Challenges and opportunities in using interpretable AI to develop relationship interventions

Family Relations, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

Abstract Objective Although still in its infancy, research shows promise that artificial intelligence (AI) models can be integrated into relationship interventions, and the potential benefits are substantial. This article articulates challenges opportunities for developing interventions integrate AI. Background After defining AI differentiating machine learning from deep learning, we review key concepts strategies related to AI, specifically natural language processing, interpretability, human‐in‐the‐loop strategies, as approaches needed develop interventions. Method We explore how is currently family life literature has served foundation further integrating The use of therapy contexts examined, identify ethical need addressed this technology develops. Results examine using focusing on four areas: diagnosis problems, providing autonomous treatment, predicting successful treatment outcomes (prognosis), biomarkers monitor client reactions. Opportunities explored include development data‐efficient training methods, creating interpretable focused relationships, integration clinical expertise during model development, combining biomarker data with other modalities. Conclusion Despite obstacles, provide families personalized support strengthen bonds overcome relational challenges. Implications emerging intersection science pioneer innovative solutions diverse needs.

Language: Английский

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Explaining Misinformation Detection Using Large Language Models

Published: April 23, 2024

LLMs are a compressed repository of vast corpus valuable information on which they trained. Therefore, this work hypothesizes that such as Llama, Orca, Falcon, and Mistral can be used for misinformation detection by making them cross-check new with the Accordingly, paper describes findings from investigation abilities in detecting multiple datasets. The results interpreted using explainable AI techniques LIME, SHAP, Integrated Gradients. themselves also asked to explain their classification. These complementary approaches aid better understanding inner workings lead conclusions about effectiveness at task.

Language: Английский

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Multi-Link Prediction for mmWave Wireless Communication Systems using Liquid Time-Constant Networks, Long Short-term Memory, and Interpretation using Symbolic Regression

Published: July 2, 2024

A significant challenge encountered in mmWave and Sub-terahertz systems used 5G 1 the upcoming 6G networks is rapid fluctuation signal quality across various beam directions. Extremely high-frequency waves are highly vulnerable to obstruction, making even slight adjustments device orientation or presence of blockers capable causing substantial fluctuations link along a designated path. This issue poses major obstacle because numerous applications with low-latency requirements necessitate precise forecasting network from many directions cells. The method that proposed this research demonstrates an avant-garde approach for assessing multi-directional connections by utilizing Liquid Time Constant (LTC) instead conventionally Long Short-Term Memory (LSTM) technique. method’s validity was tested through optimistic simulation involving monitoring multi-cell at 28 GHz scenario where humans obstructions were moving arbitrarily. results LTC significantly better than those obtained conventional approaches such as LSTM. latter resulted test Root Mean Squared Error (RMSE) 3.44 dB, while former, 0.25 demonstrating 13-fold improvement. For interpretability illustrate complexity prediction, approximate mathematical expression also fitted simulated data using Symbolic Regression.

Language: Английский

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Multiclass Classification of Mental Health Disorders Using XGBoost-HOA Algorithm

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(8)

Published: Dec. 12, 2024

Language: Английский

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