Why are you so quiet?
By: Raajat Gupta
Tags: AI, Vulnerability, Voice Assistant, Trust

Trust matters — but trust is about more than competence

I’m not alone in this sentiment. Human-Robot Interaction literature has emphasized how important trust is to fruitful human-robot relationships. In the context of healthcare, the level of agent trust directly impacts the perceived credibility of agents’ suggestions and how often users take actions based off these suggestions [1]. More broadly, trust influences how effectively human-robot teams can accomplish their goals and enables more effective relationships [2]. Trust is core to improved human-robot relationships, and as I’ve dived into the area of building trust in these relationships, I’ve become more and more convinced that the secret to building increased trust is to focus on the elements of human-human relationships that lead to trusting, strong relationships. To thrive, voice assistants will need to move away from being purely reactive, transactional bots to becoming human-like, proactive, attentive and vulnerable companions. In doing so, these agents will be able to vastly improve user trust, increase satisfaction, recommendation accuracy and enjoyability, and enhance human-robot relationships.

Agent trust directly impacts the perceived credibility of agents’ suggestions and how often users take actions based off these suggestions. More broadly, trust influences how human-robot teams can accomplish their goals and enables more effective relationships.

In making this argument, I’m making an active departure from what is considered prevailing wisdom in the field of Human-Robot Interaction. For many in the field studying how to improve relationships with voice assistants, trust has been synonymized with ability. This perception has been grounded in several studies consistently showing that trust is mediated by perceived robot competence, whether real (e.g., rating a household assistant robot as significantly less trustworthy in the wake of cognitive / physical errors) [3] or illusory (e.g., using expert language to create an appearance of intelligence and ability) [4]. Researchers have argued that trust cannot exist without performance, and a robot’s ability to constantly live up to user expectations.

However, saying that I should trust a voice assistant to help me in all my endeavors just because it has shown high degrees of competence within narrow use cases is a lot like saying I should trust my math teacher to give me great restaurant recommendations because she does a great job of teaching differential calculus. Unfortunately, we’ve seen this exact trend unveil itself from voice assistant users, who have incredibly narrow uses for voice assistants that focus on the key areas that they think these assistants are competent in. By the third week someone gets a voice assistant, they have already established how they’ll be using that assistant for the rest of its lifespan [5], and use cases rarely extend beyond three distinct domains (music, information, and automation were the three most common), while 77% of all sessions involved only one or two domains [5]. In other words, the issue doesn’t lie in optimizing assistant ability, it lies in having users trust assistants enough to try new domains that may lie outside their comfort zone.

By the third week someone gets a voice assistant, they have already established how they’ll be using that assistant for the rest of its lifespan, and use cases rarely extend beyond three distinct domains.

These issues with narrow domain usage are exacerbated by just how much of an onus there is on users to learn about the features that meet their needs. Voice assistants have no visible affordances, and so users’ mental models of what they can do is relatively bare [5], especially since the only aid they get from many large voice assistant manufacturers are periodic emails about new capabilities and commands. Google is well aware of these issues — Lilian Rincon, senior director of product for Google Assistant, noted that “one of the biggest problems we continue to have is the problem of discovery, especially for speakers — letting you know what it is you can do” [6]. These issues with discoverability create a vicious cycle of sorts — users never learn about the new or unorthodox features that their voice assistants may be able to handle well, and so they don’t trust the assistants to handle their unorthodox problems. In turn, voice assistants are unable to make continuous improvements in their capabilities to handle unorthodox problems because of limited user datasets creating users like one woman interviewed by PwC in their report on voice assistants, who noted, “I would shop for simple things like dog food, toilet paper, pizza… but ‘can you order me a sweater?’ That’s too risky.” Enhancing trust through increased assistant ability has diminishing returns when these enhanced abilities are rarely discovered, and voice assistants are unable to convince users to try new features to further improve abilities.