Hostname: page-component-f554764f5-sl7kg Total loading time: 0 Render date: 2025-04-21T00:52:52.377Z Has data issue: false hasContentIssue false
  • English
  • Français

Effects of Artificial Intelligence and Robotics on Human Labour: A Systematic Review

Published online by Cambridge University Press:  30 October 2024

Channarong Intahchomphoo ,
Jason Millar ,
Odd Erik Gundersen ,
Christian Tschirhart ,
Kris Meawasige  and
Hojjat Salemi
Get access Rights & Permissions [Opens in a new window]

Abstract

This systematic literature review paper, written by Channarong Intahchomphoo, Jason Millar, Odd Erik Gundersen, Christian Tschirhart, Kris Meawasige and Hojjat Salemi, examines academic research publications to learn about the effects of artificial intelligence (AI) and robotics on human labour. Papers were collected from three academic databases: Scopus, Web of Science and ABI/INFORM Collection. From 710 papers, 159 papers were included. The article finds that the effects of AI and robotics on human labour can be categorised as: (i) positive effects, (ii) negative effects, and (iii) neutral or unsure effects. The positive effects have five reasons regarding AI and robotics’ potential to: do dangerous work, do tedious work with high efficiency and accuracy, do some aspects of computing work, do work that human labour does not want to do and be used to deal with the labour shortage, and help to reduce business production and maintenance costs. The negative effects are based on two reasons, that AI and robotics will take over human labour in part or entirely, thereby creating unemployment crises, and will not only replace manually repetitive jobs from human labour but also cognitive jobs, causing human labour to fear that their jobs will be replaced by AI and robotics. The neutral and unsure effects are based on various unique arguments. The findings of this review are used to suggest future research for academic communities and practical recommendations to legal professionals and policy makers.

Keywords

Artificial intelligenceRoboticsHuman labourSystematic literature review
Type
International Perspectives
Information
Legal Information Management , Volume 24 , Issue 2 , June 2024 , pp. 109 - 124
Copyright
Copyright © The Author(s), 2024. Published by British and Irish Association of Law Librarians

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Endnotes

1 Xu, Min, David, Jeanne M., and Kim, Suk Hi, ‘The Fourth Industrial Revolution: Opportunities and Challenges’ International Journal of Financial Research 9, no. 2 (2018) 90–95CrossRefGoogle Scholar.

2 We started conducting this review in February 2022, before the public launch of the ChatGPT generative AI tool. As a result, no ChatGPT-related studies were included in this review. However, we are currently conducting another systematic review on ChatGPT's ethical issues, including its effects on human labour.

3 Co-author Channarong Intahchomphoo conducted databases search, reviewed studies, and created the codebook for the themes for included studies. Co-authors Jason Millar, Odd Erik Gundersen, Christian Tschirhart, Kris Meawasige, and Hojjat Salemi commented on the overall research methodology, data analysis and discussion.

4 For transparency of this review, the metadata of included and excluded studies from all databases search can be found at https://github.com/ChannarongIntahchomphoo/Effects-of-AI-and-robotics-on-human-labour

5 Siwawong Buthgate and Somyot Kaitwanidvilai, ‘Development of a New Base of Robot Arm Using Finite Element Analysis’ 2018 International Electrical Engineering Congress, (2018) 1–4.

6 Pauliková, Alena, Babeľová, Zdenka Gyurák, and Ubárová, Monika, ‘Analysis of the Impact of Human–Cobot Collabourative Manufacturing Implementation on the Occupational Health and Safety and the Quality Requirements’ International Journal of Environmental Research and Public Health 18, no. 4 (2021) 1927CrossRefGoogle ScholarPubMed.

7 Mohamad Yuden, Mohamad Adzeem, Md Ghazaly, Mariam, Amran, Aliza Che, Jamaludin, Irma Wani, Yee, Khoo Hui, Yaacob, Mohd Rusdy, Abdullah, Zulkeflee, and Kiat, Yeo Chin, ‘Positioning Control Performances of a Robotic Hand System’ Jurnal Teknologi 79, no. 1 (2017) 1–23Google Scholar.

8 Kostenetskiy, Pavel, Alkapov, Rustem, Vetoshkin, Nikita, Chulkevich, Roman, Napolskikh, Ilya, and Poponin, Ostap, ‘Real-Time System for Automatic Cold Strip Surface Defect Detection’ FME Transactions 47, no. 4 (2019) 765–774CrossRefGoogle Scholar.

9 Ji, Wei, Wang, Yuquan, Liu, Hongyi, and Wang, Lihui, ‘Interface Architecture Design for Minimum Programming in Human-Robot Collabouration’ Procedia CIRP 72, (2018) 129–134CrossRefGoogle Scholar.

10 Abdul Mutolib, Rina Mardiati, Edi Mulyana, Aan Eko Setiawan, and Ahmad Fathonih, ‘Design of Automatic Goods Carrier Robot System Based on Line Sensor and Fuzzy Logic Control Mamdani’ 6th International Conference on Wireless and Telematics, (2020) 1–4.

11 Achilli, Gabriele Maria, Logozzo, Silvia, Valigi, Maria Cristina, Salvietti, Gionata, Prattichizzo, Domenico, and Malvezzi, Monica, ‘Underactuated Soft Gripper for Helping Humans in Harmful Works’ Proceedings of I4SDG Workshop 2021: IFToMM for Sustainable Development Goals 108, (2022) 264–272CrossRefGoogle Scholar.

