83 research outputs found
Efficient XAI Techniques: A Taxonomic Survey
Recently, there has been a growing demand for the deployment of Explainable
Artificial Intelligence (XAI) algorithms in real-world applications. However,
traditional XAI methods typically suffer from a high computational complexity
problem, which discourages the deployment of real-time systems to meet the
time-demanding requirements of real-world scenarios. Although many approaches
have been proposed to improve the efficiency of XAI methods, a comprehensive
understanding of the achievements and challenges is still needed. To this end,
in this paper we provide a review of efficient XAI. Specifically, we categorize
existing techniques of XAI acceleration into efficient non-amortized and
efficient amortized methods. The efficient non-amortized methods focus on
data-centric or model-centric acceleration upon each individual instance. In
contrast, amortized methods focus on learning a unified distribution of model
explanations, following the predictive, generative, or reinforcement
frameworks, to rapidly derive multiple model explanations. We also analyze the
limitations of an efficient XAI pipeline from the perspectives of the training
phase, the deployment phase, and the use scenarios. Finally, we summarize the
challenges of deploying XAI acceleration methods to real-world scenarios,
overcoming the trade-off between faithfulness and efficiency, and the selection
of different acceleration methods.Comment: 15 pages, 3 figure
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Interpretable machine learning seeks to understand the reasoning process of
complex black-box systems that are long notorious for lack of explainability.
One flourishing approach is through counterfactual explanations, which provide
suggestions on what a user can do to alter an outcome. Not only must a
counterfactual example counter the original prediction from the black-box
classifier but it should also satisfy various constraints for practical
applications. Diversity is one of the critical constraints that however remains
less discussed. While diverse counterfactuals are ideal, it is computationally
challenging to simultaneously address some other constraints. Furthermore,
there is a growing privacy concern over the released counterfactual data. To
this end, we propose a feature-based learning framework that effectively
handles the counterfactual constraints and contributes itself to the limited
pool of private explanation models. We demonstrate the flexibility and
effectiveness of our method in generating diverse counterfactuals of
actionability and plausibility. Our counterfactual engine is more efficient
than counterparts of the same capacity while yielding the lowest
re-identification risks
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
In this review, we examine the problem of designing interpretable and
explainable machine learning models. Interpretability and explainability lie at
the core of many machine learning and statistical applications in medicine,
economics, law, and natural sciences. Although interpretability and
explainability have escaped a clear universal definition, many techniques
motivated by these properties have been developed over the recent 30 years with
the focus currently shifting towards deep learning methods. In this review, we
emphasise the divide between interpretability and explainability and illustrate
these two different research directions with concrete examples of the
state-of-the-art. The review is intended for a general machine learning
audience with interest in exploring the problems of interpretation and
explanation beyond logistic regression or random forest variable importance.
This work is not an exhaustive literature survey, but rather a primer focusing
selectively on certain lines of research which the authors found interesting or
informative
Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks
Counterfactual (CF) explanations, also known as contrastive explanations and
algorithmic recourses, are popular for explaining machine learning models in
high-stakes domains. For a subject that receives a negative model prediction
(e.g., mortgage application denial), the CF explanations are similar instances
but with positive predictions, which informs the subject of ways to improve.
While their various properties have been studied, such as validity and
stability, we contribute a novel one: their behaviors under iterative partial
fulfillment (IPF). Specifically, upon receiving a CF explanation, the subject
may only partially fulfill it before requesting a new prediction with a new
explanation, and repeat until the prediction is positive. Such partial
fulfillment could be due to the subject's limited capability (e.g., can only
pay down two out of four credit card accounts at this moment) or an attempt to
take the chance (e.g., betting that a monthly salary increase of \$800 is
enough even though \$1,000 is recommended). Does such iterative partial
fulfillment increase or decrease the total cost of improvement incurred by the
subject? We mathematically formalize IPF and demonstrate, both theoretically
and empirically, that different CF algorithms exhibit vastly different
behaviors under IPF. We discuss implications of our observations, advocate for
this factor to be carefully considered in the development and study of CF
algorithms, and give several directions for future work.Comment: AIES 202
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
Counterfactual Explanations (CEs) have received increasing interest as a
major methodology for explaining neural network classifiers. Usually, CEs for
an input-output pair are defined as data points with minimum distance to the
input that are classified with a different label than the output. To tackle the
established problem that CEs are easily invalidated when model parameters are
updated (e.g. retrained), studies have proposed ways to certify the robustness
of CEs under model parameter changes bounded by a norm ball. However, existing
methods targeting this form of robustness are not sound or complete, and they
may generate implausible CEs, i.e., outliers wrt the training dataset. In fact,
no existing method simultaneously optimises for proximity and plausibility
while preserving robustness guarantees. In this work, we propose Provably
RObust and PLAusible Counterfactual Explanations (PROPLACE), a method
leveraging on robust optimisation techniques to address the aforementioned
limitations in the literature. We formulate an iterative algorithm to compute
provably robust CEs and prove its convergence, soundness and completeness.
