Need of AI in Modern Education: in the Eyes of Explainable AI (xAI)
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Abstract (arXiv)
Modern Education is not textitModern without AI. However, AI's complex nature makes understanding and fixing problems challenging. Research worldwide shows that a parent's income greatly influences a child's education. This led us to explore how AI, especially complex models, makes important decisions using Explainable AI tools. Our research uncovered many complexities linked to parental income and offered reasonable explanations for these decisions. However, we also found biases in AI that go against what we want from AI in education: clear transparency and equal access for everyone. These biases can impact families and children's schooling, highlighting the need for better AI solutions that offer fair opportunities to all. This chapter tries to shed light on the complex ways AI operates, especially concerning biases. These are the foundational steps towards better educational policies, which include using AI in ways that are more reliable, accountable, and beneficial for everyone involved.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 48 references that are in the map. First posted on arXiv 31 Jul 2024.
Builds on
13 of 48 references.
References · 13
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2016Concrete Problems in AI Safetycited by 1,582
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
- 2023Power Hungry Processing: Watts Driving the Cost of AI Deployment?cited by 186
- 2022Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanationscited by 77
- 2021How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanationscited by 58
- 2020Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directionscited by 17
- 2023Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)cited by 10
1 more
- 2022Tools and Practices for Responsible AI Engineeringcited by 9
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Citing papers per year: 2025: 1.
1 paper.
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