Trust and security are at the center of most discussions when organizations modernize with new technologies like AI, IoT and ML. Leaders know that in order to optimize return on their technology investments, they must obtain stakeholder trust. Chatbots are a great way to gain operational efficiency, boost customer experience and modernize your workplace but before you deploy, consider these guidelines to ensure you design a bot that your users can trust.
In 2016 Gartner had predicted that “Conversational AI-first” will supersede “cloud-first, mobile first” would become the most important, high-level imperative for enterprise architecture and tech innovation leaders in the next decade and that prediction is being realized. Conversational agents are well past the hype cycle and have proliferated in the last few years owing, amongst several other factors, to the maturity of chatbot platforms like the Microsoft Bot Framework. And so have customer expectations rocketed. The rapid advances in NLP (Natural Language Processing) like Language Understanding service allows applications to understand what a person wants even when expressed in their own words, thereby allowing for more natural chatbot conversations. But whatever the type of chatbot – social engagement bots, workflow automation bot, information discovery bot, productivity bot or decision support systems- customers expect bots to engage in light-hearted conversations and failing to do so could come across as monotonous and boring. In an era where content is king, context is queen. A bot that has a personality, is contextually and socially aware, and also serves its primary use case, has a greater trust level with the users. Thankfully, innovation leaders can fulfill this modern need for chatbots that respond to common small talk in a consistent tone, thanks to tools like Project Personality Chat.
But with great power comes great responsibility. The design and implementation of conversational agents must be evaluated for risks and potential harm. The risks involved can range from misunderstanding a user’s intent to engaging in contentious topics. From our experience in this space, we can vouch that the successful adoption of such conversational systems depends not only on the technology used, the data source powering the bot and the conversational experience. How much the user “trusts” the bot is a major driver. And this trust is built on a couple of factors as covered by Microsoft’s AI Principles such as transparency, reliability, safety, fairness, diversity and privacy. This understanding of what the modern chatbot user wants combined with the tools now available to account for those parameters when building conversational agents, responsible AI chatbots are far from a dream now.
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Liji Thomas 
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