Social norms

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.

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.