Working with participants in AI data collections: drawing from user research and communication…
By Arathi Sethumadhavan, David Mondello, and Karen Chappell | "So much of the participant experience during data collections is shaped by the moderator leading the session. It is therefore a vitally important role, and one that can really make a difference in helping to address potential concerns or questions that participants might have." This article provides tips and tricks for moderating a session for data collection.
Designing in Liquid Times: Generative Graphic Design in an Age of Uncertainty
By Marlies Peeters, PLOT(s): Journal of Design Studies | The shift of information from static to mobile and ephemeral has influenced what it means to be a graphic designer. Not only do graphic designers have to adapt to a new medium, but they are no longer the only people who have access to these skills when design tools are made to be easily accessible by people with no design experience as well. With the developments of software and its accessibility, how has graphic design changed as a profession?
Teens use “algorithmic folklore” to crack TikTok’s black box
By Iretiolu Akinrinade & Joan Mukogosi | Despite the prevalence of strategic ignorance inside social media and gaming companies, today’s teen tech users have developed a number of creative and often hilarious strategies to make sure that they are seen, heard, and valued online.
By Katherine Miller | A Stanford researcher advocates for clarity about the different types of interpretability and the contexts in which it is useful.
By Elaine Lee | You may not know it, but as a product designer, you most likely have been designing for products that use some form of artificial intelligence.
Designing trustworthy machine learning systems - Algorithm
Op-ed: Many methods have been developed to promote fairness, transparency and accountability in the predictions made by artificial intelligence and machine learning systems. A technical approach is often the focal point of these methodologies, however, to develop truly ethical machine learning systems that can be trusted by their users, it's important to supplement this with a human-centred approach.