Navigating the Intersection of AI, Data Protection, and Gender in Africa: A Feminist Approach
The African continent is witnessing significant progress in artificial intelligence (AI), as countries such as Rwanda, Kenya and South Africa invest in its development. However, concerns arise regarding the potential perpetuation of biases and discrimination, leading to further marginalization of underrepresented groups. Biased historical data in loan decision-making algorithms, for example, can disadvantage women and people of color. In South Africa, AI-powered algorithms are being used to make decisions about who gets access to loans and other financial services. However, the algorithms have been criticized for being biased and disenfranchising women and people of color, making it more difficult for them to access the resources they need.
To address these issues, inclusive and representative data collection and analysis are crucial to mitigate biases within AI systems. Additionally, international policy development on AI and data protection is vital. Some governments across the African continent are establishing regulations and frameworks to ensure ethical AI use, protecting individual rights and privacy. Gender-related concerns must also be considered, as biases and discriminatory practices disproportionately affect women. By understanding these intersections, AI technologies can be developed and deployed to promote equality, fairness, and inclusion.
This article examines the specific challenges faced by women in Africa concerning AI use and data protection. It explores the interplay between these aspects and their potential impacts, highlighting both the obstacles and opportunities they present. Taking a feminist perspective in AI in which AI technologies can be used to perpetuate sexism and gender discrimination, the article analyzes the implications of datafication on underrepresented groups, with a particular focus on women’s experiences. Strategies and approaches for addressing these challenges are explored, aiming to foster the development of inclusive and equitable AI systems. By critically analyzing and studying these subjects, this article contributes to the ongoing discourse on leveraging AI technologies for the benefit of all, while minimizing risks and inequality.
Analyzing the Impact of AI on Africa’s Disenfranchised Communities: Insights from Decolonial Theory and Critical Race Theory
Understanding the influence of AI technology on marginalized communities in Africa requires employing decolonial theory and critical race theory as useful frameworks. These theories provide a lens through which we can examine how technology perpetuates and reinforces power dynamics and privilege, as well as how it can exacerbate existing inequities. Decolonial theory highlights the enduring legacy of colonialism, which continues to shape and impact global power dynamics, particularly in the realm of AI technologies in Africa. Critical Race Theory (CRT), on the other hand, delves into the intersections between race, racism, and other forms of oppression such as class, gender, and sexuality, shedding light on how these intersecting forces contribute to and sustain systems of inequality. By drawing on these frameworks, we can better understand the complex dynamics and work towards addressing gender related disparities and injustices perpetuated by AI technologies in Africa. In Kenya, a biometric digital ID system is being used for a national identity registration programme which includes the collection of biometric information. However, the system has been criticized for being biased against the nubian community in Kenya.
As some of the most active countries in the African region that are leading in AI development, both Kenya and South Africa, for example, have enacted data protection laws in place. The Kenyan Data Protection Act (DPA) was enacted in 2019 while the South African Protection of Personal Information Act (POPIA) came into effect in 2020. Both inspired by the General Data Protection Regulation (GDPR), the DPA and POPIA have a number of provisions that are similar to the GDPR, such as the requirement for organizations to obtain consent from individuals before collecting and processing their personal data. In addition, Section 35(1) of the Kenyan DPA and Section 71(1) of the South African POPIA prohibits automated decision-making that affects legal consequences for data subjects. This means that organizations cannot use AI systems to make decisions that have a significant impact on an individual’s life, such as whether they are eligible for a loan or a job.
While other African countries are also making efforts to develop AI technologies. For example, Nigeria launched a national AI policy that aims to make Nigeria a leader in the development of AI technologies. However, the lack of clear and comprehensive regulations in the use of AI systems is a significant challenge amongst most African AI policy practices and strategies. Without clear regulations, it is difficult to ensure that AI technologies are used in a fair and equitable way.
The African Union Convention on Cyber Security and Personal Data Protection (ACCP) is a significant step forward for data protection in Africa. It provides strong protections for personal data and ensures that individuals have control over their personal data. However, the ACCP is set to come into force after Mauritania became the 15th state to submit its ratification.
Kenya and South Africa, despite being major economic players in the region, have not yet ratified the ACCP. This is a missed opportunity for these countries to take a leadership role in data protection in Africa. The ACCP is a valuable tool that can help to protect personal data in Africa. It is important for all countries in Africa to ratify the ACCP and to implement its provisions. This will help to ensure that individuals in Africa have control over their personal data and that it is used in a fair and responsible way.
Addressing Gender Considerations and Bias in Data Protection and AI Policies
Another concern is the dearth of gender considerations in existing data-protection frameworks along with AI policies and strategies. A notable example is that of a black woman entrepreneur who was denied a loan from a South African bank as a result of an AI-powered algorithm loan system. This case highlights the issue of racial and sexist bias in AI loan decisions. AI systems are often trained on data that is biased against certain groups of people. This can lead to AI systems that make discriminatory decisions.
Gender Disparity in African AI Landscape: Challenges and Opportunities
Many African countries fail to account for gender neutrality, leaving women and vulnerable groups at a disadvantage. Despite these challenges some organizations are making efforts to address gender neutrality in Africa. There is still much work to be done, but these initiatives are a step in the right direction. The World Bank launched an African Gender Innovation Lab, a platform for African women innovators to develop and test new technology solutions to address gender-related challenges. In addition, the African Union (AU) has adopted a number of policies and strategies to promote gender equality in the digital age. These policies include the AU’s Agenda 2063, which calls for the elimination of gender inequality in all spheres of life, including the digital economy and the AU’s Gender Policy Framework, which provides guidance on how to promote gender equality in the digital economy.
According to the eighth edition of the annual African Tech Startups Funding Report, in 2022, just 20% of funded African tech startups had a female founder. This underrepresentation of women in the technology industry further exacerbates the gender biases present in AI technologies and decision-making processes.
In the context of data protection and AI in Africa, there is a growing recognition of the need to address gender biases and ensure gender neutrality in policies and efforts. However, there are several inadequacies that need to be addressed to achieve this goal.
One crucial aspect is the legal framework surrounding data protection and AI. Many African countries have started implementing data protection laws or are in the process of developing them. These laws aim to safeguard individuals’ personal data and regulate its collection, storage, and use. However, there are often gaps in these laws when it comes to addressing gender-specific concerns. The laws may not explicitly consider the potential biases and discriminatory outcomes that can arise from AI systems that process personal data.
Furthermore, it is important to include diverse voices and thinking in the development and implementation of AI technologies. This includes ensuring that marginalized groups, including women and people of color, are represented in decision-making processes, and that their perspectives are taken into account when developing data protection laws, AI policies and regulations. For example, the African Union’s (AU) strategy for AI emphasizes the need for inclusive participation and collaboration across sectors and stakeholders in the development of AI. The strategy can improve stakeholder involvement, development of capacity, and the regulatory framework in AI technology and data governance. Such favorable policies can help African AI practitioners overcome existing and systemic deployment challenges.
Finally, it is important to invest in education and awareness campaigns to educate individuals and communities about their rights and the potential risks and benefits of AI technologies. This includes providing training and resources to marginalized groups, including women and people of color. In contrast, the decolonial theory, critical race theory, and feminist frameworks provide important lenses to examine the ways in which power and privilege are perpetuated and reinforced through technology and how these technologies can reproduce and exacerbate existing inequalities. However, it is these missing pieces of intersectionality that the research project seeks to understand and through this blogpost series we seek to highlight key issues and emerging challenges.