mlsanityData to 7 Oct 2026

FATE in AI: Towards Algorithmic Inclusivity and Accessibility

arXiv 2301.01590 ↗First posted 3 Jan 2023cs.CY cs.AI cs.HCcited by 3

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Abstract (arXiv)

Artificial Intelligence (AI) is at the forefront of modern technology, and its effects are felt in many areas of society. To prevent algorithmic disparities, fairness, accountability, transparency, and ethics (FATE) in AI are being implemented. However, the current discourse on these issues is largely dominated by more economically developed countries (MEDC), leaving out local knowledge, cultural pluralism, and global fairness. This study aims to address this gap by examining FATE-related desiderata, particularly transparency and ethics, in areas of the global South that are underserved by AI. A user study (n=43) and a participatory session (n=30) were conducted to achieve this goal. The results showed that AI models can encode bias and amplify stereotypes. To promote inclusivity, a community-led strategy is proposed to collect and curate representative data for responsible AI design. This will enable the affected community or individuals to monitor the increasing use of AI-powered systems. Additionally, recommendations based on public input are provided to ensure that AI adheres to social values and context-specific FATE needs.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 99 references that are in the map. First posted on arXiv 3 Jan 2023.

Builds on

22 of 99 references.

References · 22

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10 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
  2. 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotationcited by 1
  3. 2026Culturally-Grounded Governance for Multilingual Language Models: Rights, Data Boundaries, and Accountable AI Designcited by 0