If 2021 has taught us anything, it is that what sounds like a consensus online is often not. Our world is dominated by algorithms whose output has shown the ability to skew our realities. Bad actors have discovered they can influence algorithms and they do so for financial gain or just a laugh. Artificial Intelligence (AI) can provide great value, but AI with bias and/or inaccuracy is something we must actively guard against. This post is going to explore the traps related to user feedback and how over reliance on that dataset can result in poor outcomes for any AI, but especially for chatbots and digital assistants which are your first line of support for your users. For the purposes of this post, we will focus our examples on use cases we typically see our customers facing. Users, in this context, are the ones chatting with the bot and looking for support.

What is User Feedback?

User Feedback is a broad term meant to cover both direct and indirect feedback. Direct feedback is when the user is asked for their opinion directly and they reply. You will see this in various forms. For example, the thumbs up and down icons are meant to collect user feedback. You may be asked, “Did this solve your issue?” or “How would you rate this experience?”. Have you seen those buttons at a store’s exit where there is a smiley face, a sad face and something in between? That is a form of direct user feedback.

The other type of feedback is far more subtle and indirect. We can look at a user’s actions and from those infer some level of feedback. These patterns can also be called user cues. An example of such a cue is when the user gets an answer and they respond, “you stink!”. The implication is that the user is unhappy about the previous answer. Another cue can be the circumstances under which a user clicks a help button or even asks to speak to a live agent. All of these indicate something may have gone wrong.

The Feedback Challenge

There is no problem with asking for feedback. In general, it is a good practice. There are some challenges, however, so let’s explore those.

Interpreting User Intent

Interpreting the user’s intended meaning is no easy task. Let’s focus in on a simple interaction to illustrate this point. With many help desk systems, upon completion of the experience, the user will be asked: Did this solve your issue? Read the complete article here.

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