Publications

@article{chikezie_measuring_2025b,

title = {Measuring SES-related traits relating to technology usage: Two validated surveys},

author = {Chimdi Chikezie and Pannapat Chanpaisaeng and Puja Agarwal and Sadia Afroz and Bhavika Madhwani and Rudrajit Choudhuri and Andrew Anderson and Prisha Velhal and Patricia Morreale and Christopher Bogart and Anita Sarma and Margaret Burnett},

url = {https://doi.org/10.1007/s10664-025-10683-5},

doi = {10.1007/s10664-025-10683-5},

issn = {1573-7616},

year  = {2025},

date = {2025-09-01},

urldate = {2025-09-01},

journal = {Empirical Software Engineering},

volume = {30},

number = {6},

pages = {159},

abstract = {Software producers are now recognizing the importance of improving their products’ suitability for diverse populations, but little attention has been given to measurements to shed light on products’ suitability to individuals below the median socioeconomic status (SES)\textemdashwho, by definition, make up half the population. To enable software practitioners to attend to both lower- and higher-SES individuals, this paper provides two new surveys that together can facilitate measuring how well a software product serves socioeconomically diverse populations. The first survey (SES-Subjective) is who-oriented: it measures who their potential or current users are in terms of their subjective SES (perceptions of their SES). The second survey (SES-Facets) is why-oriented: it collects individuals’ values for an evidence-based set of facet values (individual traits) that (1) statistically differ by SES and (2) affect how an individual works and problem-solves with software products. The surveys’ design goal is worldwide applicability, but as a first step, here we empirically validated both these surveys with deployments at University A and University B (464 and 522 responses, respectively), which showed reliability of both the surveys in a US context. Our results also statistically agree with both ground truth data on respondents’ socioeconomic statuses and with predictions from foundational literature. Finally, we explain how the pair of surveys can be uniquely actionable by software practitioners, such as in requirements gathering, debugging, quality assurance activities, maintenance activities, and fulfilling legal reporting requirements such as those being drafted by various governments for AI-powered software.},

keywords = {AI, Humans},

pubstate = {published},

tppubtype = {article}

}

@article{fallatah_intersectional_nodate,

title = {Intersectional HCI on a Budget: An Analytical Approach Powered by Types},

author = {Abrar Fallatah and Md Montaser Hamid and Fatima A. Moussaoui and Chimdi Chikezie and Martin Erwig and Christopher Bogart and Anita Sarma and Margaret Burnett},

url = {https://doi.org/10.1080/10447318.2025.2474494},

doi = {10.1080/10447318.2025.2474494},

issn = {1044-7318},

year  = {2025},

date = {2025-03-25},

urldate = {2025-03-25},

journal = {International Journal of Human\textendashComputer Interaction},

volume = {0},

number = {0},

pages = {1\textendash24},

abstract = {Intersectional HCI recognizes that humans\' interconnected social identities shape their experiences with technology. However, intersectional HCI requires extensive resources, such as access to intersectional populations, which many HCI practitioners may lack. For these practitioners, we present an analytical approach to bring intersectional lenses to HCI practices. The approach uses types\textemdashnot at the level of identities, but at the level of personal traits drawn from foundational research. We first formally prove that certain analytical methods for detecting inclusivity issues can be meaningfully composed to provide equitable consideration of typically overlooked populations; then present four design use-cases to illustrate what the approach brings to HCI practices; and then empirically investigated one of the four use-cases with 24 HCI participants. Results show that practitioners using the compositional approach detected even more intersectional inclusivity problems than those using a complementary intersectional approach.},

note = {Publisher: Taylor \& Francis 

_eprint: https://doi.org/10.1080/10447318.2025.2474494},

keywords = {AI, Humans},

pubstate = {published},

tppubtype = {article}

}

@article{fallatah_intersectional_nodateb,

title = {Intersectional HCI on a Budget: An Analytical Approach Powered by Types},

author = {Abrar Fallatah and Md Montaser Hamid and Fatima A. Moussaoui and Chimdi Chikezie and Martin Erwig and Christopher Bogart and Anita Sarma and Margaret Burnett},

url = {https://doi.org/10.1080/10447318.2025.2474494},

doi = {10.1080/10447318.2025.2474494},

issn = {1044-7318},

year  = {2025},

date = {2025-03-25},

journal = {International Journal of Human\textendashComputer Interaction},

volume = {0},

number = {0},

pages = {1\textendash24},

abstract = {Intersectional HCI recognizes that humans\' interconnected social identities shape their experiences with technology. However, intersectional HCI requires extensive resources, such as access to intersectional populations, which many HCI practitioners may lack. For these practitioners, we present an analytical approach to bring intersectional lenses to HCI practices. The approach uses types\textemdashnot at the level of identities, but at the level of personal traits drawn from foundational research. We first formally prove that certain analytical methods for detecting inclusivity issues can be meaningfully composed to provide equitable consideration of typically overlooked populations; then present four design use-cases to illustrate what the approach brings to HCI practices; and then empirically investigated one of the four use-cases with 24 HCI participants. Results show that practitioners using the compositional approach detected even more intersectional inclusivity problems than those using a complementary intersectional approach.},

