Technology Source Guide: Convolutional Neural Networks, Explainability, and AI Impact

Frame the question

This guide explores how convolutional neural networks (CNNs) [1] underpin AI advancements, while their opacity necessitates explainability frameworks [2]. The mathematical model of arm coordination [3] provides a lens for understanding human-machine interaction, which intersects with AI's broader societal impact [4]. These sources collectively address technical, ethical, and practical dimensions of AI development.

What the evidence shows

Source 1's review of CNNs [1] establishes their dominance in computer vision and NLP, with 1-D/2-D convolutional architectures. The abstract states: 'The existing reviews mainly focus on CNN's applications... without considering CNN from a general perspective.' This gap motivates the paper's analysis of hyperparameter selection and multidimensional convolutions. Source 2's survey [2] addresses black box explainability, noting that 'applications in which black box decision systems can be used are various,' with each approach defining its own 'interpretability and explanation.' Source 3's mathematical model [3] predicts human arm movement patterns, asserting that 'the theoretical analysis is based solely on the kinematics of movement.' Source 4's multidisciplinary perspective [4] emphasizes AI's 'transformative potential' across industries, warning of 'societal and industrial influence on pace and direction of AI development.'

Follow the source trail

Source 1's CNN framework [1] is foundational to AI applications discussed in source 4. Source 2's explainability methods [2] directly address the opacity of CNNs (source 1) and broader AI systems (source 4). Source 3's mathematical model [3] offers a kinematic framework that could inform human-computer interaction research, aligning with source 4's focus on AI's societal integration. Source 4 synthesizes the technical (source 1) and ethical (source 2) dimensions of AI, while source 3's model provides a biological analogy for understanding AI's impact on human behavior.

Use these sources well

For an essay on AI's technical and ethical challenges, cite source 1 to detail CNN advancements, source 2 to discuss explainability, and source 4 to contextualize societal impacts. Use source 3's model as an example of how biological systems inform AI design. Avoid overstating correlations: for instance, while source 1 notes CNNs' 'state-of-the-art results,' source 2 warns that 'interpretability may sacrifice accuracy.' When discussing AI's industry impact (source 4), reference source 3's kinematic model as a case study in human-machine interaction. For follow-up research, explore source 1's 'open issues' in multidimensional convolutions or source 2's 'research open questions' in explainability frameworks.

What to search next

Source 1 raises unresolved issues in CNN generalization and hyperparameter optimization [1]. Source 2 highlights the trade-off between accuracy and interpretability in black box models [2]. Source 3's model [3] may require validation in non-planar movement contexts. Source 4's call for 'research agenda' [4] suggests gaps in AI governance and workforce displacement studies. These questions intersect: for example, how can explainability frameworks (source 2) address CNN opacity (source 1) without compromising performance? How might source 3's kinematic principles inform safer human-AI collaboration in industrial settings (source 4)?

Verbatim source abstracts

[1] A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects — IEEE Transactions on Neural Networks and Learning Systems, 2021-06-10, doi:10.1109/tnnls.2021.3084827

A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus on CNN's applications in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide some novel ideas and prospects in this fast-growing field. Besides, not only 2-D convolution but also 1-D and multidimensional ones are involved. First, this review introduces the history of CNN. Second, we provide an overview of various convolutions. Third, some classic and advanced CNN models are introduced; especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for functions and hyperparameter selection. Fifth, the applications of 1-D, 2-D, and multidimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed as guidelines for future work. [1]

[2] A survey of methods for explaining black box models — ACM Computing Surveys, 2019-01-01, doi:10.1145/3236009

In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective. [2]

[3] The coordination of arm movements: an experimentally confirmed mathematical model — The Journal of Neuroscience, 1985-07-01, doi:10.1523/jneurosci.05-07-01688.1985

This paper presents studies of the coordination of voluntary human arm movements. A mathematical model is formulated which is shown to predict both the qualitative features and the quantitative details observed experimentally in planar, multijoint arm movements. Coordination is modeled mathematically by defining an objective function, a measure of performance for any possible movement. The unique trajectory which yields the best performance is determined using dynamic optimization theory. In the work presented here, the objective function is the square of the magnitude of jerk (rate of change of acceleration) of the hand integrated over the entire movement. This is equivalent to assuming that a major goal of motor coordination is the production of the smoothest possible movement of the hand. Experimental observations of human subjects performing voluntary unconstrained movements in a horizontal plane are presented. They confirm the following predictions of the mathematical model: unconstrained point-to-point motions are approximately straight with bell-shaped tangential velocity profiles; curved motions (through an intermediate point or around an obstacle) have portions of low curvature joined by portions of high curvature; at points of high curvature, the tangential velocity is reduced; the durations of the low-curvature portions are approximately equal. The theoretical analysis is based solely on the kinematics of movement independent of the dynamics of the musculoskeletal system and is successful only when formulated in terms of the motion of the hand in extracorporal space. The implications with respect to movement organization are discussed. [3]

[4] Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy — International Journal of Information Management, 2019-08-27, doi:10.1016/j.ijinfomgt.2019.08.002

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance , healthcare, manufacturing, retail, supply chain , logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology . This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development. [4]

Source dossiers

Reference cards for every cited source, using only verified record metadata.

