In today’s world, we’re often interacting with virtual assistants, either by speaking to them or by typing. Think about all the people who have Amazon’s Alexa-enabled devices in their homes and are asking these devices to play music and tell jokes. Amazon sold over 100 million Alexa devices in 2018 alone and that year Alexa told over 100 million jokes.


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Alexa is a chatbot, a form of AI that interacts with customers via conversation. That’s because NLP enables it to understand humans’ messages and often respond appropriately. In the case of a consumer seeking an answer to a product question, being able to type the question and have a bot immediately return an answer saves time that may have been spent on the phone, waiting for a human representative to respond. In turn, the product’s manufacturer doesn’t have to employ an extra human to respond to routine questions, which saves the company money.

The potential for such virtual assistants is enormous and many benefits are already being realized. However, all the kinks have not yet been ironed out. While building a chatbot is relatively easy, the conversation piece is often harder to get right.

Chatbots: The good

As for applications for chatbots using NLP, the sky is the limit in industries as diverse as healthcare, education, retail, tourism and others. With many people trying to educate their children via Zoom, chatbots can deliver AI enabled education across the world. Some hair salons have been employing chatbots to schedule appointments and they are also being used for scheduling things like airport shuttles and rental cars.

Healthcare presents perhaps one of the biggest opportunities for virtual assistants. Automated text reminders of appointments have resulted in reduced no show rates in the U.S. And in rural parts of the world, chatbots are helping to connect patients to clinicians via digital consultations.

For example, in Rwanda where there are only one doctor and six healthcare workers per 10,000 people, healthbots are helping reduce the heavy demand on health center staff. Instead of standing in line to see an in person provider, patients can access consultations with doctors or nurses over the phone from anywhere in the country. They can receive a text message code for a prescription or a lab test.

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In the future, these healthbots will become triage tools that will use AI localized for Rwandan language and epidemiology to allow even more patients to be served. For patients that require a physical consultation, the triage tool will prioritize those needing the most urgent care. The triage tool can also share patient information with clinicians. This makes it easier for them to quickly access what they need to treat the patient.

Through such virtual assistants, more effective use of scarce health resources will be possible. In return, the quality of care will be improved and healthcare workers will be kept in the loop.

Chatbots: The bad

AI is continuously learning but its algorithms are designed by humans who have biases. One of the pitfalls in using AI powered chatbots is the lack of diversity among creators that can lead to biased responses. Often heavily accented users are misunderstood by bots and that can have implications for patients as well as anyone seeking correct information. Poor guidance, incorrect diagnoses and failure to access timely interventions can result in serious consequences. The challenge is to attract more diverse programmers and recognize specific instances of inequity in communications.

Information privacy is also a serious consideration, as is the ability of users to distinguish whether they’re speaking to a bot or to a human. NLP powered virtual assistants are becoming increasingly sophisticated and sound more “natural” all the time. It’s understandable that patients or other users should wonder if they are talking to a human or a bot – or getting medical advice from a physician or a bot.

To address this issue, Stanford University proposes that artificial agents should be required to produce, on-demand, unambiguous identification that they are bots. In addition, the proposal calls for including information about the virtual assistant’s history of ownership and usage. This information could potentially address tracking concerns and the question of who’s responsible for outcomes. […]

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