Semanlink - Accountable AI

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Artificial Intelligence

Accountable AI

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Explainable AI
- AI black box
  - Blackbox NLP
    - BlackboxNLP (2018 workshop)
  - LIME
- Concept Bottleneck Models
- Explainable NLP
  - Blackbox NLP
  - LIME
    [2301.04709] Causal Abstraction for Faithful Model Interpretation
    Tags:
    
    Arxiv Doc
    Confiance AI
    Explainable AI
    Explainable NLP
    LIME
    NLP@Stanford
    > A faithful and interpretable explanation of an AI model's behavior and internal structure is a **high-level explanation that is human-intelligible but also consistent with the known, but often opaque low-level causal details of the model**. We argue that the theory of **causal abstraction** provides the mathematical foundations for the desired kinds of model explanations > We take the fundamental question in explainable artificial intelligence (XAI) to be why a deep learning model makes the predictions it does. > XAI needs a theory for when a high-level causal explanation [that is, interpretable by humans] is harmonious with a low-level causal explanation. > A high-level (possibly symbolic) model is a faithful proxy for a low-lever (in our setting, usually neural) model when we can align high-level variables with sets of low-level variables that play the same causal role
    
    2023-01-14 About
    [2207.05221] Language Models (Mostly) Know What They Know
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    Arxiv Doc
    Catherine Olsson
    Chris Olah
    Explainable NLP
    Language Model
    > we show that language models can evaluate whether what they say is true, and predict ahead of time whether they'll be able to answer questions correctly.
    
    2022-09-15 About
    [2010.00711] A Survey of the State of Explainable AI for Natural Language Processing
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    Arxiv Doc
    Confiance AI
    Explainable NLP
    NLP@IBM
    Survey / Review
    2022-09-08 About
    Rajiv Shah sur Twitter : "How to explain predictions from 🤗 transformer models?..."
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    Explainable NLP
    Transformers
    Tweet
    2022-08-18 About
    [1905.11852] EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction
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    Arxiv Doc
    Explainable NLP
    Ludovic Denoyer
    NLP@Facebook
    > Providing explanations along with predictions is crucial in some text processing tasks. Therefore, we propose a new self-interpretable model that performs output prediction and simultaneously provides an explanation in terms of the presence of particular concepts in the input. To do so, our model's prediction relies solely on a low-dimensional binary representation of the input, where each feature denotes the presence or absence of concepts. Presented in these [slides](/doc/2019/12/unsupervised_learning_with_text)
    
    2019-12-05 About
Uncertainty in Deep Learning
- Bayesian Deep Learning

0 Document (Long List)

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sl:creationDate : 2018-11-05
sl:creationTime : 2018-11-05T09:40:12Z
rdf:type : sl:Tag
skos:prefLabel : Accountable AI@en