25 research outputs found
Adversarial Sampling for Fairness Testing in Deep Neural Network
In this research, we focus on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given dataset. While several framework had been proposed to ensure robustness of machine learning model against adversarial attack, some of which includes adversarial training algorithm. There is still the pitfall that adversarial training algorithm tends to cause disparity in accuracy and robustness among different group. Our research is aimed at using adversarial sampling to test for fairness in the prediction of deep neural network model across different classes or categories of image in a given dataset. We successfully demonstrated a new method of ensuring fairness across various group of input in deep neural network classifier. We trained our neural network model on the original image, and without training our model on the perturbed or attacked image. When we feed the adversarial samplings to our model, it was able to predict the original category/ class of the image the adversarial sample belongs to. We also introduced and used the separation of concern concept from software engineering whereby there is an additional standalone filter layer that filters perturbed image by heavily removing the noise or attack before automatically passing it to the network for classification, we were able to have accuracy of 93.3%. Cifar-10 dataset have ten categories of dataset, and so, in order to account for fairness, we applied our hypothesis across each categories of dataset and were able to get a consistent result and accuracy
Disentangling and Operationalizing AI Fairness at LinkedIn
Operationalizing AI fairness at LinkedIn's scale is challenging not only
because there are multiple mutually incompatible definitions of fairness but
also because determining what is fair depends on the specifics and context of
the product where AI is deployed. Moreover, AI practitioners need clarity on
what fairness expectations need to be addressed at the AI level. In this paper,
we present the evolving AI fairness framework used at LinkedIn to address these
three challenges. The framework disentangles AI fairness by separating out
equal treatment and equitable product expectations. Rather than imposing a
trade-off between these two commonly opposing interpretations of fairness, the
framework provides clear guidelines for operationalizing equal AI treatment
complemented with a product equity strategy. This paper focuses on the equal AI
treatment component of LinkedIn's AI fairness framework, shares the principles
that support it, and illustrates their application through a case study. We
hope this paper will encourage other big tech companies to join us in sharing
their approach to operationalizing AI fairness at scale, so that together we
can keep advancing this constantly evolving field
Causality-based Neural Network Repair
Neural networks have had discernible achievements in a wide range of
applications. The wide-spread adoption also raises the concern of their
dependability and reliability. Similar to traditional decision-making programs,
neural networks can have defects that need to be repaired. The defects may
cause unsafe behaviors, raise security concerns or unjust societal impacts. In
this work, we address the problem of repairing a neural network for desirable
properties such as fairness and the absence of backdoor. The goal is to
construct a neural network that satisfies the property by (minimally) adjusting
the given neural network's parameters (i.e., weights). Specifically, we propose
CARE (\textbf{CA}usality-based \textbf{RE}pair), a causality-based neural
network repair technique that 1) performs causality-based fault localization to
identify the `guilty' neurons and 2) optimizes the parameters of the identified
neurons to reduce the misbehavior. We have empirically evaluated CARE on
various tasks such as backdoor removal, neural network repair for fairness and
safety properties. Our experiment results show that CARE is able to repair all
neural networks efficiently and effectively. For fairness repair tasks, CARE
successfully improves fairness by on average. For backdoor removal
tasks, CARE reduces the attack success rate from over to less than
. For safety property repair tasks, CARE reduces the property violation
rate to less than . Results also show that thanks to the causality-based
fault localization, CARE's repair focuses on the misbehavior and preserves the
accuracy of the neural networks