Gender blind AI design puts African women at greater privacy risk

Women across Africa are navigating AI-powered systems that increasingly shape access to healthcare, finance, education, and digital services, but new research finds these technologies often amplify existing privacy and power imbalances. The study warns that without gender-sensitive design, AI risks deepening exclusion rather than promoting digital inclusion.

The findings are detailed in a study, Women experience privacy differently: towards a gender and inclusion by design (GEIbD) approach in Africa, published in AI & Society, that explores how women's lived experiences influence data disclosure, trust, and participation in AI systems.

Gendered privacy risks embedded in AI deployment

The research highlights that women's privacy concerns extend beyond conventional data protection issues to include bodily integrity, psychological safety, reputation, and social standing. In many African contexts, women face heightened scrutiny within families, communities, and institutions, which shapes how they disclose information and engage with technology. When AI systems collect, analyze, and repurpose personal data without accounting for these dynamics, the risks multiply.

Across sectors, the study finds that women often have limited bargaining power over data sharing. In healthcare, financial services, and digital identification systems, women may feel compelled to disclose sensitive information to access essential services. This coerced participation undermines meaningful consent and increases exposure to misuse, profiling, or secondary data exploitation. The authors note that such risks are particularly acute where legal protections are weak or poorly enforced.

The study also identifies a paradox in data participation. Faced with privacy concerns and fear of surveillance, some women deliberately limit their engagement with AI systems or provide incomplete information. While this can serve as a self-protective strategy, it also leads to underrepresentation in datasets, reducing the accuracy of AI models and reinforcing gender bias. In turn, AI systems trained on skewed data produce outcomes that further marginalize women, creating a self-reinforcing cycle of exclusion.

The authors find that AI-powered monitoring technologies, when introduced without safeguards, can exacerbate social control over women's bodies, movements, and choices. In patriarchal settings, digital surveillance may be repurposed by family members, employers, or institutions to police behavior, restrict autonomy, or enforce conformity. These harms, the study argues, are rarely anticipated during system design.

Importantly, the research challenges the idea that privacy harms are accidental byproducts of innovation. Instead, it shows that many risks stem from design choices that prioritize efficiency, scalability, and data extraction over human rights considerations. By treating users as abstract data subjects rather than socially situated individuals, AI systems embed structural inequality into their operation.

Why existing AI frameworks fall short

A key finding of the study is that current AI governance and development frameworks are ill-equipped to address gendered privacy risks. Popular ethical AI guidelines emphasize transparency, fairness, and accountability, but often lack concrete mechanisms for integrating gender analysis into technical workflows. As a result, privacy considerations are addressed superficially or retroactively, rather than being embedded from the outset.

The authors identify a significant knowledge gap among AI developers and policymakers. Many practitioners lack training in gender studies, human rights law, or socio-legal analysis, limiting their ability to recognize how design decisions affect different user groups. This gap is compounded by the dominance of technical metrics, such as model accuracy and performance, in defining success.

Legal frameworks offer partial protection but remain fragmented. While data protection laws exist in several African countries, enforcement is inconsistent, and gender-specific harms are rarely acknowledged. The study notes that consent-based models of data governance often fail in contexts where women's choices are constrained by economic necessity or social pressure. In such cases, formal compliance does not equate to substantive protection.

The research also highlights the absence of participatory design processes. Women are frequently excluded from decision-making about AI systems that affect their lives, resulting in technologies that reflect male-dominated perspectives. This exclusion not only increases privacy risks but also undermines the legitimacy and adoption of AI tools.

Another challenge identified is the lack of accountability across the AI lifecycle. Responsibility for mitigating privacy harms is often diffused across developers, deployers, and regulators, creating gaps where no actor feels fully accountable. The study argues that without clear role definition and oversight, ethical commitments remain aspirational rather than operational.

The authors caution that these shortcomings are not unique to Africa but are intensified by structural inequalities, limited regulatory capacity, and rapid digitalization. As AI adoption accelerates, the window for corrective action is narrowing.

A gender and inclusion by design path forward

To address these challenges, the study introduces the Gender Equality and Inclusion by Design (GEIbD) framework, a structured approach aimed at embedding gender and privacy considerations throughout the AI development lifecycle. Unlike abstract ethical principles, GEIbD is presented as a practical, non-technical toolkit that can be used by developers, organizations, and policymakers.

The framework was developed through interdisciplinary collaboration and participatory workshops involving experts from law, computer science, ethics, and gender studies across multiple African countries. It emphasizes early-stage problem definition, urging developers to assess who may be harmed by an AI system and under what conditions before technical development begins.

GEIbD promotes continuous risk assessment rather than one-time evaluations. By integrating gender and privacy checks at each stage of design, deployment, and maintenance, the framework seeks to prevent harms from being locked into systems. It also stresses the importance of inclusive stakeholder engagement, ensuring that women's voices inform system goals, data practices, and governance structures.

Capacity building is another core pillar. The study argues that meaningful change requires investment in education and training for AI practitioners, regulators, and institutional leaders. Without improved understanding of gendered privacy risks, technical fixes alone will fall short.

The authors also call for stronger regulatory alignment. They recommend that data protection authorities explicitly recognize gendered harms and incorporate them into enforcement strategies. This includes moving beyond consent-centric models toward rights-based approaches that account for power imbalances.