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Welcome to the Responsible AI Paper Summaries repository! Here, you'll find concise summaries of key papers in various areas of responsible AI.
This repository provides brief summaries of AI/ML papers in the following areas:
- Explainability and Interpretability
- Fairness and Biases
- Privacy
- Security
- Safety
- Accountability
- Human Control and Interaction
- Legal and Ethical Guidelines
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The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions - arXiv, 2024. This paper from OpenAI introduces an instruction hierarchy to train LLMs to prioritize privileged instructions(system messages) over lower-level ones (user messages and third-party inputs), enhancing their robustness against adversaries.
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Taxonomy of Risks Posed by Language Models - FAccT ’22. This paper develops a comprehensive taxonomy of ethical and social risks associated with large-scale language models (LMs). It identifies twenty-one risks and categorizes them into six risk areas to guide responsible innovation and mitigation strategies.
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Why Should I Trust You? Explaining the Predictions of Any Classifier - KDD 2016. This paper introduces LIME (Local Interpretable Model-agnostic Explanations), a technique to explain the predictions of any classifier in a faithful and interpretable manner by learning an interpretable model locally around the prediction.
Explainability and Interpretability
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Why Should I Trust You? Explaining the Predictions of Any Classifier - KDD 2016. This paper introduces LIME (Local Interpretable Model-agnostic Explanations), a technique to explain the predictions of any classifier in a faithful and interpretable manner by learning an interpretable model locally around the prediction.
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A Nutritional Label for Rankings - SIGMOD’18. Provides a web-based application called Ranking Facts that generates a "nutritional label" for rankings to enhance transparency, fairness, and stability.
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A Unified Approach to Interpreting Model Predictions - NIPS 2017. Introduces SHAP (SHapley Additive exPlanations), a unified framework for interpreting model predictions by assigning each feature an importance value for a particular prediction, integrating six existing methods into a single, cohesive approach.
Fairness and Biases
- Taxonomy of Risks Posed by Language Models - FAccT ’22. This paper develops a comprehensive taxonomy of ethical and social risks associated with large-scale language models (LMs). It identifies twenty-one risks and categorizes them into six risk areas to guide responsible innovation and mitigation strategies.
Privacy
Security
Safety
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The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions - arXiv, 2024. This paper from OpenAI introduces an instruction hierarchy to train LLMs to prioritize privileged instructions(system messages) over lower-level ones (user messages and third-party inputs), enhancing their robustness against adversaries.
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Taxonomy of Risks Posed by Language Models - FAccT ’22. This paper develops a comprehensive taxonomy of ethical and social risks associated with large-scale language models (LMs). It identifies twenty-one risks and categorizes them into six risk areas to guide responsible innovation and mitigation strategies.
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To Believe or Not to Believe Your LLM - arXiv, 2024. This paper explores uncertainty quantification in LLMs to detect hallucinations by distinguishing epistemic from aleatoric uncertainties using an information-theoretic metric.
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CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models - arXiv, 2024. CARES evaluates the trustworthiness of medical vision language models (Med-LVLMs) across five dimensions: trustfulness, fairness, safety, privacy, and robustness.
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Air Gap: Protecting Privacy-Conscious Conversational Agents - arXiv 2024. A paper from Google proposes AirGapAgent to prevent data leakage from LLMs, ensuring privacy in adversarial contexts.
Accountability
Human Control and Interaction
- Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies - FAccT '23. This survey reviews over 100 empirical studies to understand and improve human-AI decision-making, emphasizing the need for unified research frameworks.
Legal and Ethical Guidelines
Each summary is stored in the relevant subfolder within the summaries/ directory. You can browse through the summaries to quickly understand the main points of various papers.
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