This week’s topics:
In Class Activity: Bot Conversation Prototype
This week’s class focused on the attention economy, exploring themes in persuasive design, algorithmic anxiety, surveillance, and predictive analysis. We started off with an exhibition of the Health AI bots that students prototyped with Figma and Poe. One team’s work carefully considered themes of forming trust in Health AI and chose to use a female presenting bot that focused on expressing empathy. Another team designed for language barriers and data sets that may not account for translation in cultural context. One question that came up was: who is held liable if a startup is using a language model that is developed from another company?
After the Bot Conversation debrief, Ariam explored the downsides of the move fast and break fast mentality that oftentimes comes with technosolutionism. All technology is embedded with human values. Thus, because there is no such thing as value-neutral technology, design is a political act. The question then becomes who are we designing in service to? And are we amplifying a matrix of domination?
In the context of surveillance, constant data collection, and profiling, predictive analytics can have a large impact on people’s lives. Historically biased data in predictive analytics can affect someone’s loans, employment, welfare, and more. Even with laws attempting to protect certain groups, proxies are used to discriminate. For example, zip codes can be a proxy for race and can determine whether or not someone qualifies for a loan.
In class: Design an Attention Attack Activity
Team of 3-4 students were assigned an industry and were tasked to create the most targeted approach to acquire a new customer. After coming up with a company name, logo, and product, students tailored their advertising campaign to target towards a specific provided profile.
Given a Potential Customer Algorithmic Profile, students had to pick three different pieces of data (i.e. biometric facial data, cookies, liking posts, etc.) from the Data Market and come up with a rationale for why they picked to buy that data.
