Social and Ethical Issues in Information Technology - material and project
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Updated
Sep 6, 2020
Social and Ethical Issues in Information Technology - material and project
Fairness in data, and machine learning algorithms is critical to building safe and responsible AI systems from the ground up by design. Both technical and business AI stakeholders are in constant pursuit of fairness to ensure they meaningfully address problems like AI bias. While accuracy is one metric for evaluating the accuracy of a machine le…
I spend over ten years writing code and applying math and science. in each keystroke I found joy. I see life is a system that has variable entropy (E). Every process (p[i]) generates dE and my job is to understand what dE(p[i]) means.
Stash of some of the most potent research papers, blogs and videos on AI which I liked.
Debiasing methods on contextualised embeddings are ineffective - CS475
We aim at developing a framework to promote the integration of CDR principles in the legal sector combining the LLI digital ethics code with a practical ethical legal innovation roadmap.
List of references about Machine Learning bias and ethics
Artificial Intelligence Attitude and Literacy Framework
Replication Code for: AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security
Replication Code for: On the Mechanics of NFT Valuation: AI Ethics and Social Media
Reviews of papers related to robot learning, computer vision, audio , NLP and AI Ethics
Comparing AI policies and strategies.
This repository provides comprehensive guidelines, frameworks, and sample policies for the ethical and effective integration of AI in progressive organizations. It serves as a platform for discussion and collaboration on AI governance and ethics.
Guidelines and frameworks for ethical AI integration and management in progressive organizations.
My notes and notebooks for Kaggle learning courses.
Courses on Kaggle
The findings of this research reveal several intriguing disparities between human and AI text generation. I demonstrated that these differences could be successfully utilized by classifiers to distinguish between human and AI-generated text.
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