What makes a chatbot work for humans?

As chatbots improve in how they communicate, will interaction with them increase?

Kaveer Beharee, CEO at Ubiquity AI looks at how to evaluate chatbots and intelligent conversational agents for commercial use and why communication as much as technical expertise will be key to wider adoption.

In the previous discussion on What makes for a good chatbot, I outlined the PARADISE framework and its contribution to the evaluation of spoken dialogue agents. The second part of the PARADISE framework centres around usability. For any chatbot designed to replace or augment human roles, retention on the platform should be a company’s top priority.

In my experience as a chatbot developer, the first objective, task-oriented evaluation, is relatively straightforward. Designing a chatbot for a task, whether the task is transactional, informational, and so on, develops that capability until the bot is proficient at executing those tasks.

The subjective measures are markedly more complex to bed down. The first significant hurdle in maximising usability is ease of communications. The main consideration here is: is your chatbot designed to adapt to your users or are your users required to adapt to the chatbot?

If you observe chatbot behaviour, you’ll notice that chatbots are built around the assumption that chatbots assume that users need to do the adapting. This is problematic for the future of chatbots. If chatbots cannot engage effectively and pleasantly, a chatbot will simply not be appealing as a channel for problem resolution.

This underscores the need for digital communication professionals who understand how to design chatbot conversations and communications that work for the human at the other end of the line. With the rise of AI technology, I predict there will be a marked increase in demand for human-machine conversation design skills, especially once companies and developers realise that the future of chatbots does not hinge on technical expertise but rather communication science and those proficient in natural language technologies.

Professional communicators – conversation designers, technical communicators, and some content designers – are well suited to fulfil the following critical chatbot development tasks, which include: •Chatbot training – although more sophisticated developers use highly automated deep-learning to train their chatbots on language, most chatbots are trained using supervised machine learning or intent tagging (matching statements to an AI model’s intent files). •Conversation architecture – while this is a reasonably new concept, communicators will play a critical role in developing a chatbot’s conversational architecture. While this is a technical consideration, it is also a usability consideration driven by conversational bots, which also seeks to maximise efficacy at the least possible cost. •Anticipate and enhance information retrieval from natural language user inputs. •Evaluate the chatbot’s efficacy.

Business seeking to adopt cost-cutting AI technologies, such as chatbots and intelligent conversational agents, should carry out a full evaluation before implementation. In my experience the most successful chatbots are the ones that bring the often-missing skills of natural language processing and linguistics skills to the table – and don’t stop at machine learning and technical development.

Adapted from an article on Firehead, a European firm specialising in recruitment, consulting and training in AI and digital communications.