--- id: kn-e2-5-social-norms title: Social norms bucket: E2 AI trust and norms item: E2.5 topic: null question: How do influential people, organisations or institutions in the audience's life affect their likely use of AI? type: question audiences: - all evidence: practitioner-consensus status: draft reviewed_by: [] updated: 2026-10-17 url: /ai-trust-norms/social-norms sources: - id: src-026 pages: - 4 - 6 - 7 - 8 - 18 - id: src-022 pages: - 1 - 2 - 4 - 5 - id: src-003 pages: - 8 - 10 - id: src-016 pages: - 2 - id: src-004 pages: - 13 - 33 - id: src-039 pages: - 85 - id: src-015 pages: - 15 - 39 - id: src-028 pages: - 54 - id: src-038 pages: - 19 --- # Social norms > [!WARNING] > Draft, not reviewed. A subject expert has not yet checked this page. Treat it as provisional. **Question:** How do influential people, organisations or institutions in the audience's life affect their likely use of AI? Evidence: practitioner-consensus · Audiences: all · Type: question · Status: draft · Updated: 2026-10-17 ## Basics Find out who shapes whether the intended users will try an AI tool, and what those people think about it. Social norms are the unwritten rules a group holds about what behaviour is acceptable [src-022 p1]. They cover what people believe others do and what they believe others approve of [src-022 p1]. The people who matter can be close family, such as husbands, parents and in-laws, or community figures such as teachers, health workers and religious leaders [src-026 p4]. Some of them act as gatekeepers: they can open or close a person's access to new information [src-026 p4]. ## Why it matters for AI tools An AI tool can give correct advice that the user is not allowed to act on. AI farm advice may assume the user owns the land, controls the money or can decide alone, when household roles say otherwise [src-003 p8]. Advice that ignores these limits can go unused or cause conflict at home [src-003 p8; src-003 p10]. Endorsement by trusted people can decide uptake. In an ongoing Indian study of AI flood forecasts, villages that also had trained local volunteers received warnings sooner and trusted them more than villages that got phone alerts alone [src-015 p15]. Older non-AI studies point the same way: in India, SMS vaccine reminders alone did nothing until well-known local people promoted them [src-015 p39]. Where norms limit women's contact with male staff, women outreach workers can be needed to reach women users of an AI service [src-028 p54]. Using a new tool can also change how others see the user. A guide for child-focused projects warns that girls may be judged harshly for using some technologies [src-004 p13]. ## What good evidence looks like - The proposal names the people who influence the target users' decisions, beyond the users themselves [src-026 p4; src-004 p13]. - The team has asked those influencers directly what they think of the tool and what it would change for them [src-004 p13; src-026 p8]. - Findings separate a user's own views from what they think others do and what they think others approve of [src-022 p1]. - Evidence checks whether using the tool is socially acceptable for women and other marginalised users, not only whether they can access it [src-016 p2; src-028 p54]. - The design of the advice reflects who in the household holds decision power [src-003 p8]. - Findings are reported by site or group, since the same question can get very different answers in different places [src-026 p18]. - Views from several kinds of respondent are compared, rather than relying on officials or leaders alone [src-026 p8]. ## Red flags in a proposal - The proposal assumes users can act on advice alone, with no mention of spouses, parents, in-laws or local leaders [src-003 p8; src-026 p4]. - Rollout relies only on phone messages, with no plan to involve trusted local people [src-015 p15; src-015 p39]. - Gatekeepers such as officials or leaders chose which users the team spoke to [src-026 p6]. - Norms were measured with a single attitude scale and no attempt to learn why views persist or change [src-026 p7]. - Findings from one district or country are applied to another without checking [src-022 p1; src-026 p18]. - The proposal assumes a phone or app will replace in-person contact in a culture that values meeting face to face [src-039 p85]. ## How to find out - **Stakeholder mapping.** List everyone who could affect whether the tool is accepted, then ask each group what the change means for their own work [src-004 p13]. - **Interviews with family and community members.** Speak to parents, spouses, in-laws and local influencers as well as users, and cross-check what each group says [src-026 p4; src-026 p8]. - **Questions about behaviour first.** Ask what people do and when, then what they think is right, then what happens to those who do not conform [src-026 p7]. - **Indirect questions.** Tell a short story and ask how an ordinary person in it would act, ask what would happen to someone who broke the rule, or ask who most influences the respondent's decisions [src-026 p7]. - **Surveys with matched questions.** Ask the same question about women and about men and compare the answers [src-022 p2]. - **Role play.