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Md. Romael Haque

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Book Open access Jul 2026

DAIEM: Decolonizing Algorithm's Role as a Team-member in Informal E-market

In Bangladesh’s rapidly expanding informal e-market, small-scale sellers use social media platforms like Facebook to run businesses outside formal infrastructures. These sellers rely heavily on platform algorithms—not just for visibility, but as active collaborators in business operations. Drawing on 41 in-depth interviews with sellers, buyers, and stakeholders, this paper examines how people in informal e-market perceive and interact with the algorithm as a “team member" that performs sales, marketing, and customer engagement tasks. We found that while sellers and local tech entrepreneurs are intrigued to develop services to support this industry, buyers and investors of the industry put their greater trust in human interactions. This surfaces a postcolonial tension associated with cultural values, local tech education and training, and a mismatch between the global and Bangladeshi e-markets’ growth. We expand this discussion from multiple ongoing HCI, political design, and AI design angles. We also address the postcolonial tension and support the decoloniality movement in informal e-markets by proposing the DAIEM framework that consists of six components: autonomy and agency; resistance; locality, culture, and history; rationality; materiality; and advocacy. Supporting the decoloniality and informality sentiment in informal e-market and other similar sectors, DAIEM will serve both as a guideline for algorithm design and as an analytical tool.

Atm Mizanur Rahman, Md. Romael Haque, Sharifa Sultana · 0 citations
Book Open access Jul 2026

From Scripted Responses To Therapeutic Dialogue: A Linguistic And Human Values Analysis Of Mental Health Chatbots

Mental health (MH) chatbots are increasingly used to provide accessible, on-demand emotional support, yet it remains unclear how these systems linguistically construct and communicate care. This work-in-progress examines whether MH chatbots produce responses that reflect supportive value orientations and counseling-adjacent tone. We conduct an observational analysis of responses from three widely used MH chatbots (Wysa, Sintelly, and Youper) across context-aware scenario prompts and a standardized-question session. Responses are analyzed using the SemEval’23 “Adam Smith” human value detection model and LIWC’22 psycholinguistic measures, including Language Style Matching (LSM), Clout, and Authenticity. Values such as “Security: Personal” and “Benevolence: Caring” appear consistently across systems, with contextual variation in secondary value emphasis. Linguistic patterns show moderate-to-high LSM and consistently high Clout, with Authenticity varying by scenario. These findings are exploratory signals intended to inform future evaluation and design of supportive conversational mental health systems.

Maleeha Sheikh, Chao Chen, Romael Haque · 0 citations

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