Through a comparative experiment involving six baselines, five of which target
robustness, we show that PROPLACE achieves state-of-the-art performances
against metrics on three evaluation aspects.Comment: Accepted at ACML 2023, camera-ready versio
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations
This work presents CounterNet, a novel end-to-end learning framework which
integrates Machine Learning (ML) model training and the generation of
corresponding counterfactual (CF) explanations into a single end-to-end
pipeline. Counterfactual explanations offer a contrastive case, i.e., they
attempt to find the smallest modification to the feature values of an instance
that changes the prediction of the ML model on that instance to a predefined
output. Prior techniques for generating CF explanations suffer from two major
limitations: (i) all of them are post-hoc methods designed for use with
proprietary ML models -- as a result, their procedure for generating CF
explanations is uninformed by the training of the ML model, which leads to
misalignment between model predictions and explanations; and (ii) most of them
rely on solving separate time-intensive optimization problems to find CF
explanations for each input data point (which negatively impacts their
runtime). This work makes a novel departure from the prevalent post-hoc
paradigm (of generating CF explanations) by presenting CounterNet, an
end-to-end learning framework which integrates predictive model training and
the generation of counterfactual (CF) explanations into a single pipeline.
Unlike post-hoc methods, CounterNet enables the optimization of the CF
explanation generation only once together with the predictive model. We adopt a
block-wise coordinate descent procedure which helps in effectively training
CounterNet's network. Our extensive experiments on multiple real-world datasets
show that CounterNet generates high-quality predictions, and consistently
achieves 100% CF validity and low proximity scores (thereby achieving a
well-balanced cost-invalidity trade-off) for any new input instance, and runs
3X faster than existing state-of-the-art baselines
Principled Diverse Counterfactuals in Multilinear Models
Machine learning (ML) applications have automated numerous real-life tasks,improving both private and public life. However, the black-box nature of manystate-of-the-art models poses the challenge of model verification; how can onebe sure that the algorithm bases its decisions on the proper criteria, or that itdoes not discriminate against certain minority groups? In this paper we proposea way to generate diverse counterfactual explanations from multilinear models,a broad class which includes Random Forests, as well as Bayesian Networks.<br/
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities
Recent advancements in AI applications to healthcare have shown incredible
promise in surpassing human performance in diagnosis and disease prognosis.
With the increasing complexity of AI models, however, concerns regarding their
opacity, potential biases, and the need for interpretability. To ensure trust
and reliability in AI systems, especially in clinical risk prediction models,
explainability becomes crucial. Explainability is usually referred to as an AI
system's ability to provide a robust interpretation of its decision-making
logic or the decisions themselves to human stakeholders. In clinical risk
prediction, other aspects of explainability like fairness, bias, trust, and
transparency also represent important concepts beyond just interpretability. In
this review, we address the relationship between these concepts as they are
often used together or interchangeably. This review also discusses recent
progress in developing explainable models for clinical risk prediction,
highlighting the importance of quantitative and clinical evaluation and
validation across multiple common modalities in clinical practice. It
emphasizes the need for external validation and the combination of diverse
interpretability methods to enhance trust and fairness. Adopting rigorous
testing, such as using synthetic datasets with known generative factors, can
further improve the reliability of explainability methods. Open access and
code-sharing resources are essential for transparency and reproducibility,
enabling the growth and trustworthiness of explainable research. While
challenges exist, an end-to-end approach to explainability in clinical risk
prediction, incorporating stakeholders from clinicians to developers, is
essential for success
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
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