Does Explainable AI Uncomplicate Artificial Intelligence? – Analytics Insight

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  • June 9, 2020
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Modern Artificial Intelligence systems are finding their way across enterprises and business domains with applications ranging from conversational AI, predictive analytics, intelligent RPA to facial recognition algorithms. Courtesy to machine learning, AI from its mystery black box gives outcomes without explaining the reasons behind it, often leaving questions unanswered.
The predictions made by AI data models find their calling in healthcare, banking, telecommunications manufacturing industry to name a few. The next course of action indicated by them is often critical in some cases especially when the applications are into healthcare, war drones or driverless cars.
C-Suite executives across geographies agree that AI-based decisions can be trusted provided they can be explained. The recent reports about the alleged bias in AI models for credit and loan decisions, recruitment, and healthcare applications highlight the lack of transparency and prejudiced decision making by AI models.

Can AI explain itself?
Augmented Intelligence and Machine Learning are already parsing huge amounts of data into intelligent insights, helping the workforce to be more productive, smarter and quicker at decision making.  To what extent the help is effective is under doubt, if we don’t have any idea how these decisions are made.
Explainable AI (XAI) attempts to answer this question. An emerging field of machine learning, Explainable AI demystifies how decisions are made, trying to understand the steps involved in the process. XAI unlocks the black box to ensure the decisions made are accountable and transparent.
The explainability behind AI solutions can be ascertained when data science experts use inherently explainable machine learning algorithms like the simpler Bayesian classifiers and decision trees. They have a certain degree of traceability in decision making and explain the approach without compromising too much on the model accuracy.

The 3 C’s of Explainable AI
In a bid to understand XAI, many users mistake correlation for causation, the two indispensable C’s of explainable AI. Here are the 3 C’s of statistical concepts which contribute to uncomplicate Artificial Intelligence-

Co-occurrence- This aspect of Machine Learning indicates how events occurring together affect an outcome. E.g., an allergy symptom may lead to an asthmatic attack at the same time.
Correlation– Correlation determines a relation between two events that may or may not be related. A negative correlation implies the events are not influencing each other like colder weather onset and a decrease in air conditioning costs. Positive correlation examples include time spent on a treadmill and calories burnt.
Causation– A critical factor to Explainable AI, causation explains why an event has occurred, helping enterprises to predict the likelihood of “Y” if they know “X”. The classic example is that of an e-commerce app, if there are too many steps leading to purchase, it is highly likable that the user may abandon shopping or uninstall the app altogether.

Causation is the basis behind understanding the predictions of ML-powered AI models. For instance, intelligent graphs come with visualizations enabling enterprises to go back and forth determining which events are causative to others, this capability is vital to solving the explainability behind AI models.

Use Cases of Explainable AI
Explainable AI finds its use case in domains where technology impacts people’s lives fundamentally requiring trust and audibility. These include-
Explainable AI provides a traceable explanation allowing doctors and medical care professionals to trust the outcome predicted by the AI model.  Explainable AI acts as a virtual assistant to doctors helping them detect diseases more accurately, for instance, cancer detection through an MRI image identifies suspicious areas as probable for cancer.
AI-powered NLP algorithms analyse unstructured data like manuals, handouts and structured data like historical inventory records, IoT sensor reading to predict preventive equipment failures helping manufacturing professionals with prescriptive guidance to equipment servicing.
Banking and Insurance are industries with far-reaching impacts, characterised by auditability and transparency. AI models deployed into BFSI help in customer acquisition, KYC checks, customer services, cross-selling and upselling by answering questions about how AI got a prediction, and what data models form the basis of prediction.
Autonomous Vehicles
The importance of Explainable AI into Autonomous Vehicles is paramount. Technology can explain if an accident is inevitable, what measures can be taken to assure the safety of the passenger, and the pedestrians.
In a nutshell, Explainable AI is all about improvement and scenario optimisation, adding the building blocks which strengthen human trust behind technology making the correct decisions for its stakeholders without any bias.

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Kamalika Some

Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

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