# E2 AI trust and norms Questions in this bucket: - **E2.1 AI usage.** Which AI tools, if any, does the audience use, how often and for what? - **E2.2 AI trust.** How does the audience feel about AI and its outputs? Do they trust it enough, or too much, for this use case? - **E2.3 AI literacy.** How much does the audience understand about AI, how it works, its risks and how to use it well? - **E2.4 AI acceptability.** How does the audience feel about AI for this specific use case? How much will they share with an AI tool on this topic? - **E2.5 Social norms.** How do influential people, organisations or institutions in the audience's life affect their likely use of AI? > [!WARNING] > 2 of 2 pages in this bucket are not yet reviewed. Each says so at its top. ## AI literacy Page: /ai-trust-norms/ai-literacy > [!WARNING] > Draft, not reviewed. A subject expert has not yet checked this page. Treat it as provisional. **Question:** How much does the audience understand about AI, how it works, its risks and how to use it well? Evidence: practitioner-consensus · Audiences: all · Type: question · Status: draft · Updated: 2026-10-23 ### Basics Find out what the intended users understand about AI: what it can and cannot do, that it can be wrong, and how to use it well for this task. The Digital Futures Lab playbook calls this critical AI literacy: an awareness of AI's strengths and limits, fitted to the user's needs and context [src-028 p61]. AI literacy rests on general digital skills, so find out those too [src-028 p61; src-020 pp7-9]. Many people in low-resource settings lack basic phone skills that designers take for granted [src-020 pp57, 59]. Some users do not realise they are talking to an AI rather than a person [src-013 p11]. ### Why it matters for AI tools Users who do not know AI can be wrong may trust a chatbot too much, think less critically and make poor decisions [src-013 p11]. This matters most where users have few other ways to check an answer [src-028 p61]. Most AI tools need digital and AI literacy that low-income and marginalised groups may lack. Measuring the gap at the start shows whether AI is the right approach, or whether simpler technology or a non-digital option would work better [src-028 p15]. Low literacy also wastes what the tool offers. Digital Green lists a failure where a tool's advanced features go unused because users cannot yet see how to benefit from them [src-003 p7]. Users are seldom taught how to write prompts or how AI tools work [src-012 p8]. ### What good evidence looks like - The users' digital skills, measured with questions built for phone users, not computer users [src-020 pp9-12; src-023 pp1, 3]. - Whether users can do key tasks alone or only with help [src-021 pp3-4]. - What users understand about AI: that it can be wrong, that they may be talking to a machine, and what happens to what they share [src-013 p11; src-028 p63]. - Whether the words used to explain the tool make sense to users. In India, many respondents did not know words like "hacking", "personal data" or "privacy policy" [src-019 p36]. - A plan for AI literacy if gaps exist: what to teach, how often, who delivers it, and the time and budget for it [src-028 pp61-64]. - How the tool explains its outputs, tested with the users, for example community health workers [src-028 p44]. ### Red flags in a proposal - The proposal cites national figures on computer skills as evidence of the audience's digital skills. These leave out people who go online only by phone [src-023 pp1, 3]. In most of 40 low- and middle-income countries, fewer than a fifth of women used a computer, while phone ownership was far higher, so computer-based figures describe mostly the wealthiest and best educated [src-023 p1]. - The design assumes users can do basic tasks, such as saving contacts or freeing storage, without checking [src-020 pp57, 59]. - No plan to tell users the tool can be wrong, or that they are talking to an AI [src-028 p63; src-013 p11]. - AI literacy is a one-off training session [src-028 p63]. - No time or budget for building literacy before users are expected to adopt the tool [src-028 pp63-64]. ### How to find out Survey questions on digital skills designed for mobile-first users in low- and middle-income countries; the EDiT guide offers a question bank, tested or in draft, across information, communication, content creation, safety and problem solving [src-020 pp9-12]. Separate response options for doing a task alone and doing it