A proof-of-concept and feasibility analysis of using social sensors in the context of causal machine learning-based emergency management

Sahoh, Bukhoree; Choksuriwong, Anant

doi:10.1007/s12652-021-03317-3

Cited by 7 publications

(9 citation statements)

References 39 publications

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“…Evidence identification converts evidence into information (Zhu et al 2019 ), acting in an analogous way to human senses for hearing, seeing, smelling, or touch. The cause-effect determination for the information is based on past likelihood (Sahoh and Choksuriwong 2021b ), which expresses how likely it is that two events can co-occur and predicts what will happen in the next phase. This models how causal details of an event can be influenced by other events (Chen et al 2019 ; Duan et al 2019 ).…”

Section: An Xai-based Architecture For High-stakes Decision Makingmentioning

confidence: 99%

The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

Sahoh

Choksuriwong

2023

J Ambient Intell Human Comput

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A high-stakes event is an extreme risk with a low probability of occurring, but severe consequences (e.g., life-threatening conditions or economic collapse). The accompanying lack of information is a source of high-stress pressure and anxiety for emergency medical services authorities. Deciding on the best proactive plan and action in this environment is a complicated process, which calls for intelligent agents to automatically produce knowledge in the manner of human-like intelligence. Research in high-stakes decision-making systems has increasingly focused on eXplainable Artificial Intelligence (XAI), but recent developments in prediction systems give little prominence to explanations based on human-like intelligence. This work investigates XAI based on cause-and-effect interpretations for supporting high-stakes decisions. We review recent applications in the first aid and medical emergency fields based on three perspectives: available data, desirable knowledge, and the use of intelligence. We identify the limitations of recent AI, and discuss the potential of XAI for dealing with such limitations. We propose an architecture for high-stakes decision-making driven by XAI, and highlight likely future trends and directions.

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Section: An Xai-based Architecture For High-stakes Decision Makingmentioning

confidence: 99%

The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

Sahoh

Choksuriwong

2023

J Ambient Intell Human Comput

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“…The BS-based model was compared with our model because of its use of conditional dependency of a Bayesian Network [50], which produces relationships based on a DAG of data dependency. We applied scenario-based sensitivity analysis to highlight the rational explanation of both models.…”

Section: B Sensitivity Analysis For Causal Assumptionmentioning

confidence: 99%

The Design and Development of a Causal Bayesian Networks Model for the Explanation of Agricultural Supply Chains

2022

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The balancing of demand and supply in the market is a complex problem because of the dynamic supply chain and environment. It causes uncertain situations and is a limitation in decisions making systems that cannot produce reasonable descriptions to help decision makers eliminate uncertainties. This study proposes the design and development of a Causal Bayesian Networks (CBNs) model for market understanding, which encodes a human-like approach to explain demand and supply events for decision makers. A framework for generating reasonable descriptions in Agricultural Supply Chains (ASCs) management is proposed. The qualitative and quantitative design of the CBNs model is developed and proved that the CBNs model can reasonably explain events using predictive performance measurement and sensitivity analysis for producing reasonable descriptions. The results illustrate that the CBNs model is suitable for ASCs situation explanation involving uncertain situations, and is ready to apply to real-world applications to support decision making systems.

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“…The score of a user depends on activities and feedbacks made by peers on any of the user's versions. 29 Sahoh and Choksuriwong 30 proposed an emergency event management model using Bayesian Belief Networks. The model is based on social sensors and domain expert knowledge.…”

Section: Social Sensorsmentioning

confidence: 99%

“…The model utilizes social sensors to uncover posterior knowledge from uncertain emergency events, and deep event understanding using Who, What, Where, When, Why, and How (5W1H). 30…”

Section: Related Workmentioning

confidence: 99%

“…The model utilizes social sensors to uncover posterior knowledge from uncertain emergency events, and deep event understanding using Who, What, Where, When, Why, and How (5W1H). 30 Most of the existing applications based on social sensors are used to address the network security problems of emergencies in social media, predict information, and establish social graph considering the behavior characteristics of users. However, this concept is rarely used in the field of shilling attack detection, and the application of dynamic knowledge graph to the description of social sensors has not been proposed.…”

Section: Social Sensorsmentioning

confidence: 99%

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A security detection approach based on autonomy-oriented user sensor in social recommendation network

Wan

Liu

2022

International Journal of Distributed Sensor Networks

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User social network-based recommender system has achieved significant performance in current recommendation fields. However, the characteristic of openness brings great hidden dangers to the security of recommender systems. Shilling attackers can change the recommendations by foraging user relationships. Most shilling attack detection approaches depend on the explicit user historical data to locate shilling attackers. Some important features such as information propagation and social feedback of users in social networks have not been noticed. We propose a security detection method based on autonomy-oriented user sensor (AOUSD) to identify shilling attackers. Specifically, (1) the user is simulated as a social sensor with autonomous capabilities, (2) the user interaction model is built based on information propagation, information feedback and information disappearance mechanisms of social sensors, and a user dynamic knowledge graph is formed by considering the variable time function, (3) hierarchical clustering method is used to generate preliminary suspicious candidate groups and graph community detection clustering method is applied on the dynamic knowledge graph to detect the attackers. Then, AOUSD is first simulated on NetLogo and it is compared with other algorithms based on the Amazon data. The results prove the advantages of AOUSD in the efficiency and accuracy on shilling attack detection.

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A proof-of-concept and feasibility analysis of using social sensors in the context of causal machine learning-based emergency management

Cited by 7 publications

References 39 publications

The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

The Design and Development of a Causal Bayesian Networks Model for the Explanation of Agricultural Supply Chains

A security detection approach based on autonomy-oriented user sensor in social recommendation network

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