Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence
arXiv:2007.04068 · doi:10.1007/s13347-020-00405-8
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. 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.
arXiv comment28 Pages. Accepted, to appear in: Philosophy and Technology (405), Springer. Submitted 16 January, Accepted 26 May 2020
References in corpus (6)
- Artificial Intelligence: the global landscape of ethics guidelines
- The role of artificial intelligence in achieving the Sustainable Development Goals
- Model Cards for Model Reporting
- Artificial Intelligence, Values and Alignment
- Towards a Critical Race Methodology in Algorithmic Fairness
- A Causal Bayesian Networks Viewpoint on Fairness
Cited by in corpus (45)
- Artificial Intelligence, Values and Alignment
- Ethical Machine Learning in Health Care
- Expanding Explainability: Towards Social Transparency in AI systems
- Power to the People? Opportunities and Challenges for Participatory AI
- Predictability and Surprise in Large Generative Models
- The Forgotten Margins of AI Ethics
- Envisioning Communities: A Participatory Approach Towards AI for Social Good
- Conservative AI and social inequality: Conceptualizing alternatives to bias through social theory
- AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia
- Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI
- Towards decolonising computational sciences
- AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances
- Data and its (dis)contents: A survey of dataset development and use in machine learning research
- `It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values
- Data Governance in the Age of Large-Scale Data-Driven Language Technology
- Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets
- Three lines of defense against risks from AI
- Bridging adaptive management and reinforcement learning for more robust decisions
- Decolonial AI as Disenclosure
- The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa
- Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
- Reflexive Design for Fairness and Other Human Values in Formal Models
- From Fitting Participation to Forging Relationships: The Art of Participatory ML
- Subverting machines, fluctuating identities: Re-learning human categorization
- A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure
- Non-portability of Algorithmic Fairness in India
- Creative Writers' Attitudes on Writing as Training Data for Large Language Models
- Foundations for the Future: Institution building for the purpose of Artificial Intelligence governance
- Generative Art Using Neural Visual Grammars and Dual Encoders
- The Centers and Margins of Modeling Humans in Well-being Technologies: A Decentering Approach
- Artificial Intelligence Ethics: An Inclusive Global Discourse?
- Beyond "Fairness:" Structural (In)justice Lenses on AI for Education
- Nteasee: Understanding Needs in AI for Health in Africa -- A Mixed-Methods Study of Expert and General Population Perspectives
- Good AI for the Present of Humanity Democratizing AI Governance
- Experimental Standards for Deep Learning in Natural Language Processing Research
- The Arc of the Data Scientific Universe
- Anticipatory Ethics and the Role of Uncertainty
- "Business on WhatsApp is tough now -- but am I really a businesswoman?" Exploring Challenges with Adapting to Changes in WhatsApp Business
- We Haven't Gone Paperless Yet: Why the Printing Press Can Help Us Understand Data and AI
- Beyond traditional assumptions in fair machine learning
- Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach
- Insiders and Outsiders in Research on Machine Learning and Society
- Towards an Abolitionist AI: the role of Historically Black Colleges and Universities
- A Sociotechnical View of Algorithmic Fairness
- AI in the "Real World": Examining the Impact of AI Deployment in Low-Resource Contexts