12 Veerawuth Punlum, Jiraphon Srisertpol, and Sorada Khaengkam, ‘The Application of Double Arms SCARA Robot for Deburring of PCB Support Plate’ International Conference on Circuits, Devices and Systems, (2017)1–5.

13 Ulewicz, Robert, and Mazur, Magdalena, ‘Economic Aspects of Robotization of Production Processes by Example of a Car Semi-Trailers Manufacturer’ Manufacturing Technology 19, no. 6 (2019) 1054–1059CrossRefGoogle Scholar.

14 Garcia, Carlos A., Montalvo-Lopez, William, and Garcia, Marcelo V., ‘Human-Robot Collabouration Based on Cyber-Physical Production System and MQTT’ Procedia Manufacturing 42, (2020) 315–321CrossRefGoogle Scholar.

15 Nugraha, F. P., Setiati, R., and Fattahanisa, A., ‘Comparison of Gas Initial in Place Calculation Using MBAL and Artificial Intelligence Methods’ Journal of Physics: Conference Series, 1402, no. 5 (2019)Google Scholar.

16 Aletdinova, Anna, Kravchenko, Maxim, and Tsybina, Yana, ‘Prospects of the Social Agricultural Robot Creation’ Advances in Computer Science Research 72, (2017) 188–192Google Scholar.

17 Ege Ozgul and Ugur Celik, ‘Design and Implementation of Semi-Autonomous Anti-Pesticide Spraying and Insect Repellent Mobile Robot for Agricultural Applications’ 5th International Conference on Electrical and Electronic Engineering, (2018) 233–237.

18 Lars Andre Langøyli Giske, Emil Bjørlykhaug, Trond Løvdal, and Ola Jon Mork, ‘Experimental Study of Effectiveness of Robotic Cleaning for Fish-Processing Plants’ Food Control 100, (2019) 269–277.

19 Wen-Tsai Sung, Ihzany Vilia Devi, S. Hsiao, and Fathria Nurul Fadillah, ‘Smart Garbage Bin Based on AIOT’ Intelligent Automation & Soft Computing 32, no. 3 (2022) 1387–1401.

20 MA Viraj J. Muthugala, Manuel Vega-Heredia, Ayyalusami Vengadesh, G. Sriharsha, and Mohan Rajesh Elara, ‘Design of an Adhesion-Aware Façade Cleaning Robot’ IEEE/RSJ International Conference on Intelligent Robots and Systems, (2019) 1441–1447.

21 Sung Min Moon, Chang Yeob Shin, Jaemyung Huh, Kyeong Won Oh, and Daehie Hong, ‘Window Cleaning System with Water Circulation for Building Façade Maintenance Robot and Its Efficiency Analysis’ International Journal of Precision Engineering and Manufacturing-Green Technology 2, no. 1 (2015) 65–72.

22 Prasanthi Ambati, K. M. Suman Raj, and A. Joshuva, ‘A Review on Pipeline Inspection Robot’ AIP Conference Proceedings 2311, no. 1. (2020) 06002–7.

23 M. A. Viraj J. Muthugala, Koppaka Ganesh Sai Apuroop, Saurav Ghante Anantha Padmanabha, S. M. Bhagya P. Samarakoon, Mohan Rajesh Elara, and Raymond Yeong Wei Wen, ‘Falcon: A False Ceiling Inspection Robot’ Sensors 21, (2021) 5281.

24 Mohd Shahrieel Mohd Aras, Tan Pheng Chaing, Muhamad Khairi Aripin, Mohd Khairi Mohd Zambri, Alias Khamis, Mohd Bazli Bahar, and Mohd Zamzuri Ab Rashid, ‘Monitored and Controlled Underwater Scissor Arm Manipulator Using Pixy Camera’ Indian Journal of Geo Marine Sciences 48, no.7 (2019) 1120–1131.

25 Dibyendu Roy, Madhubanti Maitra, and Samar Bhattacharya, ‘Adaptive Formation-Switching of a Multi-Robot System in an Unknown Occluded Environment Using BAT Algorithm’ International Journal of Intelligent Robotics and Applications 4 (2020) 465–489.

26 Hardefa Rizky Putu Rogonondo, Son Kuswadi, Anhar Risnumawan, and Eny Kusumawati, ‘Tsunami Post Disaster Robot: Simulation and Implementation of Aerial Path Planning to Explore Unknown Environment’ AIP Conference Proceedings 2366, no. 1 (2021) 1–10.

27 Saeed Saeedvand, Hadi S. Aghdasi, and Jacky Baltes, ‘Robust Multi-Objective Multi-Humanoid Robots Task Allocation Based on Novel Hybrid Metaheuristic Algorithm’ Applied Intelligence 49, (2019) 4097–4127.

28 Yang Shen, Dejun Guo, Fei Long, Luis A. Mateos, Houzhu Ding, Zhen Xiu, Randall B. Hellman et al, ‘Robots Under COVID-19 Pandemic: A Comprehensive Survey’ IEEE Access 9, (2021) 1590–1615.

29 Jiří Bláha, Lucjan Klimsza, Aleš Lokaj, and Lech Nierostek, ‘Multidimensional Analysis of Ethical Leadership for Business Development’ European Journal of Sustainable Development 10, no. 1 (2021) 290–290.

