Data Feminism for AI (2405.01286v1)

Abstract: This paper presents a set of intersectional feminist principles for conducting equitable, ethical, and sustainable AI research. In Data Feminism (2020), we offered seven principles for examining and challenging unequal power in data science. Here, we present a rationale for why feminism remains deeply relevant for AI research, rearticulate the original principles of data feminism with respect to AI, and introduce two potential new principles related to environmental impact and consent. Together, these principles help to 1) account for the unequal, undemocratic, extractive, and exclusionary forces at work in AI research, development, and deployment; 2) identify and mitigate predictable harms in advance of unsafe, discriminatory, or otherwise oppressive systems being released into the world; and 3) inspire creative, joyful, and collective ways to work towards a more equitable, sustainable world in which all of us can thrive.

• The paper presents an intersectional feminist framework for AI that challenges corporate influence, biases, and environmental injustices.
• It rearticulates data feminism principles for AI by emphasizing power analysis, inclusive design, and the visibility of hidden labor.
• The framework advocates systemic reforms in AI research through interdisciplinary collaboration to ensure consent, accountability, and equity.

Data Feminism for AI: Principles and Implications

The paper "Data Feminism for AI," authored by Lauren Klein and Catherine D'Ignazio, proposes a set of principles for incorporating feminist thought into AI research and practice. Building upon their previous work in "Data Feminism" (2020), Klein and D'Ignazio present an intersectional feminist framework aimed at mitigating the unequal, undemocratic, extractive, and exclusionary dynamics prevalent in AI research, development, and deployment. This paper seeks to adapt the original seven principles of data feminism to the AI context and suggests two additional principles addressing environmental impact and consent.

Overview of Data Feminism Principles in AI

The authors begin by rearticulating the original principles of data feminism for AI research:

1. Examine Power: An analysis of power structures is critical in AI, where economic and political hierarchies, notably those perpetuated by corporate interests, greatly influence research agendas and outcomes. This principle highlights the need to scrutinize how capitalist and colonialist dynamics perpetuate inequalities within AI systems.
2. Challenge Power: AI development should aim to counter existing power imbalances. This involves building alternative, justice-oriented AI systems and engaging in collective action to resist corporate capture of AI technology.
3. Rethink Binaries and Hierarchies: AI often reinforces rigid binaries, such as the gender binary. This principle calls for challenging these constructs and creating inclusive AI systems that do not depend on reductive classifications.
4. Elevate Emotion and Embodiment: The authors emphasize the importance of integrating emotional and embodied knowledge into AI research, arguing against the dismissal of these forms of knowledge as irrational.
5. Embrace Pluralism: Incorporating diverse perspectives, particularly those of marginalized communities, into AI research is vital for producing inclusive and representative technologies.
6. Consider Context: AI systems should account for the socio-political contexts in which they are developed and deployed. The authors call for practices that foreground the origins, intentions, and potential biases of datasets used in AI.
7. Make Labor Visible: The often-invisible labor undergirding AI systems, particularly that performed by marginalized communities, must be acknowledged and valued appropriately.

New Directions: Environmental Impact and Consent

In addition to recontextualizing these principles, the paper introduces two emerging principles:

• Environmental Impact: AI research and deployment often lead to uneven environmental burdens, predominantly affecting marginalized communities in the Global South. This principle draws from ecofeminism and Indigenous feminist thought to address these imbalances.
• Consent: AI systems frequently operate without the explicit consent of individuals whose data they exploit. This principle suggests rethinking traditional notions of consent to include more collective and interdependent frameworks.

Implications and Future Directions

Klein and D'Ignazio's paper suggests a significant shift in AI research, proposing that a feminist lens can provide valuable insights into addressing entrenched power imbalances, improving accountability, and fostering greater inclusivity and sustainability in technology development. Moving forward, AI researchers are urged to incorporate these principles into their methodologies and frameworks. This involves not only critical reflection but also proactive engagement with affected communities to ensure equitable and ethical AI practices.

The implications of this work reach beyond academic discourse, calling for systemic changes in how AI and related technologies are conceptualized, developed, and deployed. Strategies include fostering interdisciplinary collaborations, advocating for policy reforms, and supporting organizational practices that align with these feminist principles.

In conclusion, "Data Feminism for AI" serves as a critical addition to AI research, encouraging scholars and practitioners to rethink the ethical dimensions of technology development through a feminist lens. As AI continues to evolve, applying these principles may pave the way for more equitable, ethical, and sustainable technological futures.