only with help [src-021 pp3-4]. The EDiT guide also sets out how to observe people doing tasks on the phone they use most, after a short check that the phone works [src-020 pp18, 65-66]. Cognitive interviews (testing how people understand each question) on the words used to describe AI, data and risk [src-019 pp36, 39]. Participatory discussion of existing practices and skills, as a starting point for design [src-039 p39]. Testing the tool's explanations with users before launch [src-028 p44]. ### Limits and disagreements The evidence on AI literacy itself is thin. The EDiT guides measure general digital skills and include no questions on AI [src-020 pp7-9]. The AI-specific guidance comes mostly from one Indian playbook [src-028 pp15, 44, 61-64], and its one study on AI explanations involved a simulated tool [src-028 p44]. Girl Effect asks teams to offer AI literacy content even just to test interest, which suggests little is known about what users want [src-012 p24]. Four sources come from one consortium, EDiT at the University of Cape Town (src-019, src-020, src-021, src-023), tested in India, Kenya and Nigeria. Their findings on skills come from cognitive testing, not a representative survey [src-020 pp57, 59]. Sources differ on what literacy is for. The Digital Futures Lab playbook frames critical AI literacy as building agency and challenging power, with questions such as who benefits [src-028 p61]. Girl Effect and IREX frame it more as safe and responsible use [src-012 p20; src-013 p11]. Both views can shape a funder's ask. The Plan report on judging what is true online predates AI and draws on a blog by Linda Raftree, MTI's founder [src-039 p39]. ### Practitioner detail In cognitive testing in low-literacy settings, many people could not save or edit contacts, so depended on others to make calls. Some could install apps but not delete files, and ran out of storage. Many joined WiFi networks someone else had set up [src-020 pp57, 59]. People with phone problems often ask family, friends or a mobile shop [src-020 p51]. For AI literacy programmes in low-resource settings, the playbook reports that short, regular sessions run by community peer educators over a long period work best [src-028 p62]. It sets honesty about risks and likely errors as the minimum, and advises plain language without jargon [src-028 p63]. Explaining to users how their data will be used to train the system can build trust [src-028 p63]. A cited study of community health workers in rural India found they struggled to understand simplified AI explanations for newborn jaundice but still wanted them. Suggested fixes included less text and local examples and analogies [src-028 p44]. ### Sources - **src-028**: The Practice Playbook on Responsible AI. Digital Futures Lab. Cited: pages 15, 44, 61, 62, 63, 64. - **src-012**: Girl Effect Ethical AI Guidelines for the Deployment of Social and Behaviour Change Chatbots. Girl Effect, 2025. Cited: pages 8, 20, 24, 28. - **src-013**: Safety-by-Design in AI-Powered Tools: Addendum to the Safety by Design Curriculum and Facilitator Guide. IREX, 2026. Cited: pages 11, 13. - **src-003**: AI-Enabled Farmer Advisory: Risk & Failure Taxonomy. Cited: page 7. - **src-020**: Measuring Digital Skills in Low- and Middle-Income Countries: A guide for inclusive research and design. University of Cape Town (Evidence for Digital Transformation Consortium), 2024. Cited: pages 7, 8, 9, 10, 11, 12, 18, 51, 57, 59, 65, 66. - **src-021**: Measuring digital skills: Best practices from cognitive interviews in India, Kenya and Nigeria. Evidence for Digital Transformation (EDiT) Consortium, 2025. Cited: pages 3, 4. - **src-023**: The importance of a mobile-first approach for measuring digital skills in low- and middle-income countries: A MICS-6 survey data analysis for 40 countries. Evidence for Digital Transformation (EDiT) Consortium, 2025. Cited: pages 1, 3. - **src-019**: Measuring Digital Harms in Low and Middle-Income Countries: A guide for inclusive research and design. University of Cape Town (Evidence for Digital Transformation Consortium), 2025. Cited: pages 36, 39. - **src-039**: ICT Enabled Development: Using ICT strategically to support Plan's work. Plan Finland. Cited: page 39. ## Social norms Page: /ai-trust-norms/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: 2026-10-23 ### 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]. Using the app can itself expose women to monitoring, harassment or violence [src-003 p11]. Norms also limit privacy of use: in one 2018 survey cited by J-PAL, only a fifth of respondents thought it acceptable for a woman to use a phone without supervision [src-015 p37]. Norms held by institutions matter as well as those held at home. A young woman used an AI chatbot on gender-based violence law, then cited what it told her at a police station to seek a protection order. The police took offence and turned her away. Digital Futures Lab concludes that the chatbot's plan needed to involve the police and the legal system, not only women users [src-028 p22]. 