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Technologies like artificial intelligence are shaped by our societies, norms and cultures in ways that are both obvious and difficult to measure.

This maxim represents particular danger to women and girls of the Global South, historically marginalized and systematically excluded from technology wielding, decision-making, or multiple opportunities to participate in creating their own unique solutions.

Why is AI biased? Is it designed to be biased or is it an unintentional flaw in the system?

AI can serve to challenge social inequality and dismantle structures of power.

This paper attempts to understand the system of technical requirements, epistemologies, institutions, organizational practices and cultural norms that shape human decision making within AI research.

Many of the techniques used by Artificial Intelligence systems - from official statistics to health records to online metadata to sensors and satellites - rely on training via vast quantities of data.

This is a review article that examines recommendations and examples of how to implement the principles of data feminism introduced earlier in this chapter by D’Ignazio and Klein.

Data is not the solution to the problem but without the right data, without giving visibility to the “missing data”, it is impossible to understand and to think about solutions.

This chapter explores the ethics, definitions, and metrics of conversations on fairness, and explores how even when models take these into account, how they are deployed and used in the real world matters just as much.

Many efforts have been made to try to ‘debias’ the data and create a ‘fair’ model. But what is fairness? And how can this translate to machine learning?

Algorithms are a set of instructions that determine how programs read, collect, process, and analyze data to generate an output. Intentionally or unintentionally, bias can be introduced at any stage of this process.

In this chapter, we explore potential directions to advance scholarship on debiasing AI, and more importantly, applying feminist approaches and methodologies to how AI systems are designed, created, deployed, and governed.

The talent and innovation of the global South will contribute to the world's technological development, and in particular to that of artificial intelligence.

A participatory, speculative design project that uses a card deck embedded with transfeminist values to help us collectively envision and share ideas for transfeminist technologies from the future.

Inclusion lessons from the indigenous data movement

The African continent has seen the growth of a number of high-impact innovations across several domains that are leveraging emerging technologies to solve pressing problems, and Artificial Intelligence (AI) is a major driver for these innovations.

Regulations for public procurement of AI systems and new technology for managing and automating public procurement are unfolding together, guided by the need to make public services more efficient, competitive, transparent and accountable.

Public funding of AI is often just as opaque as its algorithms. Companies influence what tech gets built and deployed in the innovation economy and the policies that support them.

We must bring back the vision of AI as something collective and complex that will help us deal with the big problems that affect all of us.

What’s next for Feminist approaches to AI? We explore practical next steps for key actors in ensuring that AI doesn’t embed bias, and can instead be positioned to overcome bias and help to realize a bountiful, equitable future.

Help us make these practical and urgent recommendations for inclusive algorithms a reality.

This publication represents the work of the entire f<a+i>r network, and ongoing work in Feminist AI. We are grateful to the following people for their contributions.