With the rise of AI, algorithms have become better at learning underlying patterns from the training data including ingrained social biases based on gender, race, etc. Deployment of such algorithms to domains such as hiring, healthcare, law enforcement, etc. has raised serious concerns about fairness, accountability, trust and interpretability in machine learning algorithms. To alleviate this problem, we propose D-BIAS, a visual interactive tool that embodies human-in-the-loop AI approach for auditing and mitigating social biases from tabular datasets. It uses a graphical causal model to represent causal relationships among different features in the dataset and as a medium to inject domain knowledge. A user can detect the presence of bias against a group, say females, or a subgroup, say black females, by identifying unfair causal relationships in the causal network and using an array of fairness metrics. Thereafter, the user can mitigate bias by refining the causal model and acting on the unfair causal edges. For each interaction, say weakening/deleting a biased causal edge, the system uses a novel method to simulate a new (debiased) dataset based on the current causal model while ensuring a minimal change from the original dataset. Users can visually assess the impact of their interactions on different fairness metrics, utility metrics, data distortion, and the underlying data distribution. Once satisfied, they can download the debiased dataset and use it for any downstream application for fairer predictions. We evaluate D-BIAS by conducting experiments on 3 datasets and also a formal user study. We found that D-BIAS helps reduce bias significantly compared to the baseline debiasing approach across different fairness metrics while incurring little data distortion and a small loss in utility. Moreover, our human-in-the-loop based approach significantly outperforms an automated approach on trust, interpretability and accountability.
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TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security
Despite AI’s significant growth, its “black box”
nature creates challenges in generating adequate trust. Thus, it
is seldom utilized as a standalone unit in high-risk applications.
Explainable AI (XAI) has emerged to help with this problem.
Designing effectively fast and accurate XAI is still challenging,
especially in numerical applications. We propose a novel XAI
model named Transparency Relying Upon Statistical Theory
(TRUST) for XAI. TRUST XAI models the statistical behavior of
the underlying AI’s outputs. Factor analysis is used to transform
the input features into a new set of latent variables. We use
mutual information to rank these parameters and pick only the
most influential ones on the AI’s outputs and call them “representatives” of the classes. Then we use multi-model Gaussian
distributions to determine the likelihood of any new sample
belonging to each class. The proposed technique is a surrogate
model that is not dependent on the type of the underlying AI.
TRUST is suitable for any numerical application. Here, we use
cybersecurity of the industrial internet of things (IIoT) as an
example application. We analyze the performance of the model
using three different cybersecurity datasets, including “WUSTLIIoT”, “NSL-KDD”, and “UNSW”. We also show how TRUST
is explained to the user. The TRUST XAI provides explanations
for new random samples with an average success rate of 98%.
Also, the advantages of our model over another popular XAI
model, LIME, including performance, speed, and the method of
explainability are evaluated.
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- Award ID(s):
- 1718929
- NSF-PAR ID:
- 10326532
- Date Published:
- Journal Name:
- IEEE Internet of Things Journal
- ISSN:
- 2372-2541
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/JIOT.2021.3122019