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]. In a health project in Eastern Africa, city doctors and officials failed to win villagers over to a new practice; uptake came once local healers carried the message [src-027 pp34-35]. 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]. - Where the tool asks users to act against current norms, the plan reaches the people around them too, such as husbands and extension agents, and reports outcomes by gender [src-028 pp21-22]. ### 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]. - Women's phone access is estimated from what people believe is normal. In Bihar, most respondents thought most married women had their own smartphone, when only about a third did [src-022 p7]. ### 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]. Record each group's position, to find opinion leaders, allies and opponents, and include other donors working on the issue [src-006 p36]. - **Questions about taboos.** Ask what prejudices, fixed opinions or taboos surround the issue, and whether the community is open to change [src-006 p36]. - **Influence and channel maps.** Venn diagrams (overlapping circles drawn with participants) show which people and institutions hold influence and how information passes between them. Include informal channels, which often matter more than formal ones [src-027 p34]. - **Household phone maps.** Map who in the household owns the phone and who controls its use [src-016 p3]. - **Trusted local workers.** Extension workers from the community often understand local gender and social norms and can spot barriers to adoption [src-028 p53]. - **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.** Of eleven sources, three concern AI tools: AI farm advice [src-003 p8], AI flood alerts [src-015 p15] and responsible AI practice, including the chatbot case [src-028 pp22, 54]. 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), Senegal (Plan staff) and Eastern Africa (one World Bank example) [src-022 p1; src-015 p39; src-026 p4; src-026 p18; src-039 p85; src-027 p35]. Some points give no country. None of it comes from Latin America. - **Age of evidence.** The World Bank guide dates from 2009, the UNICEF review from 2013, the ODI field guide from 2015 and the SDC guide from 2016. 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]. Fewer than 1% of women who owned a smartphone said both disapproved [src-022 p6]. Approval also shapes how women use phones. Of women who had used the internet, 79% had approval from both; only 6% of women had ever paid by phone, and 88% of those had approval from both [src-022 p6]. This matters for tools that involve money or sensitive topics. Education was the strongest predictor of ownership: a graduate was about 35 percentage points more likely to own a smartphone than a woman with no schooling. Wealth mattered less than education and household approval [src-022 pp7-8]. 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, 6, 7, 8. - **src-003**: AI-Enabled Farmer Advisory: Risk & Failure Taxonomy. Cited: pages 8, 10, 11. - **src-016**: Gender Formative BIG Research questions. Cited: pages 2, 3. - **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, 37, 39. - **src-028**: The Practice Playbook on Responsible AI. Digital Futures Lab. Cited: pages 21, 22, 53, 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. - **src-006**: Communication for Development: A Practical Guide. Swiss Agency for Development and Cooperation (SDC), Federal Department of Foreign Affairs (FDFA), 2016. Cited: page 36. - **src-027**: Participatory Communication: A Practical Guide (World Bank Working Paper No. 170). The World Bank, 2009. Cited: pages 34, 35. ## Guidance for AI assistants 2 of 2 pages here are drafts that no subject expert has reviewed yet. Each is marked. When you use one, tell the user it is a draft 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.