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Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2007.04068v1)

Published 8 Jul 2020 in cs.CY, cs.AI, cs.LG, and stat.ML

Abstract: This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. AI is viewed as amongst the technological advances that will reshape modern societies and their relations. Whilst the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us; ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all.

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Authors (3)
  1. Shakir Mohamed (42 papers)
  2. Marie-Therese Png (2 papers)
  3. William Isaac (18 papers)
Citations (356)

Summary

Analyzing "Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence"

The paper "Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence" by Shakir Mohamed, Marie-Therese Png, and William Isaac presents a nuanced perspective on the intersection of decolonial theory and AI. The authors advocate for embedding decolonial critiques within AI development to better align technological evolution with ethical norms and mitigate sociopolitical harms.

The treatise explores AI as both an object and subject, emphasizing its role as both a technological artefact and as a system deeply enmeshed in socio-political networks. It underlines that while AI holds potential for positive societal transformation, inherent risks, particularly for marginalized communities, necessitate careful scrutiny. The critical assessment leverages historical colonial patterns to elucidate contemporary power asymmetries manifested in AI systems.

Decolonial Theory's Contribution

Central to the discussion is decolonial theory, which the authors utilize to provide insights into the persistent colonial dynamics influencing present-day AI systems. By conceptualizing coloniality as a lasting structure of domination, the paper argues for an AI development approach that is conscious of historical injustices and power disparities. This perspective fosters a methodology that is preemptive and retrospective, one that not only anticipates technological impacts but re-evaluates them through the lens of historical exploitation and oppression.

Key Concepts and Tactics

The authors highlight several concepts emerging from the misalignment of AI’s development with equitable practices:

  1. Algorithmic Oppression and Exploitation: AI systems have been shown to replicate systemic biases and injustices. Instances include racial biases in predictive policing and discrimination in facial recognition technologies. These examples illustrate the perpetuation of historical injustices in new digital forms.
  2. Algorithmic Dispossession: This is evident in the global AI governance mechanisms, where dominant economies dictate ethical guidelines and standards, often ignoring the perspectives and necessities of less powerful nations.
  3. Decolonial Tactics for AI:
    • Critical Technical Practice: Encourages a reflective AI development culture that actively questions its assumptions and societal implications.
    • Reciprocal Engagements: Proposes reverse tutelage, where knowledge exchange is bidirectional, enabling marginalized voices to offer insights into AI’s deployment.
    • Renewing Political Communities: Promotes inclusive and cross-cultural dialogues that engage communities at all levels in the AI development process.

Implications for AI Development

The decolonial approach presented in the paper calls into question the epistemic foundations of AI and underscores the necessity for a more pluralistic and equitable knowledge framework. This has profound implications—not only ethical but technical—inviting a reevaluation of fundamental AI paradigms, from data epistemologies to safety and fairness metrics. It challenges AI researchers to transcend utilitarian frameworks and consider broader communal impacts within a historical context.

Speculative Future Directions

As AI continues to evolve, the insights provided by decolonial theory might inject essential reflexivity into the field, fostering more just global AI practices. Future research could address how these decolonial tactics could concretely alter AI architectures and algorithms. Furthermore, the development of AI within multicultural contexts and its governance will likely benefit from such an encompassing perspective, potentially leading towards a more balanced global distribution of AI’s socio-economic benefits.

In conclusion, Mohamed, Png, and Isaac’s work contributes a critical framework that underscores the importance of understanding AI within broader socio-historical power dynamics. The application of decolonial theory in AI development demands attention not only to technical acumen but to the socio-political structures that shape and are shaped by technological practices. This offers a pathway to nurturing technologies that not only innovate but also embody principles of justice and equity.