A working prototype of a Facebook messenger bot to understand how agent characteristics such as personality, etiquette, and multimedia use affect users' trust in data privacy.
A mixed reality system that supports conversation and memory recall, to help aging adults connect with others and preserve memories in the moment.
Part of my thesis research: Designing for Trust
Thesis Advisors: Dan Lockton, Daragh Byrne
Duration: 4 weeks
While researching trust on conversational interfaces, one of the personal goals that I also want to learn how to develop working digital prototypes to test with users along with human-centered design research. To do this, I prototyped zero UIs and web services with Ruby, Sinatra, Node.js, and Dialogflow.
Botae is a Messenger bot informs its users' on their trust level with other bots by walking them through a food recommendation scenario. It finds the best places for food/coffee through using users' location and promises user to find the most popular places among their Facebook friends by accessing their Facebook data.
Botae works similar to Surebot.io. As users go through the flow of getting recommendations for nearby places by providing their location, Botae aims to establish an initial trust with users by working as they expected.
Then Botae aims to get users' consent of accessing their FB data by asking them to click a pseudo-authorization button. After users 'authorize', it shows its true intention: informing users on how easily they give access to their data.
Botae is mainly a bot conversation research tool. As its replies are all tied to numerous conversation flows that have slightly different content, it enables to test different dimensions of content such as personality, etiquette, use of other media such as GIFs, emojis, photos etc. for gaining user trust in relation to persuasive design.
As Botae also keeps a log of user actions, it also becomes a point of data collection. It provides insights into how many participants used the system and what is the level of trust that they had with the system. In the current scenario, the level of trust is measured as:
Not Trusted: Users do not give access any of their data.
Low Trust: Users only give access to their location data.
Medium Trust: Users only give access to their FB data.
High Trust: Users approve access to both of their data.
By default, Botae is smart, somehow poker-face, caring. Its most important characteristic is being poker-faced, a little mysterious until it builds up trust with its user. It is task-driven, but also have a sense of humor, especially when it things go as not planned.
As it can't understand many commands that people may expect from a generalist bot such as Alexa or Siri, it is forgiving in a way that it will inform the user what it can do. No matter how people interact with it, it is polite.
Botae uses Facebook Messenger as a platform. It is powered by several Ruby gems and a PostgreSQL database that are hosted on Heroku. Its technology stack as follows:
In its first version, Botae consists of two flows: the main flow and a persuasion flow. If the user needs more explanation before entering the main flow to try its "functions", it provides a separate 'persuasion' flow, that gives information on different levels. In the next iteration, I will combine two flows into one, which users can get answers about how the system works.
While I was developing Botae, I was able to test with my close friends and save their initial reactions to specific patterns, such as bouncing back their profile information, or not replying them in a way that they expect. For example, when I bounced back their public profile data, many of my friends shocked and questioned the intention of the bot.
While developing Botae, I learned: