Machine Morale Evaluation Model utilizing Electroencephalography (EEG) and Galvanic Skin Response (GSR) for gauging human trust in artificial intelligence
In a groundbreaking development, researchers have unveiled a new approach to building classifier-based empirical trust sensor models for intelligent machines. These models utilise electroencephalography (EEG) and galvanic skin response (GSR) measurements to estimate human trust levels in real-time, enhancing user-machine interaction with immediate feedback.
The research, presented in a recent publication, outlines two approaches for developing these trust sensor models. The first approach employs a general set of psychophysiological features across all participants, resulting in a 'general trust sensor model'. The second approach considers a customised feature set for each individual.
To create these models, the researchers follow a systematic process: data collection, feature extraction, classifier training, model evaluation, and real-time implementation.
Data collection involves using EEG and GSR devices to gather physiological data from subjects interacting with intelligent machines. EEG signals, which can indicate cognitive states such as attention and involvement, and GSR signals, which reflect physiological arousal and emotional responses, are both collected.
Next, relevant features are extracted from this data for use in the classifiers. For EEG, power spectral density (PSD) in different frequency bands (delta, theta, alpha, beta) relevant to cognitive states is extracted. GSR features include skin conductance levels and event-related responses that indicate emotional responses.
Machine learning models, such as Random Forest or Support Vector Machines (SVM), are then used to classify trust levels based on these features. Feature selection is also carried out to identify the most relevant features influencing the model's trust predictions.
The performance of the models is evaluated using metrics like accuracy, precision, recall, and F1 score. Cross-validation is performed to ensure the models generalise well to new data.
Finally, a real-time data processing pipeline is implemented to continuously collect and analyse EEG and GSR data, with the trained classifier integrated to estimate trust levels in real-time.
The research compared the general and customised feature sets in the development of the classifier-based trust sensor models, finding that the customised feature set improves mean accuracy but at the cost of increased training time.
Despite this trade-off, both approaches are suggested to be useful depending on the specific requirements and resources of the application. The study underscores the need for further investigation to optimise this trade-off in the trust sensor models.
The use of real-time psychophysiological measurements for the development of a human trust sensor is a significant advancement in the field. The research highlights the potential of the developed trust sensor models for real-time trust estimation in intelligent machines, and discusses the implications of the work for the design of trust management algorithms for such machines.
Moreover, the study emphasises the importance of considering individual differences in the design of trust sensors for intelligent machines. The research suggests that the developed trust sensor could potentially be integrated with other sensors to create a comprehensive trust management system for intelligent machines.
In summary, this research presents a promising avenue for improving human-machine interaction through the development of classifier-based empirical trust sensor models using EEG and GSR measurements. The findings underscore the need for further research to optimise the trade-off between accuracy and training time in the trust sensor models, and the potential for the development of a comprehensive trust management system for intelligent machines.
- The researchers suggest that science, particularly in the field of psychophysiology, plays a crucial role in the development of these trust sensor models by providing insights into cognitive states and emotional responses through the use of EEG and GSR measurements.
- The advancement in technology, namely machine learning algorithms such as Random Forest or Support Vector Machines (SVM), is essential for training the classifier in the trust sensor models, enabling real-time estimation of human trust levels in user-machine interaction.
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