ML

Explain how AI recommendations are being made to end users.

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Explaining “how and why” behavior of any product & activity has always been very crucial. In the digital era, it has just become more prominence.
An example such as facebook inability to explain “how and why” of data sharing led to a big trust deficit with its users. “Explainability ” Revolution has started a while ago as evidence from the huge popularity of Jupyter/Zeppelin notebooks, data lineage in reporting, data governance project in enterprise & roles such as chief data officer.
The revolution is now pacing up as the adoption of Machine Learning and AI goes mainstream. With open source ML library and tons of code available online, an immature and a professional both can create a model that can be as critical as predicting your illness. How do we differentiate and trust this model and results?
Consider for example Healthcare recommendation engine on https://www.healthcare.com/.

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By providing some basic inputs such as age, location it recommends healthcare plan personalized for you. As there is no explanation, top three recommendation coming for the same provider raises doubt and questions. Is the recommendation engine or company bias toward a specific provider? What were the criteria to recommend?

A black-box approach toward AI would be insensitive to the consumer and create lack of trust and will defeat the very purpose of leveraging AI to accelerate and improve customer experience.

“Explainability ” is the next big thing.
Visit https://www.ibm.com/cloud/ai-openscale to experience what it takes to provide explainability to your recommendation.

Say hello to “Lisa” the most impressive customer care officer. None of AI can compete.

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I recently called my bank and went through ten minutes of waiting on the phone, punching multiple keys, iterative menu and dialing again. I was stupid enough to identify in which category my request should be placed, and customer care AI system was efficient enough to disconnect a client not fully oriented with the bank voice menu. After few, attempt the AI-powered system gave on my intelligence and connected to Lisa.

What a relief, a truly advanced system that can understand emotions, answer my open-ended questions, have no issue with my lingo and finished my request faster and recommended a new product which I gladly accepted as it was fun to interact. Truly impressive customer care. Lisa is not the next generation of humanoid but human itself. Say hello to Lisa the most exceptional customer care officer.

In a genuinely democratic world where few vocals are re-writing the history and being neutral is a Sin as evident from recent US election ( http://brilliantmaps.com/did-not-vote/  ).  I want to ensure I am doing my job to set the right priority for myself and the fellow professionals, being myself an ML, AI, and big data evangelist. I see a lot of conferences where professionals and startup pride in replacing normal human interaction with a robot or AI-powered system. While this may sound cool, it certainly doesn’t make a business sense. Consider a BankA which replaces human interaction with the NLP-based engine to respond to customers may be saving millions of dollars.  This saving is diverted toward improving brand recognition, loyalty and reach out to potential buyers.  Now imagine another BankB which employs a mass, each employee brings new customers consistently & effortlessly due to their network and relationship. The enhanced customer experience becomes a “Brand” for itself, and the customer remains loyal irrespective of promotional offering from XYZ banks.  A Happy employee, happy clients, makes the world a better place to live.

There are problems that human race has been struggling since generations such as poverty, food crisis, natural disaster, drinking water availability, healthcare, education.  And we have new ones such as cyber security, abuse of social media, fraud and terrorism, efficient transportation in rural areas and a lot of “big Questions” that can be answered by Big data.  As a professional I prioritize and support projects such as

As IBM Watson Machine Learning, Microsoft Azure ML and Amazon ML aim to simplify ML and empower more professional it’s time to emphasize on the first and the most important phase, and that’s the phase where you ask the question and you specify what is it that you’re interested in learning from data. “what question we are asking “.