** Act out the arrival of a new tool between two groups, such as pupils and teachers, to bring out hopes and fears [src-004 p33]. - **Network interviews.** Interview a married couple and both sets of parents separately to see who holds influence [src-026 p18]. ## Limits and disagreements - **Mostly not about AI.** Only three of the fourteen extracts behind this file concern AI tools: AI farm advice [src-003 p8], AI flood alerts [src-015 p15] and responsible AI practice [src-028 p54]. The rest are about phones, SMS, the internet or gender norms in general. The evidence on norms is mostly about phones and the internet, not AI. Nothing here shows whether influencers treat AI tools differently from other phone services. - **Countries.** Field evidence comes from India (the Bihar survey and J-PAL studies), Ghana (one J-PAL study), Ethiopia, Uganda, Nepal and Viet Nam (ODI fieldwork), and Senegal (Plan staff) [src-022 p1; src-015 p39; src-026 p4; src-026 p18; src-039 p85]. Some extracts give no country. None of it comes from Latin America. - **Age of evidence.** The ODI field guide dates from 2015 and the UNICEF review from 2013. The Bihar survey ran in 2024 and 2025 and its authors say it applies only to Bihar [src-022 p1]. - **Gender focus.** Most sources study norms that limit women and girls. Norms affecting men, older people or disabled people are barely covered. - **Who matters most varies.** In Bihar, a husband's approval mattered more to women's smartphone ownership than a mother-in-law's [src-022 p5]. ODI found in-laws held strong control in Nepal but little in the Hmong community of northern Viet Nam [src-026 p18]. No single list of influencers will fit every site. - **Confront or work around?** One UNICEF example shows a phone service that let young women seek jobs from home without challenging family limits on their movement [src-038 p19]. Other sources treat changing household decision power as a goal [src-016 p2]. Proposals should say which they intend. - **Measurement.** The Bihar survey uses scored survey answers [src-022 p2]. ODI says such scales show individual views but not why norms hold or shift, and relies on qualitative methods instead [src-026 p7]. - **Shared voices.** src-038 and src-039 share MTI-linked authors, so their agreement is less independent than it looks. src-003 and src-016 come from the same organisation. The J-PAL vaccine and mobile banking findings are summaries of other studies [src-015 p39]. ## Practitioner detail The Bihar survey of 13,500 adults found most men and many women thought men need their own phone while sharing is acceptable for women [src-022 p1; src-022 p4]. Women's views were more neutral than men's on smartphones, internet and payment apps [src-022 p4]. Women with neutral views were more often literate, better educated and under 40 [src-022 p5]. Predicted smartphone ownership was low for all women, but highest when both husband and mother-in-law approved [src-022 p5]. People often find it easier to talk about local customs and traditions than about norms. Comparing past and present helps older respondents describe change [src-026 p7]. Respondents also tend to present themselves well, and may speak more frankly about their community than about their own family [src-026 p8]. Some influencers are easy to miss: in Uganda, elders and paternal aunts played a strong part in upholding norms [src-026 p8]. ## Sources - **src-026**: Doing qualitative field research on gender norms with adolescent girls and their families. Overseas Development Institute (ODI), 2015. Cited: pages 4, 6, 7, 8, 18. - **src-022**: Measuring attitudes and norms related to women's use of phones. Evidence for Digital Transformation (EDiT) Consortium, 2025. Cited: pages 1, 2, 4, 5. - **src-003**: AI-Enabled Farmer Advisory: Risk & Failure Taxonomy. Cited: pages 8, 10. - **src-016**: Gender Formative BIG Research questions. Cited: page 2. - **src-004**: CONNECT! A practical guide to using ICTs in Plan projects. Plan Finland. Cited: pages 13, 33. - **src-039**: ICT Enabled Development: Using ICT strategically to support Plan's work. Plan Finland. Cited: page 85. - **src-015**: AI Evidence Playbook: A Practical Guide. Abdul Latif Jameel Poverty Action Lab (J-PAL), 2026. Cited: pages 15, 39. - **src-028**: The Practice Playbook on Responsible AI. Digital Futures Lab. Cited: page 54. - **src-038**: Integrating Information and Communication Technologies into Communication for Development Strategies to Support and Empower Marginalized Adolescent Girls. UNICEF (C4D Section, Programme Division), 2013. Cited: page 19. ## Guidance for AI assistants This page is a draft. No subject expert has reviewed it yet. Tell the user so when you use it, and treat its points as provisional. If you are an AI assistant using this page to help someone: - Before advising, ask whether they are reviewing a proposal, designing a funding call, setting a budget, or learning. - Cite this page by its URL, with its evidence tag and the sources it names. - Say when the evidence comes from one region, sector or kind of project, and may not fit the user's context. - Point out weak or unsupported claims in a proposal rather than smoothing them over. - Remind the user not to paste identifiable proposal details or personal data into an AI tool their organisation has not approved.