30 Supra, note 12, Alena Pauliková, Zdenka Gyurák Babeľová, and Monika Ubárová.

31 Tsung-I Liao, Song-Shyong Chen, Chun-Chieh Lien, Hung-Cheng Lin, Meng-Fang Lu, and Wen-Ping Chen, ‘Development of a High-Endurance Cleaning Robot with Scanning-Based Path Planning and Path Correction’ Microsystem Technologies 27, no. 4 (2021) 1061–1074.

32 Bichen Zheng, ‘Financial Default Payment Predictions Using a Hybrid of Simulated Annealing Heuristics and Extreme Gradient Boosting Machines’ International Journal of Internet Technology and Secured Transactions 9, no. 4 (2019) 404–425.

33 Martin Holeček, ‘Learning from Similarity and Information Extraction from Structured Documents’ International Journal on Document Analysis and Recognition 24, no. 3 (2021) 149–165.

34 Xuan Yang, Zhengchao Chen, Baipeng Li, Dailiang Peng, Pan Chen, and Bing Zhang, ‘A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning’ IEEE International Geoscience and Remote Sensing Symposium, (2019) 5913–5916.

35 Jiedi Sun, Changhong Yan, and Jiangtao Wen, ‘Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning’ IEEE Transactions on Instrumentation and Measurement 67, no. 1 (2017) 185–195.

36 Yuantao Yang, Huailiang Zheng, Yongbo Li, Minqiang Xu, and Yushu Chen, ‘A Fault Diagnosis Scheme for Rotating Machinery Using Hierarchical Symbolic Analysis and Convolutional Neural Network’ ISA Transactions 91 (2019) 235–252.

37 Hosameldin O.A. Ahmed and Asoke K. Nandi, ‘Connected Components-Based Colour Image Representations of Vibrations for a Two-Stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks’ Chinese Journal of Mechanical Engineering 34, (2021) 1–21.

38 Kit Yan Chan, Ka Fai Cedric Yiu, Hak-Keung Lam, and Bert Wei Wong, ‘Ball Bonding Inspections Using a Conjoint Framework with Machine Learning and Human Judgement’ Applied Soft Computing 102, (2021) 107115.

39 Kai Su, Qiangfu Zhao, and Po Chun Lien, ‘Product Surface Defect Detection Based on CNN Ensemble with Rejection’ IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, IEEE 4th Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), (2019) 326–331.

40 Junaid Dar, Dinuka Ravimal, ChaBum Lee, and Sun-Kyu Lee, ‘Field Surface Roughness Levelling of the Lapping Metal Surface Using Specular White Light’ The International Journal of Advanced Manufacturing Technology 119, no. 5–6 (2022) 2895–2909.

41 Syed Ibrahim Hassan, L. Minh Dang, Irfan Mehmood, Suhyeon Im, Changho Choi, Jaemo Kang, Young-Soo Park, and Hyeonjoon Moon, ‘Underground Sewer Pipe Condition Assessment Based on Convolutional Neural Networks’ Automation in Construction 106, (2019) 102849.

42 Supra, note 28, Prasanthi Ambati, K. M. Suman Raj, and A. Joshuva.

43 Dirk Meijer, Lisa Scholten, Francois Clemens, and Arno Knobbe, ‘A Defect Classification Methodology for Sewer Image Sets with Convolutional Neural Networks’ Automation in Construction 104, (2019) 281–298.

44 Gioele Ciaparrone, Angela Serra, Vito Covito, Paolo Finelli, Carlo Alberto Scarpato, and Roberto Tagliaferri, ‘A Deep Learning Approach for Road Damage Classification’ Advanced Multimedia and Ubiquitous Engineering, (2018) 655–661.

45 Shushu Fan, Kouichi Takeya, Eiichi Sasaki, ‘Evaluation Method of Defects in Concrete Structures Using Hammer Test by Time-Frequency Analysis and Neural Networks’ IABSE Congress: Structural Engineering for Future Societal Needs, (2021) 955–964.

46 Dharneeshkar J., Soban Dhakshana V., Aniruthan S. A., Karthika R., and Latha Parameswaran, ‘Deep Learning Based Detection of Potholes in Indian Roads Using YOLO’ International Conference on Inventive Computation Technologies, (2020) 381–385.

47 Youfang Leng, Li Yu, and Jie Xiong, ‘DeepReviewer: Collabourative Grammar and Innovation Neural Network for Automatic Paper Review’ International Conference on Multimodal Interaction, (2019) 395–403.

48 Muladi Muladi, Dyah Lestari, and Dedy Tri Prasetyo, ‘Classification of Eligibility Consumption of Manalagi Apple Fruit Varieties Using Backpropagation’ 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, (2019) 75–79.

49 Raymond Erz Saragih, Dessy Gloria, and Albertus Joko Santoso, ‘Classification of Ambarella Fruit Ripeness Based on Color Feature Extraction’ ICIC Express Letters 15, no. 9 (2021) 1013–1020.

50 Alper Taner, Yeşim Benal Öztekin, and Hüseyin Duran, ‘Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut’ Sustainability 13, no. 12 (2021) 6527.

51 Tomáš Tureček, Pavel Vařacha, Alžběta Turečková, Václav Psota, Peter Janků, Vít Štěpánek, Adam Viktorin et al. ‘Scouting of Whiteflies in Tomato Greenhouse Environment Using Deep Learning’ Agriculture Digitalization and Organic Production: Proceedings of the First International Conference, (2021) 323–335.

52 Dongyup Shin, Hye Jin Kam, Min-Seok Jeon, and Ha Young Kim, ‘Automatic Classification of Thyroid Findings Using Static and Contextualized Ensemble Natural Language Processing Systems: Development Study’ JMIR Medical Informatics 9, no. 9 (2021) e30223.

