From Colonial Legacy to AI Progress: Re-defining Gender and Data Protection in Africa

From Colonial Legacy to AI Progress: Re-defining Gender and Data Protection in Africa


The legacy of colonialism in Africa is still visible in various forms, impacting different aspects of society such as the way people live, the economy, and the government structure. Emerging Artificial Intelligence (AI) systems in Africa have drawn attention to the parallels between colonialism and the current algorithmic invasion. The term algorithmic invasion refers to the widespread integration and impact of AI algorithms in various aspects of society. It implies that AI systems often collect and analyze data about people and communities, and to make decisions that affect their lives. The reason behind this is that the progress and application of AI technology in Africa predominantly stem from Western viewpoints and priorities, potentially leading to the continuation of exploiting and disregarding the needs of local communities. For example, AI systems used to make lending decisions, have been shown to be more likely to deny loans to African people than to white people. The presence of AI in Africa is often seen as an extension of the exploitative and dominant practices from the colonial era, fueled by political and economic motives. One of the biggest concerns about AI in Africa is the issue of data colonialism. This refers to the practice of extracting data from African countries without their consent or knowledge, and then using that data to develop AI systems that benefit foreign companies and governments. For example, a study by the Center for International Policy found that Facebook was collecting data from millions of Kenyans without their consent, and then using that data to target them with advertising.

To avoid this, it is essential to ensure that AI technology in Africa is developed and implemented from the social contract theory where society operates because of an implicit agreement to ensure moral and political rules of behavior and ethical frameworks developed by African philosophical ideologies. For example, an African ethical framework might prioritize community well-being, ubuntu (a philosophy of interconnectedness and compassion), or respect for cultural diversity. By grounding the development and implementation of AI technology in these frameworks, it becomes possible to address concerns such as algorithmic bias, privacy, inclusivity, and the impact on marginalized communities. Africa has a long history of colonialism, which has had a significant impact on gender relations and data protection on the continent. In the early 20th century, the British colonial government in Kenya collected data on the Kikuyu people. This data was used to identify and detain Kikuyu people who were suspected of being involved in the Mau Mau rebellion. This colonial legacy has created a context for data extraction and exploitation, which affects the way data is collected, analyzed, and used in AI systems.

By grounding the development and implementation of AI technology in Africa within social contract theory and ethical frameworks shaped by African philosophers, it is possible to challenge the dominant narrative about AI in Africa that it will be used by foreign companies and governments to take advantage of the continent while its trajectory is still being determined in the continent. This approach seeks to rectify the historical imbalances and ensure that AI is harnessed for the benefit of local communities while safeguarding their rights and interests.

This article looks at how gender and other social identities, such as gender, race, and class, can overlap, affecting data protection in Africa. The ways in which AI systems continue and magnify gender bias and discrimination, the value of incorporating diverse perspectives in gathering and analyzing data, and the importance of safeguarding data in the African context. These issues demonstrate the colonial legacies dominated by Western technological corporations and their imported solutions across Africa that reflect a form of algorithmic colonialism, where corporate agendas replace government forces in driving domination and control.

Inclusive and Equitable AI: Addressing Gender Bias and Power Imbalances

The collection, analysis, and use of data in AI systems may be affected by gender-based violence, racial and ethnic profiling, and discrimination. Technologists possess knowledge, authority, and power to organize and classify human activity, leaving humanity as mere producers of data, often referred to as “human natural resources. These issues highlight the need to examine the impact of gender in algorithmic colonialism. The development of AI technologies may inadvertently perpetuate gender biases and inequalities if not properly scrutinized and regulated from a feminist perspective. Ultimately AI technologies can be used to reinforce or challenge existing gender norms and power structures. According to the Pew Research Center survey, women are more inclined than men to be enthused about the rising usage of AI computer programs in daily life (13% vs. 22%). This highlights the potential systemic biases and power imbalances that can lead to the marginalization of certain groups, including women, in the development and application of AI technology. Through a feminist lens, it is possible to promote the creation of AI systems that are inclusive, equitable, and representative of diverse perspectives, while also ensuring that they do not reinforce or exacerbate existing inequalities. When considering the gender digital divide. Women are less likely than men to have access to technology and the internet. For example, in Sub-Saharan Africa (13%), the gender difference in mobile ownership is substantially larger.

AI systems have the potential to reinforce and perpetuate gender stereotypes, resulting in discrimination against women. To illustrate, consider an AI system utilized for making loan decisions. If this system has been trained on biased data indicating that individuals from certain racial groups, such as whites, have a higher likelihood of financial stability, it may disproportionately recommend them for loans. As a result, women and other marginalized groups may face obstacles in accessing financial opportunities due to the biased outcomes produced by these AI systems. Hence, it is important to develop AI systems that take diversity and inclusivity into account in their design, in order to prevent the perpetuation of harmful biases. This can be achieved through the inclusion of diverse perspectives and data sets during the development process and by ensuring that AI systems undergo regular checks and balances to avoid discriminatory outcomes.

The contributions of African women in data science are significant and cannot be ignored. Women-led data initiatives in Africa prioritize ethical data collection and usage, which is critical to promoting gender equality in the field. One such initiative is the African Women in Data Science (AWDS), which is focused on empowering women in data science and promoting gender equality. AI systems can be built to be more inclusive and fair by prioritizing different viewpoints in data collection and analysis, resulting in better outcomes for all people. The need for data protection extends beyond individual privacy to include socioeconomic and political implications for the continent. As a result, it is critical to prioritize gender risks and data protection measures in the design and deployment of AI systems.


To promote inclusive and gender-sensitive AI systems, policymakers and industry leaders in Africa should prioritize ethical data collection and analysis. This can be achieved by developing comprehensive or well-enforced policies and regulations that promote diversity and inclusivity in data collection and analysis. For example, the South African POPI Act and Kenyan Data Protection Act requires processing of personal data in a fair and transparent manner, and to take steps to protect the privacy of individuals. However, both do not specifically address gender bias in data collection and analysis. In addition to the above recommendations, it is also important to remember that AI and data science are not a panacea for gender inequality. These technologies can be used to reinforce or exacerbate existing inequalities if they are not carefully designed and implemented. It is therefore essential to have a gender-sensitive approach to the development and use of AI and data science in Africa. By working together, policymakers, industry leaders, and women in data science can help to ensure that these technologies are used to create a more equitable and just future for all Africans.

Photo by Markus Winkler on Unsplash

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