implement and follow an AI-model which would do justice to the sheer scale of the organizational data.
To combat the inaccuracy and mitigate the unknown factor in the predictability of the results, the Explainable AI or XAI provides a comprehensible decision-making algorithm to the developers, to understand exactly how a result is arrived-at, while also strengthening the accuracy and predictability factors of the results, created by the machine-learning algorithms.
The extremely complex neural decisions, often termed as the ‘black box,’ makes the decision-making extraordinarily indecipherable for human brains, which also fails to detect any innate bias that the machine might have learnt in the process of collecting humongous amount of data fed everyday.
It then becomes crucial for every organization to ensure the learnt data and the output are in alignment and auditable, making the data output measurable, compliant and business goal-oriented. Explainable AI or XAI, therefore is not an upgradation but a requirement for businesses to remain responsible towards the data it manages and produces for consumption at various levels of managerial decision-making. And to help organizations resort to ethical AI practices, it is the need of the hour to implement responsible AI-models and embed XAI in their business processes to ensure that the AI-systems are based on transparency and trust.
While the relentless efforts are on amongst the teams of data scientists across businesses globally, the XAI is being based on the idea of future predictability, traceability and accuracy of data results. One might want to know how the XAI is the emerging generation of artificially intelligent machine partners and to justify the same, let’s look at some of the highlights of XAI models:
1. Prediction accuracy: Accuracy is essential for the successful use of AI in daily operations. To determine prediction accuracy, simulations are run, and the output of explainable AI (XAI) is compared to the results in the training dataset. One of the most widely used techniques for this purpose is Local Interpretable Model-Agnostic Explanations (LIME), which provides insights into the predictions made by machine learning classifiers.
2. Traceability: It is another crucial technique for achieving explainable AI (XAI). This can be accomplished by restricting how decisions are made and defining a narrower scope for machine learning rules and features. DeepLIFT (Deep Learning Important FeaTures) is an example of a traceability XAI technique. It compares the activation of each neuron to a reference neuron, establishing a traceable link between each activated neuron and revealing dependencies among them.
3. Decision understanding: This focuses on the human aspect. Many people are wary of AI, but to use it effectively, they need to develop trust in it. This can be achieved by educating the team working with AI, enabling them to understand how and why the AI makes its decisions.
Therefore, we recognize the reason why XAI is the next in order of implementation that large organizations are willing to adopt in order to fortify their prediction capabilities in the market, adapt preemptive measures, and also forecast any crime, especially in the domain of prison and criminology data sets to mitigate future fiascos.
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