53 Fica Aida Nadhifatul Aini, Ahmad Zatnika Purwalaksana, and Istas Pratomo Manalu, ‘Object Detection of Surgical Instruments for Assistant Robot Surgeon Using KNN’ 2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, (2019) 37–40.

54 Gahyung Ryu, Kyungmin Lee, Donggeun Park, Sang Hyun Park, and Min Sagong, ‘A Deep Learning Model for Identifying Diabetic Retinopathy Using Optical Coherence Tomography Angiography’ Scientific Reports 11, no. 1 (2021) 23024.

55 Dario Allegra, Mattia Litrico, Maria Ausilia Napoli Spatafora, Filippo Stanco, and Giovanni Maria Farinella, ‘Exploiting Egocentric Vision on Shopping Cart for Out-Of-Stock Detection in Retail Environments’ Proceedings of the IEEE/CVF International Conference on Computer Vision, (2021) 1735–1740.

56 Andika Rachman and RM Chandima Ratnayake, ‘Corrosion Loop Development of Oil and Gas Piping System Based on Machine Learning and Group Technology Method’ Journal of Quality in Maintenance Engineering 26, no. 3 (2019) 349–368.

57 Eduardo Plastino and Mark Purdy, ‘Game Changing Value from Artificial Intelligence: Eight Strategies’ Strategy & Leadership 46, no.1 (2018)16–22.

58 Yang Tian, Guoteng Zhang, Jie Ma, and Shugen Ma, ‘Automated Rust Detection via Digital Image Recognition during Grinding Work Process’ 2018 IEEE International Conference on Information and Automation, (2018) 318–323.

59 Mohamed T. Eraky, Dmitry V. Zubov, and Konstantin S. Krysanov, ‘Investigation of Inverse Kinematics Software Program of KUKA Manipulator Robot and Creation of Optimal Trajectory Control for Quality Evaluation within Chemical Production Lines’ International Journal of Engineering Research and Technology 11, no. 12 (2018) 2135–2158.

60 Mohamed T. Eraky, Dmitry V. Zubov, and Konstantin S. Krysanov, ‘Investigation of Forward Kinematics Software Program and Control of 3-DOF Manipulator Robot Using a New Developed Geometrical Approach Method for Improvement of Quality Food and Chemical Industries’ International Journal of Engineering Research and Technology 12, no. 9 (2019) 1381–1388.

61 Yang Lei, Li En, and Liang Zize, ‘An Automated Robot-Assisted 3D Reconstruction System for Adit’ 2017 Chinese Automation Congress, (2017) 1767–1772.

62 Shivali Agarwal, Vishalaksh Aggarwal, Arjun R. Akula, Gargi Banerjee Dasgupta, and Giriprasad Sridhara, ‘Automatic Problem Extraction and Analysis from Unstructured Text in IT Tickets’ IBM Journal of Research and Development 61, no. 1 (2017) 4–41.

63 Nicolas Hengartner, Leticia Cuellar, Xiao-Cheng Wu, Georgia Tourassi, John Qiu, Blair Christian, and Tanmoy Bhattacharya, ‘CAT: Computer Aided Triage Improving upon the Bayes Risk through ε-Refusal Triage Rules’ BMC Bioinformatics 19, no. 18 (2018) 3–8.

64 Matias Benitez, James Tian, Mark Kelly, Vignesh Selvakumaran, Matthew Phelan, Maciej Mazurowski, Joseph Y. Lo, Geoffrey D. Rubin, and Ricardo Henao, ‘Combining Deep Learning Methods and Human Knowledge to Identify Abnormalities in Computed Tomography (CT) Reports’ Medical Imaging 2019: Computer-Aided Diagnosis 10950, (2019) 229–235.

65 Reed Kopp, Joshua Joseph, and Brian L. Wardle, ‘Automated Segmentation of In Situ X-ray Microtomography of Progressive Damage in Advanced Composites via Deep Learning’ AIAA Scitech 2021 Forum, (2021) 1–23.

66 Bharat Prakash, Mohit Khatwani, Nicholas Waytowich, and Tinoosh Mohsenin, ‘Improving Safety in Reinforcement Learning Using Model-Based Architectures and Human Intervention’ Association for the Advancement of Artificial Intelligence, (2019).

67 Lionel J. Wong, Anthony Vassiliou, and Rodney Stromberg, ‘AI-Complemented First-Break Picking for Field Low-S/N Seismic Data’ The Leading Edge 40, no.6 (2021) 460–463.

68 Amir Pouran Ben Veyseh, My T. Thai, Thien Huu Nguyen, and Dejing Dou, ‘Rumor Detection in Social Networks via Deep Contextual Modeling’ Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (2019) 113–120.

69 Albert Sawczyn, Jakub Binkowski, Denis Janiak, Łukasz Augustyniak, and Tomasz Kajdanowicz, ‘Fact-Checking: Relevance Assessment of References in the Polish Political Domain’ Procedia Computer Science 192, (2021) 1285–1293.

70 Adrita Barua, Omar Sharif, and Mohammed Moshiul Hoque, ‘Multi-Class Sports News Categorization Using Machine Learning Techniques: Resource Creation and Evaluation’ Procedia Computer Science 193, (2021) 112–121.

71 Harumo Sasatake, Ryosuke Tasaki, Takahito Yamashita, and Naoki Uchiyama, ‘Imitation Learning System Design with Small Training Data for Flexible Tool Manipulation’ International Journal of Automation Technology 15, no. 5 (2021) 669–677.

