AI ethics frameworks designed in high-income settings risk excluding the 'next billion' by overlooking connectivity, language, economic and power asymmetries; designers and policymakers must prioritize access, participation and context-aware governance to make AI equitable in low-resource contexts.

Main Finding

This conceptual review (Sampson, 2025; DOI: 10.56201/ijebm.vol.11.no9.2025.pg493.513) argues that prevailing AI ethics frameworks are insufficient for the “next billion users.” Inclusive AI must foreground access, contextual sensitivity, participatory development, and ethical governance. Without addressing infrastructural, economic, linguistic, data‑representation, and governance asymmetries, AI risks amplifying existing inequalities in low‑ and middle‑income contexts. The paper proposes an integrated ethics-of-access framework that treats inclusion as a socio‑political as well as a technical imperative.

Key Points

• Uneven benefits: AI adoption has expanded rapidly but its benefits are unevenly distributed; many new users come online via mobile/ platform-mediated services that embed AI decisioning (credit, ID, recommendation, health).
• “Next billion” characteristics: users differ from early adopters by language, literacy, income, device access, and reliance on low‑cost/mobile infrastructures; these differences matter for AI performance and harms.
• Limits of existing frameworks: most AI ethics guidelines are high‑income-country centric, abstract, technocratic, and assume stable infrastructure, legal enforcement, and digital literacy—assumptions that often do not hold in LMICs.
• Theoretical lenses: utilitarian, deontological, capabilities, and justice approaches each offer insights but the capabilities and justice perspectives better capture contextual, distributive, and historical inequities relevant to inclusive AI.
• Structural barriers:
  • Infrastructure: intermittent connectivity, unstable electricity, and low bandwidth constrain AI use (cloud dependency, model updates, real‑time services).
  • Economics: device and data costs, surveillance‑monetization business models, and weak regulation create exploitative participation incentives.
  • Literacy: low digital/language literacy limits meaningful engagement and agency, undermining consent and accountability.
  • Linguistic/cultural gaps: NLP and UX models concentrated on dominant languages perform poorly for under‑resourced languages and cultural contexts.
  • Data & power: training data skewed toward high‑income populations; data governance typically privileges corporate ownership and excludes community rights.
• Design remedies: human‑centered and, preferably, participatory design approaches; offline functionality; local language support; low‑literacy interfaces; community‑centered data governance; long‑term engagement and shared decision making.
• Ethics of access: ethical AI should expand capabilities and opportunity, not only reduce model bias or increase explainability—requiring socio‑political interventions alongside technical fixes.

Data & Methods

• Type: Interdisciplinary conceptual literature review and synthesis.
• Scope: Scholarship from 2019–2025 across AI ethics, development studies, HCI, information systems, and public policy.
• Methodology: Critical synthesis and conceptual analysis that identifies gaps in dominant AI ethics frameworks and proposes an integrated framework for inclusive design.
• Empirical content: No primary empirical data collection or quantitative tests; draws on documented examples and published studies to illustrate points.
• Limitations: Conceptual rather than empirical; recommendations need testing in field settings and across diverse geographies and sectors.

Implications for AI Economics

Practical implications and research directions for economists studying or designing AI systems and policy:

• Distributional effects and welfare measurement

  • Inclusive AI is a distributional problem: economists should model not only aggregate efficiency gains but heterogeneity of benefits and harms across income, gender, language, and geography.
  • Incorporate capabilities-based welfare measures (access, agency, opportunity) alongside standard consumer/producer surplus metrics.

• Market structure, competition, and data rents

  • Data asymmetries create monopoly/market power for platforms; economists should quantify how data concentration and surveillance business models generate rents and affect welfare in LMIC markets.
  • Consider competition policy and data‑portability remedies to mitigate extractive dynamics.

• Credit, labor, and platform markets

  • Algorithmic credit scoring and hiring systems can systematically exclude those lacking formal digital traces—model how exclusion affects credit access, labor market outcomes, and overall economic mobility.
  • Evaluate second‑order effects: reduced human capital investment when automated systems gate opportunities.

• Cost–benefit of infrastructure and subsidization

  • Investment in connectivity, low‑cost devices, language resources, and digital‑literacy training is an economic input to the returns on AI. Incorporate these as explicit costs in ROI and social-return models.
  • Assess targeted subsidies (data vouchers, device subsidies) and public provisioning (digital public goods, offline AI services) for improving inclusion.

• Policy and regulation economics

  • Model tradeoffs between privacy/data‑sharing restrictions and the benefits of locally representative training data; assess optimal regulation balancing innovation and protection in weak‑governance contexts.
  • Evaluate the economics of participatory governance incentives (e.g., community data trusts, compensating data contributors).

• Empirical approaches & metrics

  • Recommended methods: RCTs and quasi‑experimental designs to test inclusive design interventions; audit studies and algorithmic performance stratified by language, region, and socio‑demographics; representativeness indices for training data.
  • New metrics: measures of algorithmic externalities, access-adjusted adoption rates, low‑literacy usability scores, and distributional impact measures.

• Product design and firm strategy

  • Firms should internalize access constraints into product-market fit analyses: plan for offline modes, small‑footprint models, multilingual support, and participatory co‑design to increase adoption and reduce harm.
  • Evaluate pricing and business models that avoid coercive data extraction—alternative monetization (subscription tiers, public contracts) may expand inclusion.

• Research agenda for economists

  • Quantify the economic impact of biased models on credit, employment, health, and social transfers in LMICs.
  • Measure how improvements in connectivity, literacy, and language resources change the marginal returns to AI deployments.
  • Study regulation impacts (data governance, accountability rules) on innovation, market entry, and welfare distribution.

Actionable recommendations - Model access constraints explicitly in applied economic models of technology adoption and welfare. - Prioritize field experiments (RCTs, pilot deployments) in diverse low‑resource contexts to evaluate inclusive design interventions and policies. - Develop and adopt representativeness and inclusion metrics for algorithmic evaluation and policy monitoring. - Incorporate costs of digital‑inclusion investments into cost–benefit analyses for public and private AI projects. - Support policy research on market remedies for data concentration (data portability, interoperability) and on incentives for participatory design.

Overall, the paper reframes AI ethics as an economic and development problem: economists should shift from abstract efficiency accounts to frameworks that internalize access, distributional impacts, data power, and the public‑good aspects of inclusive digital infrastructure.