9 research outputs found
Perfectly Parallel Fairness Certification of Neural Networks
Recently, there is growing concern that machine-learning models, which currently assist or even automate decision making, reproduce, and in the worst case reinforce, bias of the training data. The development of tools and techniques for certifying fairness of these models or describing their biased behavior is, therefore, critical. In this paper, we propose a perfectly parallel static analysis for certifying causal fairness of feed-forward neural networks used for classification tasks. When certification succeeds, our approach provides definite guarantees, otherwise, it describes and quantifies the biased behavior. We design the analysis to be sound, in practice also exact, and configurable in terms of scalability and precision, thereby enabling pay-as-you-go certification. We implement our approach in an open-source tool and demonstrate its effectiveness on models trained with popular datasets
Perfectly Parallel Fairness Certification of Neural Networks
International audienceRecently, there is growing concern that machine-learned software, which currently assists or even automates decision making, reproduces, and in the worst case reinforces, bias present in the training data. The development of tools and techniques for certifying fairness of this software or describing its biases is, therefore, critical. In this paper, we propose a perfectly parallel static analysis for certifying fairness of feed-forward neural networks used for classification of tabular data. When certification succeeds, our approach provides definite guarantees, otherwise, it describes and quantifies the biased input space regions. We design the analysis to be sound, in practice also exact, and configurable in terms of scalability and precision, thereby enabling pay-as-you-go certification. We implement our approach in an open-source tool called libra and demonstrate its effectiveness on neural networks trained on popular datasets
Learning Certified Individually Fair Representations
Fair representation learning provides an effective way of enforcing fairness
constraints without compromising utility for downstream users. A desirable
family of such fairness constraints, each requiring similar treatment for
similar individuals, is known as individual fairness. In this work, we
introduce the first method that enables data consumers to obtain certificates
of individual fairness for existing and new data points. The key idea is to map
similar individuals to close latent representations and leverage this latent
proximity to certify individual fairness. That is, our method enables the data
producer to learn and certify a representation where for a data point all
similar individuals are at -distance at most , thus
allowing data consumers to certify individual fairness by proving
-robustness of their classifier. Our experimental evaluation on five
real-world datasets and several fairness constraints demonstrates the
expressivity and scalability of our approach.Comment: Conference Paper at NeurIPS 202
Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment
Deep learning models are trained with certain assumptions about the data
during the development stage and then used for prediction in the deployment
stage. It is important to reason about the trustworthiness of the model's
predictions with unseen data during deployment. Existing methods for specifying
and verifying traditional software are insufficient for this task, as they
cannot handle the complexity of DNN model architecture and expected outcomes.
In this work, we propose a novel technique that uses rules derived from neural
network computations to infer data preconditions for a DNN model to determine
the trustworthiness of its predictions. Our approach, DeepInfer involves
introducing a novel abstraction for a trained DNN model that enables weakest
precondition reasoning using Dijkstra's Predicate Transformer Semantics. By
deriving rules over the inductive type of neural network abstract
representation, we can overcome the matrix dimensionality issues that arise
from the backward non-linear computation from the output layer to the input
layer. We utilize the weakest precondition computation using rules of each kind
of activation function to compute layer-wise precondition from the given
postcondition on the final output of a deep neural network. We extensively
evaluated DeepInfer on 29 real-world DNN models using four different datasets
collected from five different sources and demonstrated the utility,
effectiveness, and performance improvement over closely related work. DeepInfer
efficiently detects correct and incorrect predictions of high-accuracy models
with high recall (0.98) and high F-1 score (0.84) and has significantly
improved over prior technique, SelfChecker. The average runtime overhead of
DeepInfer is low, 0.22 sec for all unseen datasets. We also compared runtime
overhead using the same hardware settings and found that DeepInfer is 3.27
times faster than SelfChecker.Comment: Accepted for publication at the 46th International Conference on
Software Engineering (ICSE 2024
A Review of Formal Methods applied to Machine Learning
We review state-of-the-art formal methods applied to the emerging field of
the verification of machine learning systems. Formal methods can provide
rigorous correctness guarantees on hardware and software systems. Thanks to the
availability of mature tools, their use is well established in the industry,
and in particular to check safety-critical applications as they undergo a
stringent certification process. As machine learning is becoming more popular,
machine-learned components are now considered for inclusion in critical
systems. This raises the question of their safety and their verification. Yet,
established formal methods are limited to classic, i.e. non machine-learned
software. Applying formal methods to verify systems that include machine
learning has only been considered recently and poses novel challenges in
soundness, precision, and scalability.
We first recall established formal methods and their current use in an
exemplar safety-critical field, avionic software, with a focus on abstract
interpretation based techniques as they provide a high level of scalability.
This provides a golden standard and sets high expectations for machine learning
verification. We then provide a comprehensive and detailed review of the formal
methods developed so far for machine learning, highlighting their strengths and
limitations. The large majority of them verify trained neural networks and
employ either SMT, optimization, or abstract interpretation techniques. We also
discuss methods for support vector machines and decision tree ensembles, as
well as methods targeting training and data preparation, which are critical but
often neglected aspects of machine learning. Finally, we offer perspectives for
future research directions towards the formal verification of machine learning
systems
Perfectly Parallel Fairness Certification of Neural Networks
Recently, there is growing concern that machine-learning models, which currently assist or even automate decision making, reproduce, and in the worst case reinforce, bias of the training data. The development of tools and techniques for certifying fairness of these models or describing their biased behavior is, therefore, critical. In this paper, we propose a perfectly parallel static analysis for certifying causal fairness of feed-forward neural networks used for classification tasks. When certification succeeds, our approach provides definite guarantees, otherwise, it describes and quantifies the biased behavior. We design the analysis to be sound, in practice also exact, and configurable in terms of scalability and precision, thereby enabling pay-as-you-go certification. We implement our approach in an open-source tool and demonstrate its effectiveness on models trained with popular datasets