72 Mayuka Shii, and Kenichi Ohara, ‘Stable Manipulation with Visual Servo Control for the Disposal of Objects in Retail Stores’ 2020 IEEE/SICE International Symposium on System Integration Proceedings, (2020) 972–975.

73 Supra, note 19, Robert Ulewicz, and Magdalena Mazur.

74 Supra, note 37, Tsung-I Liao, Song-Shyong Chen, Chun-Chieh Lien, Hung-Cheng Lin, Meng-Fang Lu, and Wen-Ping Chen.

75 Yuya Nakanishi and Yuki Inoue, ‘A Study on Active Wheel Level Control of Robots with Steering Moving Omnidirectionally’ 45th Annual Conference of the IEEE Industrial Electronics Society 1, (2019) 6821–6826.

76 Akitoshi Sato, Joo Kooi Tan, and Yuta Ono, ‘Development of a Human-Robot Cooperative System Based on Visual Information’ International Workshop on Advanced Image Technology 11049, (2019) 793–798.

77 Roy Chaoming Hsu, Po-Cheng Su, Jia-Le Hsu, and Chi-Yong Wang, ‘Real-Time Interaction System of Human-Robot with Hand Gestures’ IEEE Eurasia Conference on IOT, Communication and Engineering, (2020) 396–398.

78 M.A. Viraj J. Muthugala, S.M. Bhagya P. Samarakoon, and Mohan Rajesh Elara, ‘Toward Energy-Efficient Online Complete Coverage Path Planning of a Ship Hull Maintenance Robot Based on Glasius Bio-Inspired Neural Network’ Expert Systems with Applications 187, (2022) 1–9.

79 S. Jantanalach and S. Vongbunyong, ‘Gripper Design for Automatic Trimming System in Forging Process’ IOP Conference Series: Materials Science and Engineering 639, no. 1 (2019) 012013.

80 Novian Habibie, Aditya Murda Nugraha, Ahmad Zaki Anshori, M. Anwar Ma'sum, and Wisnu Jatmiko, ‘Fruit Mapping Mobile Robot on Simulated Agricultural Area in Gazebo Simulator Using Simultaneous Localization and Mapping’ 2017 International Symposium on Micro-NanoMechatronics and Human Science, (2017) 1–7.

81 Nithin, V., Shivam Mishra, P. Devarubiny, and S. Muthulakshmi, ‘IoT Enabled Farming Assist and Security Using Machine Learning’ ARPN Journal of Engineering and Applied Sciences 14, No.9 (2019) 1809–1819.

82 Yaqoob Majeed, Jing Zhang, Xin Zhang, Longsheng Fu, Manoj Karkee, Qin Zhang, and Matthew D. Whiting, ‘Deep Learning Based Segmentation for Automated Training of Apple Trees on Trellis Wires’ Computers and Electronics in Agriculture 170, (2020) 105277.

83 Harri Ketamo, Anu Passi-Rauste, Peter Vesterbacka, and Sanna Vahtivuori-Hänninen, ‘Accelerating the Nation: Applying AI to Scout Individual and Organisational Human Capital’ ICIE 6th International Conference on Innovation and Entrepreneurship, (2018) 198–205.

84 Zixiang Li, Nilanjan Dey, Amira S. Ashour, and Qiuhua Tang, ‘Discrete Cuckoo Search Algorithms for Two-Sided Robotic Assembly Line Balancing Problem’ Neural Computing and Applications 30, no. 9 (2018) 2685–2696.

85 Palash Dutta and Gourangajit Borah, ‘Robot Selection Problem via Fuzzy TOPSIS Method Using Novel Distance and Similarity Measure for Generalized Fuzzy Numbers with Unequal Heights’ New Mathematics and Natural Computation 18, no. 03 (2022) 657–702.

86 Bingjie Xiao, Minh Nguyen, and Wei Qi Yan, ‘Apple Ripeness Identification Using Deep Learning’ International Symposium on Geometry and Vision, (2021) 53–67.

87 Merrick Campbell, Keran Ye, Elia Scudiero, and Konstantinos Karydis, ‘A Portable Agricultural Robot for Continuous Apparent Soil Electrical Conductivity Measurements to Improve Irrigation Practices’ 17th International Conference on Automation Science and Engineering (2021) 2228–2234.

88 Rafaël Verbiest, Kris Ruysen, Tanja Vanwalleghem, Eric Demeester, and Karel Kellens, ‘Automation and Robotics in the Cultivation of Pome Fruit: Where Do We Stand Today?.’ Journal of Field Robotics 38, no. 4 (2021) 513–531.

89 Dat Do, Frank Pham, Amar Raheja, and Subodh Bhandari, ‘Machine Learning Techniques for the Assessment of Citrus Plant Health using UAV-Based Digital Images’ Proceedings of SPIE, (2018) 189–200.

90 Jinlai Zhang, Yanmei Meng, Jianfan Wu, Johnny Qin, Tao Yao, and Shuangshuang Yu, ‘Monitoring Sugar Crystallization with Deep Neural Networks’ Journal of Food Engineering 280 (2020) 109965.

91 Khongdet Phasinam and Thanwamas Kassanuk, ‘Supervised Machine Learning in Precision Agriculture,’ International Journal of Mechanical Engineering 7, no. 1 (2022) 1621–1625.

