A Framework for Democratizing AI
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Abstract (arXiv)
Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, are yet to be comprehended. These technologies are very specialized, and few organizations and select highly trained professionals have the wherewithal, in terms of money, manpower, and might, to chart the future. However, concentration of power can lead to marginalization, causing severe inequalities. Regulatory agencies and governments across the globe are creating national policies, and laws around these technologies to protect the rights of the digital citizens, as well as to empower them. Even private, not-for-profit organizations are also contributing to democratizing the technologies by making them accessible and affordable. However, accessibility and affordability are all but a few of the facets of democratizing the field. Others include, but not limited to, portability, explainability, credibility, fairness, among others. As one can imagine, democratizing AI is a multi-faceted problem, and it requires advancements in science, technology and policy. At textttmlsquare, we are developing scientific tools in this space. Specifically, we introduce an opinionated, extensible, textttPython framework that provides a single point of interface to a variety of solutions in each of the categories mentioned above. We present the design details, APIs of the framework, reference implementations, road map for development, and guidelines for contributions.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1.
4 papers.
- 2022Compute-Efficient Deep Learning: Algorithmic Trends and Opportunitiescited by 38
- 2021Estimation and Applications of Quantiles in Deep Binary Classificationcited by 3
- 2024Understanding "Democratization" in NLP and ML Researchcited by 3
- 2023Deep Learning and Ethicscited by 0
- 2024Understanding "Democratization" in NLP and ML Research17 Jun 2024
- 2023Deep Learning and Ethics24 May 2023
- 2022Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities13 Oct 2022
- 2021Estimation and Applications of Quantiles in Deep Binary Classification9 Feb 2021