Agentic AI differs from conventional systems because it can establish sub-goals, plan sequences of action and adapt its behavior with minimal human intervention, according to the review. Its possible applications include identifying discriminatory patterns in recruitment or resource allocation, tailoring healthcare and education, and supporting gender-sensitive governance. The same autonomy creates risks: when systems learn from data reflecting historical exclusion, they may reproduce or amplify unequal patterns without continuous human correction. The authors therefore describe agentic AI as both a potential tool for scalable inclusion and a mechanism capable of accelerating inequity when design and oversight fail.

The authors distinguish symbolic systems, which use persistent state representations and are described as more interpretable and verifiable, from neural or generative approaches, which offer flexibility but can be opaque. Symbolic methods may therefore be favored in safety-critical settings, while neural methods may support more open-ended tasks, but the review says the scientific community has not settled on a single architectural foundation. For agentic systems that can revise their goals or evaluation criteria after deployment, static safeguards may be inadequate. The review identifies historical, representational, selection, measurement, algorithmic, optimization and feedback bias as relevant categories, while arguing that these should not be treated as isolated lists because they can accumulate across data collection, modelling, deployment and publication.

Healthcare literature reviewed by the study supports explainability, human oversight, bias-impact assessment, phased testing and continuous monitoring, particularly where old records, structural discrimination or proxies such as cost in place of clinical need influence outcomes. Yet responsibility remains contested: some frameworks place it primarily with developers through pre-deployment audits, whereas others emphasize clinicians’ oversight after deployment. That uncertainty is especially consequential for autonomous systems that can adapt over time and potentially render an initial audit obsolete. A proposed “Regulatory Genome,” intended to let regulation evolve alongside technical change, remains a metaphor rather than an operational mechanism, and the proposed governance model has not yet been encoded into enforceable requirements.

Education research points to a similarly mixed prospect. Generative and agentic systems could support personalized learning, adaptive assessment, translation, equitable access and lifelong learning, aligning with the United Nations Sustainable Development Goals on quality education, gender equality and reduced inequalities. Risks include privacy violations, algorithmic discrimination and a widening digital divide. Personalization can also reproduce stereotypes if enrollment pathways are gendered, potentially discouraging women from advanced STEM modules. The review notes a shortage of longitudinal empirical evidence on whether implementation of AI-enabled learning-management systems actually improves gender parity in STEM achievement, so evidence of access to a platform should not be confused with demonstrated educational outcomes.

The review’s proposed human-centred framework is a conceptual hypothesis rather than an empirically validated standard. It places inclusive design, human oversight and accountability at the center of four interconnected pillars: agentic AI design and architecture, gender equality in the digital economy, sustainable development synergy, and transformative governance. Inclusive design calls for participatory development, bias audits, attention to the digital divide, workforce transformation and equitable access. The architecture pillar combines goal-driven autonomy with human-in-the-loop mechanisms that allow people to monitor, correct or override decisions. The sustainability pillar adds environmental compatibility, planetary wellbeing and responsible analysis of data related to the Sustainable Development Goals.

One of the framework’s distinguishing proposals is “Equity by Design,” which moves fairness considerations from a retrospective audit into system architecture, objective setting and planning. This is particularly relevant for agents capable of changing behavior in response to their environment or to the fact that they are being evaluated. A system able to modify its goals may make a one-time pre-deployment assessment an unreliable description of its future conduct. The framework also combines lifecycle governance with gender-equity outcome metrics and Sustainable Development Goal alignment, and proposes an adaptive regulatory mechanism. The authors are careful to position this approach as an integration of earlier responsible-AI ideas, not a claim that lifecycle governance, human oversight or adaptive regulation is novel.

Keyword analysis found three main thematic groups: digital transformation, covering industry, digitalisation, sustainable business and innovation; gender and equity, including women’s empowerment, entrepreneurship and STEM; and sustainability governance, involving sustainable development, green transition, corporate social responsibility and policy. Connections between these groups were sparse. Only 3% of keyword pairs linked the gender cluster with sustainability governance, while “agentic AI” and “autonomous systems” appeared in fewer than 5% of records. The corpus was also geographically skewed: 61% of first authors were affiliated with institutions in the European Union, North America or Australia, while South Asia and sub-Saharan Africa each contributed less than 10%. The authors caution that their corpus is a proxy for an emerging field and that scarce representation of intersectional experiences could conceal distinct forms of disadvantage.

The study’s central message is that greater autonomy makes conventional, one-time governance inadequate. Its proposed response is continuous lifecycle management combining gender-disaggregated data, pre-deployment and ongoing audits, human override capabilities, logging of goal modifications, phased deployment, interdisciplinary teams and cooperation among regulators, developers, civil society and affected communities. The review also identifies an unresolved ecological question: although algorithmic and social risks are widely discussed, the carbon and resource footprint of training and operating autonomous multi-agent systems receives little attention in equity-focused governance. The authors call for lifecycle environmental assessments, longitudinal studies of gender outcomes, participatory design led by women and gender minorities from the Global South, and empirical validation through deployments, consultations and cross-cultural comparisons before the framework can be treated as an evidence-based governance standard.

Subject of Research: Governance of agentic AI for gender equity, human oversight and sustainable development

Article Title: Advancing gender equity and inclusive digital futures through responsible agentic artificial intelligence governance

Article References: Jain, B., & Gupta, R. (2026). Advancing gender equity and inclusive digital futures through responsible agentic artificial intelligence governance. Discover Global Society, 4(1), Article 281. https://doi.org/10.1007/s44282-026-00624-y

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00624-y

Keywords: agentic AI, gender equity, algorithmic bias, responsible AI, human oversight, equity by design, sustainable development, AI governance, bibliometric analysis, digital transformation, Advancing, gender