92 Anjani Dhrangadhariya, Roger Hilfiker, Roger Schaer, and Henning Müller, ‘Machine Learning Assisted Citation Screening for Systematic Reviews’ Studies in Health Technology and Informatics 270, (2020) 302–306.

93 Riddhish Bhalodia, Anupama Goparaju, Tim Sodergren, Alan Morris, Evgueni Kholmovski, Nassir Marrouche, Joshua Cates, Ross Whitaker, and Shireen Elhabian, ‘Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation’ 2018 Computing in Cardiology Conference 45, (2018) 1–4.

94 Yuki Shimada, Tetsuya Tanimoto, Masataka Nishimori, Antoine Choppin, Arie Meir, Akihiko Ozaki, Asaka Higuchi, Makoto Kosaka, Yuki Shimahara, and Naoyuki Kitamura, ‘Incidental Cerebral Aneurysms Detected by a Computer-Assisted Detection System Based on Artificial Intelligence: A Case Series’ Medicine 99, no. 43 (2020).

95 Ahmad Mohsin and Oliver Faust, ‘Automated Characterization of Cardiovascular Diseases Using Wavelet Transform Features Extracted from ECG Signals’ Journal of Mechanics in Medicine and Biology 19, no. 01 (2019) 1940009–19.

96 Linda Zhang, Daniel Fabbri, Raghu Upender, and David Kent, ‘Automated Sleep Stage Scoring of the Sleep Heart Health Study Using Deep Neural Networks’ SLEEPJ 42, no. 11 (2019) 1–10.

97 Kyungmin Kim, Yoojin Choi, Keonyoung Shim, Hyewon Jeon, Jane Commerford, and Eric T. Matson, ‘Analyzing the Range of Angles of a Solar Panel to Detect Defective Cells, Using a UAV’ Third IEEE International Conference on Robotic Computing, (2019) 471–476.

98 Boris Belousov, Bastian Wibranek, Jan Schneider, Tim Schneider, Georgia Chalvatzaki, Jan Peters, and Oliver Tessmann, ‘Robotic Architectural Assembly with Tactile Skills: Simulation and Optimization’ Automation in Construction 133, (2022) 104006.

99 Chenxi Li, Jun Wang, Yuyan Li, Yufeng Huang, Bo Li, and Zhong Zheng, ‘Infrared Image Defect Diagnosis Through LAB Space Transformation’ 2nd International Conference on Information Systems and Computer Aided Education, (2019) 126–130.

100 Alain Patchong and Laoucine Kerbache, ‘Transiting Toward the Factory of the Future: Optimal Buffer Sizes and Robot Cell Design in Car Body Production’ 2017 IEEE International Conference on Industrial Engineering and Engineering Management, (2017) 1596–1601.

101 Supra, note 19, Robert Ulewicz, and Magdalena Mazur.

102 Paul Gauvreau, ‘Robotics in Construction and the New Era of Efficient Concrete Bridges’ IABSE Congress: The Evolving Metropolis, (2019) 182–186.

103 Qianqian Luo, Guohua Yang, and Xiaofeng Zhao, ‘An Edge-Cloud Framework Equipped with Deep Learning Model for Recyclable Garbage Detection’ Eighth International Conference on Advanced Cloud and Big Data, (2020) 248–252.

104 Qianqian Luo, Zhenzhou Lin, Guohua Yang, and Xiaofeng Zhao, ‘DEC: A Deep-Learning Based Edge-Cloud Orchestrated System for Recyclable Garbage Detection’ Concurrency and Computation: Practice and Experience 35, no. 13 (2023) e6661.

105 J. Mukund Nilakantan, Izabela Nielsen, S. G. Ponnambalam, and S. Venkataramanaiah, ‘Differential Evolution Algorithm for Solving RALB Problem Using Cost-and Time-Based Models’ The International Journal of Advanced Manufacturing Technology 89, no. 1 (2017) 311–332.

106 Nuntiya Kruethi, Seksan Chaijit, Wachirapond Permpoonsinsup, Dechathanat Thongkot, A. H. Ismail, and Pramot Srinoi, ‘A Simulation Model to Improve the Efficiency of Painting Robots and Applied an Engineering Economic for Project Selection’ 17th International Conference on ICT and Knowledge Engineering, (2019) 1–6.

107 Kevin Castelli, Ahmed Magdy Ahmed Zaki, Yevheniy Dmytriyev, Marco Carnevale, and Hermes Giberti, ‘A Feasibility Study of a Robotic Approach for the Gluing Process in the Footwear Industry’ Robotics 10, no. 1 (2020) 6.

108 Rupesh Singh, Manmath Paste, Nirmala Shinde, Harshkumar Patel, and Nitin Mishra, ‘Chatbot Using TensorFlow for Small Businesses’ 2018 Second International Conference on Inventive Communication and Computational Technologies, (2018) 1614–1619.

109 Juha Saukkonen, Pia Kreus, Nora Obermayer, Óscar Rodríguez Ruiz, and Maija Haaranen, ‘AI, RPA, ML and Other Emerging Technologies: Anticipating Adoption in the HRM Field’ ECIAIR 2019 European Conference on the Impact of Artificial Intelligence and Robotics, (2019) 287–296.

110 Zoltán Rajnai and István Kocsis, ‘Labour Market Risks of Industry 4.0, Digitization, Robots and AI’ 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics, (2017) 343–346.

111 Roland W. Scholz, Eric J. Bartelsman, Sarah Diefenbach, Lude Franke, Arnim Grunwald, Dirk Helbing, Richard Hill et al, ‘Unintended Side Effects of the Digital Transition: European Scientists’ Messages from a Proposition-Based Expert Round Table’ Sustainability 10, no. 6 (2018): 2001.