[1] A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects

• Authors: Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, Jun Zhou
• Venue: IEEE Transactions on Neural Networks and Learning Systems
• Published: 2021-06-10
• DOI: 10.1109/tnnls.2021.3084827
• Citation count: 5,068
• Institutions: Hohai University; Griffith University
• Topics: Advanced Neural Network Applications, Anomaly Detection Techniques and Applications, Video Surveillance and Tracking Methods, Convolutional neural network, Computer science, Data science, Artificial intelligence
• License/access: open access — license unspecified (open access)
• Record: https://doi.org/10.1109/tnnls.2021.3084827
• Abstract (verbatim): "A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention from both industry and academia in the past few years. The existing reviews mainly focus on CNN's applications in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide some novel ideas and prospects in this fast-growing field. Besides, not only 2-D convolution but also 1-D and multidimensional ones are involved. First, this review introduces the history of CNN. Second, we provide an overview of various convolutions. Third, some classic and advanced CNN models are introduced; especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for functions and hyperparameter selection. Fifth, the applications of 1-D, 2-D, and multidimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed as guidelines for future work." [1]

[2] A survey of methods for explaining black box models

• Authors: Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, Dino Pedreschi
• Venue: ACM Computing Surveys
• Published: 2019-01-01
• DOI: 10.1145/3236009
• Citation count: 4,941
• Institutions: University of Pisa; Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo"
• Topics: Explainable Artificial Intelligence (XAI), Adversarial Robustness in Machine Learning, Bayesian Modeling and Causal Inference, Interpretability, Black box, Computer science, Perspective (graphical), Data science, Artificial intelligence, Machine learning
• License/access: other-oa (open access)
• Record: https://doi.org/10.1145/3236009
• Abstract (verbatim): "In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective." [2]

[3] The coordination of arm movements: an experimentally confirmed mathematical model

• Authors: Tamar Flash, N. Hogan
• Venue: The Journal of Neuroscience
• Published: 1985-07-01
• DOI: 10.1523/jneurosci.05-07-01688.1985
• Citation count: 4,406
• Institutions: Massachusetts Institute of Technology; Melodea (Israel)
• Topics: Motor Control and Adaptation, Muscle activation and electromyography studies, Robot Manipulation and Learning, Kinematics, Jerk, Curvature, Acceleration, Movement (music), Trajectory, Function (biology)
• License/access: open access — license unspecified (open access)
• Record: https://doi.org/10.1523/jneurosci.05-07-01688.1985
• Abstract (verbatim): "This paper presents studies of the coordination of voluntary human arm movements. A mathematical model is formulated which is shown to predict both the qualitative features and the quantitative details observed experimentally in planar, multijoint arm movements. Coordination is modeled mathematically by defining an objective function, a measure of performance for any possible movement. The unique trajectory which yields the best performance is determined using dynamic optimization theory. In the work presented here, the objective function is the square of the magnitude of jerk (rate of change of acceleration) of the hand integrated over the entire movement. This is equivalent to assuming that a major goal of motor coordination is the production of the smoothest possible movement of the hand. Experimental observations of human subjects performing voluntary unconstrained movements in a horizontal plane are presented. They confirm the following predictions of the mathematical model: unconstrained point-to-point motions are approximately straight with bell-shaped tangential velocity profiles; curved motions (through an intermediate point or around an obstacle) have portions of low curvature joined by portions of high curvature; at points of high curvature, the tangential velocity is reduced; the durations of the low-curvature portions are approximately equal. The theoretical analysis is based solely on the kinematics of movement independent of the dynamics of the musculoskeletal system and is successful only when formulated in terms of the motion of the hand in extracorporal space. The implications with respect to movement organization are discussed." [3]

[4] Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

• Authors: Yogesh K. Dwivedi, Laurie Hughes, Elvira Ismagilova, Gert Aarts, Crispin Coombs, Tom Crick, Yanqing Duan, Rohita Dwivedi, John S. Edwards, Aled Eirug, Vassilis Galanos, P. Vigneswara Ilavarasan, Marijn Janssen, Paul Jones, Arpan Kumar Kar, Hatice Kizgin, Bianca Kronemann, Banita Lal, Biagio Lucini, Rony Medaglia, Kenneth Le Meunier‐FitzHugh, Leslie Caroline Le Meunier-FitzHugh, Santosh K. Misra, Emmanuel Mogaji, Sujeet Kumar Sharma, Jang Bahadur Singh, Vishnupriya Raghavan, Ramakrishnan Raman, Nripendra P. Rana, Spyridon Samothrakis, Jak Spencer, Kuttimani Tamilmani, Annie Tubadji, Paul Walton, Michael D. Williams
• Venue: International Journal of Information Management
• Published: 2019-08-27
• DOI: 10.1016/j.ijinfomgt.2019.08.002
• Citation count: 4,260
• Institutions: Prin. L. N. Welingkar Institute of Management Development and Research; Swansea University; University of Bradford; Loughborough University; University of Bedfordshire; Aston University; University of Edinburgh; Indian Institute of Technology Delhi
• Topics: Big Data and Business Intelligence, Economic and Technological Systems Analysis, Digital Transformation in Industry, Pace, Transformative learning, Government (linguistics), Multidisciplinary approach, Supply chain, Knowledge management, Business
• License/access: public-domain (open access)
• Record: https://doi.org/10.1016/j.ijinfomgt.2019.08.002
• Abstract (verbatim): "As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance , healthcare, manufacturing, retail, supply chain , logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology . This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development." [4]