Measuring AI's Social Impact on Human Wellbeing: A Cross-Income Methodology

Why this matters

Artificial intelligence is reshaping how people connect, trust, communicate, and organize socially — but its effects are not uniform. A teenager in a high-income country losing social skills to AI companion apps, a rural community in a low-income country gaining first-time access to translated health information via AI, and a middle-income democracy watching AI-generated disinformation erode institutional trust are all "AI's social impact" — yet they are barely comparable phenomena. Most existing AI indices measure adoption, investment, or economic productivity. Few measure what AI is actually doing to the social fabric of human life, and fewer still do so in a way that's fair across radically different economic and infrastructural contexts.

The Global Wellbeing Forum's AIHWI Social Dimension exists to close that gap: a methodology built specifically to be meaningful in a wealthy tech hub, a rapidly digitizing middle-income economy, and a low-connectivity rural region alike.

Core design principles

1. Net impact, not net sentiment. The methodology measures positive and negative social indicators as separate scores, then combines them into a net social impact score. This avoids the common flaw of composite indices where real harms get quietly canceled out by unrelated gains. A country can score well on civic-access tools while still showing serious erosion in social trust — and the index will show both.

2. Income-tier segmentation, not a single global bar. Nations are grouped into high-, middle-, and low-income tiers (aligned with World Bank classifications) and evaluated against tier-adjusted baselines and indicator sets. A rural connectivity gain that is transformative in a low-income tier may be a non-event in a high-income one; a decline in face-to-face social interaction may only be measurable where smartphone and app penetration is already near-universal. Comparing raw scores across tiers without this adjustment produces misleading rankings — a poorer country could appear to be doing "worse" simply because it's earlier in a very different adoption curve.

3. Two data pillars, not one. The methodology deliberately combines two distinct evidence streams rather than relying on national statistics alone:

• Research-based analysis: household and social surveys, academic studies on misinformation and trust, national digital-wellbeing data, and quantitative panels (drawing on sources such as ILO, ITU, WHO, OECD, and Edelman-style trust barometers where available).

• Grassroots analysis: structured qualitative fieldwork — community focus groups, local interviews, and case studies conducted in partnership with in-country social scientists and civil society organizations. This layer exists specifically to capture effects that never show up in national statistics: dependency patterns, shifts in intergenerational trust, community cohesion changes, and lived experiences of algorithmic bias or exclusion.

Neither pillar alone is sufficient. Survey data can miss what's happening on the ground in under-resourced or under-surveyed regions; grassroots interviews alone can't establish statistical comparability across 190+ nations. The two are triangulated against each other during scoring, and where they diverge significantly, that divergence is flagged and reported rather than smoothed over.

Indicator structure

Each country's social score is built from indicators grouped into positive and negative categories:

Positive indicators: connection and accessibility gains (translation, disability access), community support tools (mental health and companion technology usage and outcomes), civic participation enablement, and social-mobility effects from AI-assisted education.

Negative indicators: social isolation trends, misinformation and institutional trust erosion, algorithmic polarization, discriminatory outcomes in AI-mediated systems (hiring, lending, policing), and dependency or agency-loss patterns, particularly among children and elderly populations.

Indicators are normalized to a 0–100 scale within each income tier before aggregation, using weights set by an independent advisory panel of social scientists and civil society representatives — reviewed and re-published each cycle so the weighting logic itself is auditable, not a black box.

Fieldwork and partnership model

Because grassroots research is central to this methodology, in-country delivery relies on a partnership network rather than a centralized research team:

• Local social scientists design and adapt interview protocols to cultural and linguistic context, rather than translating a single global instrument.

• Civil society organizations provide access to communities that formal surveys often miss — informal settlements, indigenous communities, refugee populations, and other groups underrepresented in national statistics.

• Independent academic reviewers validate findings before publication, checking both the quantitative aggregation and the qualitative fieldwork for methodological soundness.

This model is deliberately slower than a purely desk-based index, but it is the only way to make claims about "grassroots-level" impact credible rather than assumed.

Coverage and rollout

Given real constraints on data availability and fieldwork capacity, coverage will scale in phases: an initial cohort of countries with strong existing data infrastructure and established civil society partners across all three income tiers, expanding annually as partnerships and funding grow. Every published scorecard will clearly state its confidence level and data completeness, rather than presenting incomplete-data countries with false precision.

Transparency and limitations

The Global Wellbeing Forum commits to publishing the full indicator list, weighting rationale, and data sources alongside every release, and to disclosing where an income tier's score rests more heavily on modeled estimates than direct measurement. Social impact is inherently harder to quantify than economic impact; this methodology treats that difficulty as something to be disclosed, not hidden behind a single tidy number.