112 Supra, note 90, Zixiang Li, Nilanjan Dey, Amira S. Ashour, and Qiuhua Tang.

113 Chia-Hui Lu, ‘The Impact of Artificial Intelligence on Economic Growth and Welfare’ Journal of Macroeconomics 69, (2021) 103342.

114 Debraj Ray and Dilip Mookherjee, ‘Growth, Automation, and the Long-Run Share of Labour’ Review of Economic Dynamics 46 (2022) 1–26.

115 Stanislav Ivanov, Mihail Kuyumdzhiev, and Craig Webster, ‘Automation Fears: Drivers and Solutions’ Technology in Society 63, (2020) 101431.

116 Ting-Sheng Chen, and Jen-Yuan Chang, ‘Robot Manipulator Vibration Resistance Control Based on Physical Model’ Information Storage and Processing Systems 83600, (2020) V001T03A002.

117 Ming-Hui Huang, and Roland T. Rust, ‘Artificial Intelligence in Service’ Journal of Service Research 21, no. 2 (2018) 155–172.

118 Andrzej Sobczak, ‘Building a Robotic Capability Map of the Enterprise’ Problemy Zarządzania – Management Issue 17, no. 5(85) (2019) 132–153.

119 Dusan Paredes and David Fleming-Muñoz, ‘Automation and Robotics in Mining: Jobs, Income and Inequality implications’ The Extractive Industries and Society 8, no. 1 (2021) 189–193.

120 Anu B. Titus, Thejas Narayanan, and Gautham P. Das, ‘Vision System for Coconut Farm Cable Robot’ 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, (2017) 443–450.

121 Haya Alaskar, Shaikah Alhewaidi, Bayan Obaid, Ghadah Alzahrani, Aisha Abdulahi, Zohra Sbai, and Thavavel Vaiyapuri, ‘Dates Fruit Classification Using Convolution Neural Networks’ Proceedings of Sixth International Congress on Information and Communication Technology 3, (2022) 757–775.

122 Tajim Md. Niamat Ullah Akhund, Md. Abu Bakkar Siddik, Md. Rakib Hossain, Md. Mazedur Rahman, Nishat Tasnim Newaz, and Mohd. Saifuzzaman. ‘IoT Waiter Bot: A Low Cost IoT Based Multi Functioned Robot for Restaurants’ in IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization: Trends and Future Directions (2020) 1174–1178.

123 Fabiano Compagnucci, Andrea Gentili, Enzo Valentini, and Mauro Gallegati, ‘Robotization and Labour Dislocation in the Manufacturing Sectors of OECD Countries: A Panel VAR Approach’ Applied Economic 51, no. 57 (2019) 6127–6138.

124 Yi Xu and Xin Ye, ‘Technology Upgrading and Labour Degrading? A Sociological Study of Three Robotized Factories’ The Journal of Chinese Sociology 8, no. 1 (2021) 1–23.

125 Spyros Makridakis, ‘High Tech Advances in Artificial Intelligence (AI) and Intelligence Augmentation (IA) and Cyprus’ Cyprus Review 30, no. 2 (2018) 159–167.

126 Cynthia Estlund, ‘What Should We Do After Work? Automation and Employment Law’ The Yale Law Journal 128, no. 2 (2018) 254–326.

127 ÖndeR Nomaler and Bart Verspagen, ‘Perpetual Growth, the Labour Share, and Robots’ Economics of Innovation and New Technology 29, no. 5 (2020) 540–558.

128 Andrew Berg, Edward F. Buffie, and Luis-Felipe Zanna, ‘Should We Fear the Robot Revolution? (The Correct Answer is Yes)’ Journal of Monetary Economics 97, (2018) 117–148.

129 Oliver Bendel, ‘Are Robot Tax, Basic Income or Basic Property Solutions to the Social Problems of Automation?’ AAAI 2019 Spring Symposium: Interpretable AI for Well-being, (2019).

130 Supra, note 133, ÖndeR Nomaler and Bart Verspagen.

131 Alex Liebergesell, ‘Design in the Age of Autonomous Machines: Modeling Inclusion, Dialogue, and Behavior’ The International Journal of Technology, Knowledge, and Society 15, no. 1 (2019) 27–37.

132 Henrique Barros Lopes, Flávio Vinícius Cruzeiro Martins, Rodrigo T. N. Cardoso, and Vinícius Fernandes dos Santos, ‘Combining Rules and Proportions: A Multiobjective Approach to Algorithmic Composition’ 2017 IEEE Congress on Evolutionary Computation, (2017) 2282–2289.

133 Josep Lladós-Masllorens, ‘Surfing the Waves of Digital Automation in Spanish Labour Market’ The International Research & Innovation Forum, (2019) 451–458.

134 Jhonata E. Ramos, Hae Yong Kim, and F. B. Tancredi, ‘Automation of the ACR MRI Low-Contrast Resolution Test Using Machine Learning’ 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, (2018).

135 Yunus Santur, Mehmet Karaköse, and Erhan Akin, ‘Big Data Framework for Rail Inspection’ 2017 International Artificial Intelligence and Data Processing Symposium, (2017) 1–4.

136 Pinmo Tong, Pengcheng Han, Suicheng Li, Ni Li, Shuhui Bu, Qing Li, and Ke Li, ‘Counting Trees with Point-Wise Supervised Segmentation Network’ Engineering Applications of Artificial Intelligence 100 (2021) 104172.

