Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions

Longo, Luca; Goebel, Randy; Lécué, Freddy; Kieseberg, Peter; Holzinger, Andreas

doi:10.1007/978-3-030-57321-8_1

Cited by 122 publications

(62 citation statements)

References 47 publications

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“…Explainable AI is an advanced artificial intelligence concept followed by easily comprehensible reasoning for how it arrived at a given conclusion [202]. Whether via pre-emptive layout or retrospective analysis.…”

Section: H Explainable Aimentioning

confidence: 99%

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

et al. 2021

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Facial expressions are mirrors of human thoughts and feelings. It provides a wealth of social cues to the viewer, including the focus of attention, intention, motivation, and emotion. It is regarded as a potent tool of silent communication. Analysis of these expressions gives a significantly more profound insight into human behavior. AI-based Facial Expression Recognition (FER) has become one of the crucial research topics in recent years, with applications in dynamic analysis, pattern recognition, interpersonal interaction, mental health monitoring, and many more. However, with the global push towards online platforms due to the Covid-19 pandemic, there has been a pressing need to innovate and offer a new FER analysis framework with the increasing visual data generated by videos and photographs.Furthermore, the emotion-wise facial expressions of kids, adults, and senior citizens vary, which must also be considered in the FER research. Lots of research work has been done in this area. However, it lacks a comprehensive overview of the literature that showcases the past work done and provides the aligned future directions. In this paper, the authors have provided a comprehensive evaluation of AI-based FER methodologies, including datasets, feature extraction techniques, algorithms, and the recent breakthroughs with their applications in facial expression identification. To the best of the author's knowledge, this is the only review paper stating all aspects of FER for various age brackets and would significantly impact the research community in the coming years.

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Section: H Explainable Aimentioning

confidence: 99%

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

et al. 2021

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“…A common criticism of LSTM-based models in particular and neural-network models in general, is that they are regarded as black-box models [23,24]. We sought to address this issue by conducting a model interpretability analysis, to understand how the model ranked the importance of variables when predicting mortality outcomes.…”

Section: Variable Importance and Model Interpretabilitymentioning

confidence: 99%

Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Mamandipoor

Frutos‐Vivar

Peñuelas

et al. 2021

BMC Med Inform Decis Mak

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Background Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters. Methods We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders. Results Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders. Conclusion The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes. Trial registration: NCT02731898 (https://clinicaltrials.gov/ct2/show/NCT02731898), prospectively registered on April 8, 2016.

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“…Presenting statistical and probabilistic data alone does not help practitioners in understanding how domain-specific requirements were met [4,5]. Particularly, the limited amount of explanations offered to users does not comply with regulatory and industrial standards [8,9]. Furthermore, an agreed and proven method of determining non-explainability of AI systems is not yet available to let practitioners assess XAI capabilities [10].…”

Section: Introductionmentioning

confidence: 99%

Toward Accountable and Explainable Artificial Intelligence Part Two: The Framework Implementation

Khan¹,

Vice²

2022

Preprint

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<div>This paper builds upon the theoretical foundations of the Accountable explainable Artificial Intelligence (AXAI) capability framework presented in part one of this paper. This part demonstrates the incorporation of the AXAI capability in the real time Affective State Assessment Module (ASAM) of a robotic system. The paper argues that adhering to the extreme Programming (XP) practices would help in understanding user behavior while systematically incorporating the AXAI capability in AI systems. Issues pertaining to identification of ethical, technical, functional, and domain-specific system requirements were resolved using an appropriate software design process, thus establishing confidence in the system. The presented ASAM synthesizes discrete and continuous models of affective state expressions and classifies them in real-time. Input, processed and output data are continuously shared with users via a graphical user interface (GUI) for providing reasons behind decisions. The GUI also disseminates information about local reasoning, data handling and decision-making. We hope this work will initiate further investigations on selection of suitable software design practices for incorporating the AXAI capability in AI systems, enhancing AI system acceptability and utility and, establishing a chain of responsibility.</div>

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Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions

Cited by 122 publications

References 47 publications

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions

Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Toward Accountable and Explainable Artificial Intelligence Part Two: The Framework Implementation

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