40 research outputs found
Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence
An important challenge for safety in machine learning and artificial
intelligence systems is a~set of related failures involving specification
gaming, reward hacking, fragility to distributional shifts, and Goodhart's or
Campbell's law. This paper presents additional failure modes for interactions
within multi-agent systems that are closely related. These multi-agent failure
modes are more complex, more problematic, and less well understood than the
single-agent case, and are also already occurring, largely unnoticed. After
motivating the discussion with examples from poker-playing artificial
intelligence (AI), the paper explains why these failure modes are in some
senses unavoidable. Following this, the paper categorizes failure modes,
provides definitions, and cites examples for each of the modes: accidental
steering, coordination failures, adversarial misalignment, input spoofing and
filtering, and goal co-option or direct hacking. The paper then discusses how
extant literature on multi-agent AI fails to address these failure modes, and
identifies work which may be useful for the mitigation of these failure modes.Comment: 12 Pages, This version re-submitted to Big Data and Cognitive
Computing, Special Issue "Artificial Superintelligence: Coordination &
Strategy
FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks
Federated learning has created a decentralized method to train a machine
learning model without needing direct access to client data. The main goal of a
federated learning architecture is to protect the privacy of each client while
still contributing to the training of the global model. However, the main
advantage of privacy in federated learning is also the easiest aspect to
exploit. Without being able to see the clients' data, it is difficult to
determine the quality of the data. By utilizing data poisoning methods, such as
backdoor or label-flipping attacks, or by sending manipulated information about
their data back to the server, malicious clients are able to corrupt the global
model and degrade performance across all clients within a federation. Our novel
aggregation method, FedBayes, mitigates the effect of a malicious client by
calculating the probabilities of a client's model weights given to the prior
model's weights using Bayesian statistics. Our results show that this approach
negates the effects of malicious clients and protects the overall federation.Comment: Accepted to IEEE CCWC 202
Comprehensive Literature Review on Machine Learning Structures for Web Spam Classification
AbstractVarious Web spam features and machine learning structures were constantly proposed to classify Web spam in recent years. The aim of this paper was to provide a comprehensive machine learning algorithms comparison within the Web spam detection community. Several machine learning algorithms and ensemble meta-algorithms as classifiers, area under receiver operating characteristic as performance evaluation and two public available datasets (WEBSPAM-UK2006 and WEBSPAM-UK2007) were experimented in this study. The results have shown that random forest with variations of AdaBoost had achieved 0.937 in WEBSPAM-UK2006 and 0.852 in WEBSPAM-UK2007