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Grant: Discovery Grants Program - Individual

York University

$36,000.00

Using Persuasive Technology and Machine Learning Models to Address Digital Inequity

FY 2023-24

Summary
Persuasive Technologies (PTs) are interactive systems intentionally designed to change human behavior. Currently, they are characterized by inequity, cultural insensitivity, one-size-fits-all (OSFA), and the cold-start (CS) problem: the inability to know what persuasive strategies or messages will be effective in changing a new user's behavior. Whereas the goal of PTs is to promote beneficial behaviors by tailoring strategies and messages to users, the OSFA approach has resulted in the underservice of minority groups in digital design, which often caters to the dominant group (eg. Euro-Canadians). While attempts have been made to address the CS problem in e-commerce systems, there have been limited attempts in other domains to bridge the digital divide. So far, most personalization efforts have been based on explanatory models that use self-reports to build static group-based persuasion profiles. This approach assumes that users that share similar demographic attributes are a monolithic group. However, research shows that one size does not fit all. For example, personal strategies may not be effective for all Canadians as Indigenous Canadians may respond to social strategies better given their collectivist worldview. Yet, most existing PT research assumes that national groups (eg. Canadians) are a monolithic group. To address the CS problem affecting mostly marginalized groups, I aim to use Machine Learning methods, which use historical data, user attributes and interaction data to recommend persuasive strategies. Using large-scale studies for different countries and domains, I will build and validate predictive models and share them with other researchers in an Ecosystem for PT Artifacts and Data Sharing (EcoPAD). To address the OSFA problem, I aim to examine, through participatory research, how culture-tailored PTs can reduce digital inequity. The EcoPAD will serve as a collaborative platform for PT researchers in and outside Canada to advance PT research beyond explanatory models existing in silos to make research replication easier and cost-effective. The research program has the potential to improve the effectiveness of digital tools among underserved groups in different cultures. It will provide PT researchers in Canada and worldwide with new collaborative methods, models, and tools that will advance the field. It will strengthen Indigenous capacity building by affording Indigenous researchers the opportunity to lead participatory research and acquire skills in research and project management. The research outcome can help persuade digital stakeholders to integrate Indigenous/rural peoples' cultural norms into digital tools to foster equitable access. The knowledge gained will advance PT research in personalization and tackling digital inequity. Decision-makers can leverage the findings to shape policies and target interventions to underserved groups in and outside Canada, eg. Nigeria where I have strong ongoing research collaborations.
Program
Discovery Grants Program - Individual
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
RGPIN-2023-05193
Name nom
Oyibo, Kiemute
Department département
Electrical Engineering and Computer Science
Organization id
38
Institution établissement
York University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2023
Award amount
36000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
7000
Research subject group en
Other studies in natural sciences and engineering
Research subject group fr
Other studies in natural sciences and engineering
Research subject en
Autres études en sciences naturelles et en génie
Research subject fr
Autres études en sciences naturelles et en génie
Application title
Using Persuasive Technology and Machine Learning Models to Address Digital Inequity
Keyword
Behavior Change Support System; Culture; Digital Health; Digital Inequity; Machine Learning; Mobile App; Personalization; Persuasive Technology; Predictive Model; Recommender System
Application summary
Persuasive Technologies (PTs) are interactive systems intentionally designed to change human behavior. Currently, they are characterized by inequity, cultural insensitivity, one-size-fits-all (OSFA), and the cold-start (CS) problem: the inability to know what persuasive strategies or messages will be effective in changing a new user's behavior. Whereas the goal of PTs is to promote beneficial behaviors by tailoring strategies and messages to users, the OSFA approach has resulted in the underservice of minority groups in digital design, which often caters to the dominant group (eg. Euro-Canadians). While attempts have been made to address the CS problem in e-commerce systems, there have been limited attempts in other domains to bridge the digital divide. So far, most personalization efforts have been based on explanatory models that use self-reports to build static group-based persuasion profiles. This approach assumes that users that share similar demographic attributes are a monolithic group. However, research shows that one size does not fit all. For example, personal strategies may not be effective for all Canadians as Indigenous Canadians may respond to social strategies better given their collectivist worldview. Yet, most existing PT research assumes that national groups (eg. Canadians) are a monolithic group. To address the CS problem affecting mostly marginalized groups, I aim to use Machine Learning methods, which use historical data, user attributes and interaction data to recommend persuasive strategies. Using large-scale studies for different countries and domains, I will build and validate predictive models and share them with other researchers in an Ecosystem for PT Artifacts and Data Sharing (EcoPAD). To address the OSFA problem, I aim to examine, through participatory research, how culture-tailored PTs can reduce digital inequity. The EcoPAD will serve as a collaborative platform for PT researchers in and outside Canada to advance PT research beyond explanatory models existing in silos to make research replication easier and cost-effective. The research program has the potential to improve the effectiveness of digital tools among underserved groups in different cultures. It will provide PT researchers in Canada and worldwide with new collaborative methods, models, and tools that will advance the field. It will strengthen Indigenous capacity building by affording Indigenous researchers the opportunity to lead participatory research and acquire skills in research and project management. The research outcome can help persuade digital stakeholders to integrate Indigenous/rural peoples' cultural norms into digital tools to foster equitable access. The knowledge gained will advance PT research in personalization and tackling digital inequity. Decision-makers can leverage the findings to shape policies and target interventions to underserved groups in and outside Canada, eg. Nigeria where I have strong ongoing research collaborations.
York University - $36,000 Grant (2023-24) | Canada Spends