note = {Publisher: Taylor \& Francis 

_eprint: https://doi.org/10.1080/10447318.2025.2474494},

keywords = {AI, Humans},

pubstate = {published},

tppubtype = {article}

}

@article{fallatah_intersectional_nodatec,

title = {Intersectional HCI on a Budget: An Analytical Approach Powered by Types},

author = {Abrar Fallatah and Md Montaser Hamid and Fatima A. Moussaoui and Chimdi Chikezie and Martin Erwig and Christopher Bogart and Anita Sarma and Margaret Burnett},

url = {https://doi.org/10.1080/10447318.2025.2474494},

doi = {10.1080/10447318.2025.2474494},

issn = {1044-7318},

year  = {2025},

date = {2025-03-25},

journal = {International Journal of Human\textendashComputer Interaction},

volume = {0},

number = {0},

pages = {1\textendash24},

abstract = {Intersectional HCI recognizes that humans' interconnected social identities shape their experiences with technology. However, intersectional HCI requires extensive resources, such as access to intersectional populations, which many HCI practitioners may lack. For these practitioners, we present an analytical approach to bring intersectional lenses to HCI practices. The approach uses types\textemdashnot at the level of identities, but at the level of personal traits drawn from foundational research. We first formally prove that certain analytical methods for detecting inclusivity issues can be meaningfully composed to provide equitable consideration of typically overlooked populations; then present four design use-cases to illustrate what the approach brings to HCI practices; and then empirically investigated one of the four use-cases with 24 HCI participants. Results show that practitioners using the compositional approach detected even more intersectional inclusivity problems than those using a complementary intersectional approach.},

note = {Publisher: Taylor \& Francis 

_eprint: https://doi.org/10.1080/10447318.2025.2474494},

keywords = {AI, Humans},

pubstate = {published},

tppubtype = {article}

}

@article{anderson_measuring_2024,

title = {Measuring User Experience Inclusivity in Human-AI Interaction via Five User Problem-Solving Styles},

author = {Andrew Anderson and Jimena Noa Guevara and Fatima Moussaoui and Tianyi Li and Mihaela Vorvoreanu and Margaret Burnett},

url = {https://dl.acm.org/doi/10.1145/3663740},

doi = {10.1145/3663740},

issn = {2160-6455},

year  = {2024},

date = {2024-09-01},

urldate = {2024-09-01},

journal = {ACM Trans. Interact. Intell. Syst.},

volume = {14},

number = {3},

pages = {21:1\textendash21:90},

abstract = {Motivations: Recent research has emerged on generally how to improve AI products’ human-AI interaction (HAI) user experience (UX), but relatively little is known about HAI-UX inclusivity. For example, what kinds of users are supported, and who are left out? What product changes would make it more inclusive?Objectives: To help fill this gap, we present an approach to measuring what kinds of diverse users an AI product leaves out and how to act upon that knowledge. To bring actionability to the results, the approach focuses on users’ problem-solving diversity. Thus, our specific objectives were (1) to show how the measure can reveal which participants with diverse problem-solving styles were left behind in a set of AI products and (2) to relate participants’ problem-solving diversity to their demographic diversity, specifically gender and age.Methods: We performed 18 experiments, discarding two that failed manipulation checks. Each experiment was a 2 textbackslash(textbackslashtimestextbackslash) 2 factorial experiment with online participants, comparing two AI products: one deliberately violating 1 of 18 HAI guidelines and the other applying the same guideline. For our first objective, we used our measure to analyze how much each AI product gained/lost HAI-UX inclusivity compared to its counterpart, where inclusivity meant supportiveness to participants with particular problem-solving styles. For our second objective, we analyzed how participants’ problem-solving styles aligned with their gender identities and ages.Results and Implications: Participants’ diverse problem-solving styles revealed six types of inclusivity results: (1) the AI products that followed an HAI guideline were almost always more inclusive across diversity of problem-solving styles than the products that did not follow that guideline\textemdashbut “who” got most of the inclusivity varied widely by guideline and by problem-solving style; (2) when an AI product had risk implications, four variables’ values varied in tandem: participants’ feelings of control, their (lack of) suspicion, their trust in the product, and their certainty while using the product; (3) the more control an AI product offered users, the more inclusive it was; (4) whether an AI product was learning from “my” data or other people’s affected how inclusive that product was; (5) participants’ problem-solving styles skewed differently by gender and age group; and (6) almost all of the results suggested actions that HAI practitioners could take to improve their products’ inclusivity further. Together, these results suggest that a key to improving the demographic inclusivity of an AI product (e.g., across a wide range of genders, ages) can often be obtained by improving the product’s support of diverse problem-solving styles.},

keywords = {AI, Humans},

pubstate = {published},

tppubtype = {article}

}