Widespread adoption of artificial intelligence (AI) in civilian and defense government agencies requires the stakeholders to have trust in AI solutions. One of the five principles of ethical AI, identified by the Department of Defense, emphasizes that AI solutions be equitable. The AI system involves a series of choices from data selection to model definition, each of which is subject to human and algorithmic biases and can lead to unintended consequences. This paper focuses on allowing AI bias mitigation with the use of synthetic data. The proposed technique, named Fair-GAN, builds upon the recently developed Fair-SMOTE approach, which used synthesized data to fix class and other imbalances caused by protected attributes such as race and gender. Fair-GAN uses Generative Adversarial Networks (GAN) instead of the Synthetic Minority Oversampling Technique (SMOTE). While SMOTE can only synthesize tabular and numerical data, GAN can synthesize tabular data with numerical, binary, and categorical variables. GAN can also synthesize other data forms such as images, audio and text. In our experiments, we use the Synthetic Data Vault (SDV), which implements approaches such as conditional tabular GAN (CTGAN) and tabular variational autoencoders (TVAE). We show the applicability of Fair-GAN to several benchmark problems, which are used to evaluate the efficacy of AI bias mitigation algorithms. It is shown that Fair-GAN leads to significant improvements in metrics used for evaluating AI fairness such as the statistical parity difference, disparate impact, average odds difference, and equal opportunities difference.
Deminers around the globe are still using handheld metal detectors that lack the capability to distinguish mines from clutter, detect mines containing very little metal, or find mines buried at deeper depths. In the southern African country of Angola, many areas and roads are impassable due to the threat of anti-tank landmines. Some of these mines are undetectable using current metal detector technology. The US Army has funded the development of the NIITEK ground penetrating radar (GPR) for detection of anti-tank (AT) landmines. This radar detects metal and plastic mines as well as mines that are buried too deep for handheld metal detectors to find. The US Department of Defense Humanitarian Demining (HD) Research & Development Program focuses on developing, testing, demonstrating, and validating new technology for immediate use in humanitarian demining operations around the globe. The HD team provided funding and guidance to NIITEK Incorporated for development of a prototype system called Mine Stalker - a relatively light-weight, remote-controlled vehicle outfitted with the NIITEK GPR, detection algorithms, and a marking system. Individuals from the HD team, NIITEK Inc, and the non-governmental organization Meschen Gegen Minen (MgM) participated in a field evaluation of the Mine Stalker in Angola. The primary aim was to evaluate the effectiveness and reliability of the NIITEK GPR under field conditions. The Mine Stalker was extremely reliable during the evaluation with no significant maintenance issues. All AT mines used to verify GPR performance were detected, even when buried to depths as deep as 25-33cm.
KEYWORDS: Land mines, Autoregressive models, General packet radio service, Digital filtering, Genetic algorithms, Data modeling, Signal processing, Electronic filtering, Detection and tracking algorithms, Optimization (mathematics)
Previous large-scale blind tests of anti-tank landmine detection utilizing the NIITEK ground penetrating radar indicated the potential for very high anti-tank landmine detection probabilities at very low false alarm rates for algorithms based on adaptive background cancellation schemes. Recent data collections under more heterogeneous multi-layered road-scenarios seem to indicate that although adaptive solutions to background cancellation are effective, the adaptive solutions to background cancellation under different road conditions can differ significantly, and misapplication of these adaptive solutions can reduce landmine detection performance in terms of PD/FAR. In this work we present a framework for the constrained optimization of background-estimation
filters that specifically seeks to optimize PD/FAR performance as measured by the area under the ROC curve between two FARs. We also consider the application of genetic algorithms to the problem of filter optimization for landmine detection. Results indicate robust results for both static and adaptive background cancellation schemes, and possible real-world advantages and disadvantages of static and adaptive approaches are discussed.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.