404 research outputs found
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
Compositionality as an Analogical Process: Introducing ANNE
Usage-based constructionist approaches consider language a structured inventory of constructions, form-meaning pairings of different schematicity and complexity, and claim that the more a linguistic pattern is encountered, the more it becomes accessible to speakers. However, when an expression is unavailable, what processes underlie the interpretation? While traditional answers rely on the principle of compositionality, for which the meaning is built word-by-word and incrementally, usage-based theories argue that novel utterances are created based on previously experienced ones through analogy, mapping an existing structural pattern onto a novel instance. Starting from this theoretical perspective, we propose here a computational implementation of these assumptions. As the principle of compositionality has been used to generate distributional representations of phrases, we propose a neural network simulating the construction of phrasal embedding as an analogical process. Our framework, inspired by word2vec and computer vision techniques, was evaluated on tasks of generalization from existing vectors
TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring
Continuous monitoring of trained ML models to determine when their
predictions should and should not be trusted is essential for their safe
deployment. Such a framework ought to be high-performing, explainable, post-hoc
and actionable. We propose TRUST-LAPSE, a "mistrust" scoring framework for
continuous model monitoring. We assess the trustworthiness of each input
sample's model prediction using a sequence of latent-space embeddings.
Specifically, (a) our latent-space mistrust score estimates mistrust using
distance metrics (Mahalanobis distance) and similarity metrics (cosine
similarity) in the latent-space and (b) our sequential mistrust score
determines deviations in correlations over the sequence of past input
representations in a non-parametric, sliding-window based algorithm for
actionable continuous monitoring. We evaluate TRUST-LAPSE via two downstream
tasks: (1) distributionally shifted input detection, and (2) data drift
detection. We evaluate across diverse domains - audio and vision using public
datasets and further benchmark our approach on challenging, real-world
electroencephalograms (EEG) datasets for seizure detection. Our latent-space
mistrust scores achieve state-of-the-art results with AUROCs of 84.1 (vision),
73.9 (audio), and 77.1 (clinical EEGs), outperforming baselines by over 10
points. We expose critical failures in popular baselines that remain
insensitive to input semantic content, rendering them unfit for real-world
model monitoring. We show that our sequential mistrust scores achieve high
drift detection rates; over 90% of the streams show < 20% error for all
domains. Through extensive qualitative and quantitative evaluations, we show
that our mistrust scores are more robust and provide explainability for easy
adoption into practice.Comment: Keywords: Mistrust Scores, Latent-Space, Model monitoring,
Trustworthy AI, Explainable AI, Semantic-guided A
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research
When users perceive AI systems as mindful, independent agents, they hold them
responsible instead of the AI experts who created and designed these systems.
So far, it has not been studied whether explanations support this shift in
responsibility through the use of mind-attributing verbs like "to think". To
better understand the prevalence of mind-attributing explanations we analyse AI
explanations in 3,533 explainable AI (XAI) research articles from the Semantic
Scholar Open Research Corpus (S2ORC). Using methods from semantic shift
detection, we identify three dominant types of mind attribution: (1)
metaphorical (e.g. "to learn" or "to predict"), (2) awareness (e.g. "to
consider"), and (3) agency (e.g. "to make decisions"). We then analyse the
impact of mind-attributing explanations on awareness and responsibility in a
vignette-based experiment with 199 participants. We find that participants who
were given a mind-attributing explanation were more likely to rate the AI
system as aware of the harm it caused. Moreover, the mind-attributing
explanation had a responsibility-concealing effect: Considering the AI experts'
involvement lead to reduced ratings of AI responsibility for participants who
were given a non-mind-attributing or no explanation. In contrast, participants
who read the mind-attributing explanation still held the AI system responsible
despite considering the AI experts' involvement. Taken together, our work
underlines the need to carefully phrase explanations about AI systems in
scientific writing to reduce mind attribution and clearly communicate human
responsibility.Comment: 21 pages, 6 figures, to be published in PACM HCI (CSCW '24
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