Evaluating Age Bias In E-commerce

Abstract

Software should be designed for diverse end users particularly because people of different ages, genders and/or levels of digital literacy use software differently. Understanding the needs of diverse end-users is essential when developing successful software. This article explores how people from different age groups behaved when they used e-commerce applications to inform the development of an age bias evaluation tool. We focused on age because the software industry is dominated by a younger workforce, and currently there are no systematic methods to evaluate age bias in software. We chose the e-commerce domain as the use of e-commerce is increasing rapidly, particularly because of the COVID-19 pandemic lockdown. Following the InclusiveMag methodology, this study aimed to determine if there are specific requirements for different generations. We explored the views of people from different age groups about e-commerce applications using semi-structured interviews and field notes when observing people as they used an e-commerce application. The interviewees were purposively sampled from well recognised generational classifications based on age, including Generation Z (Gen Z) and Generation Y (Gen Y) (combined), Generation X (Gen X), Baby Boomers (BB), and the Silent Generation (SG). Based on four different facets, we built personas to represent the different age groups and performed a cognitive walkthrough with the personas for two e-commerce applications. The results found potential age bias against older people when using e-commerce applications. This will directly inform the development of an age-inclusiveness Magnifier (AgeMag) to help identify age bias within e-commerce applications.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 29 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.