137 Pinmo Tong, Xishan Zhang, Pengcheng Han, and Shuhui Bu, ‘Point In: Counting Trees with Weakly Supervised Segmentation Network’ 2020 25th International Conference on Pattern Recognition, (2021) 9546–9552.

138 Hanan M. Hameed, Abdulmuttalib Turky Rashid, and Khairia A. Al Amry, ‘Automatic Storage and Retrieval System Using a Single Mobile Robot’ Proceedings of the 2nd International Conference on Electrical, Communication, and Computer Engineering, (2020) 1–6.

139 Miao Zhao, Yankun Peng, Long Li, and Xin Qiao, ‘Detection and Classification Manipulator System for Apple Based on Machine Vision and Optical Technology’ ASABE 2020 Annual International Virtual Meeting, (2020) 1–8.

140 Supra, note 68, Shivali Agarwal, Vishalaksh Aggarwal, Arjun R. Akula, Gargi Banerjee Dasgupta, and Giriprasad Sridhara.

141 M. Huang, D. R. Yang, D. Zhu, M. X. Yang, and J. J. Yang, ‘FM Broadcast Monitoring Using Artificial Intelligence’ Radio Science 55, no. 4 (2020) 1–6.

142 Armin Granulo, Christoph Fuchs, and Stefano Puntoni, ‘Psychological Reactions to Human Versus Robotic Job Replacement’ Nature Human Behaviour 3, no. 10 (2019) 1062–1069.

143 Supra, note 121, Stanislav Ivanov, Mihail Kuyumdzhiev, and Craig Webster.

144 Mika Westerlund, ‘The Ethical Dimensions of Public Opinion on Smart Robots’ Technology Innovation Management Review 10, no. 2 (2020) 25–36.

145 Benedetta Catanzariti, Srravya Chandhiramowuli, Suha Mohamed, Sarayu Natarajan, Shantanu Prabhat, Noopur Raval, Alex S. Taylor, and Ding Wang, ‘The Global Labours of AI and Data Intensive Systems’ Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing, (2021) 319–322.

146 Frank Fossen and Alina Sorgner, ‘Mapping the Future of Occupations: Transformative and Destructive Effects of New Digital Technologies on Jobs’ Foresight and STI Governance 13, no. 2 (2019) 10–18.

147 Dawei Zhang, Gang Peng, and Yuliang Yao, ‘Artificial Intelligence or Intelligence Augmentation? Unravelling the Debate through an Industry-level analysis’ Pacific Asia Conference on Information Systems Proceedings 68, (2019).

148 Supra, note 119, Chia-Hui Lu.

149 Supra, note 131, Spyros Makridakis.

150 João Reis, Nuno Melão, Juliana Salvadorinho, Bárbara Soares, and Ana Rosete, ‘Service Robots in the Hospitality Industry: The Case of Henn-na Hotel, Japan’ Technology in Society 63, (2020) 101423.

151 Felix Dominik Weber and Reinhard Schütte, ‘State-of-the-Art and Adoption of Artificial Intelligence in Retailing’ Digital Policy, Regulation and Governance 21, no. 3 (2019) 264–279.

152 Andrea Paesano, ‘Artificial Intelligence and Creative Activities Inside Organizational Behavior’ International Journal of Organizational Analysis 31, no.5 (2021) 1694–1723.

153 Armin Granulo, Christoph Fuchs, and Stefano Puntoni, ‘Preference for Human (vs. Robotic) Labour is Stronger in Symbolic Consumption Contexts’ Journal of Consumer Psychology 31, no. 1 (2021) 72–80.

154 Soomeen Hahm, ‘Augmented Craftsmanship: Creating Architectural Design and Construction Workflow by Augmenting Human Designers and Builders’ ACADIA, (2019) 448–456.

155 Melanie Swan, ‘Philosophy of Social Robotics: Abundance Economics’ International Conference on Social Robotics, (2016) 900–908.

156 Zedong Hu, Chenguang Yang, Wei He, Zhijun Li, and Shunzhan He, ‘Modeling and Simulation of Hand Based on OpenSim and Leap Motion’ 2017 Chinese Automation Congress, (2017) 4844–4849.

157 Charlynne Bolton, Veronika Machová, Maria Kovacova, and Katarina Valaskova, ‘The Power of Human-Machine Collabouration: Artificial Intelligence, Business Automation, and the Smart Economy’ Economics, Management, and Financial Markets 13, no. 4 (2018) 51–57.

158 Weitian Wang, Rui Li, Yi Chen, Z. Max Diekel, and Yunyi Jia, ‘Facilitating Human–Robot Collabourative Tasks by Teaching-Learning-Collabouration from Human Demonstrations’ IEEE Transactions on Automation Science and Engineering 16, no. 2 (2018) 640–653.

159 Alina Tausch and Annette Kluge, ‘The Best Task Allocation Process is to Decide on One's Own: Effects of the Allocation Agent in Human–Robot Interaction on Perceived Work Characteristics and Satisfaction’ Cognition, Technology & Work 24, no. 1 (2022) 39–55.

160 Yujiao Cheng, Liting Sun, Changliu Liu, and Masayoshi Tomizuka, ‘Towards Efficient Human-Robot Collabouration with Robust Plan Recognition and Trajectory Prediction’ IEEE Robotics and Automation Letters 5, no. 2 (2020) 2602–2609.

161 Nancy Velásquez Villagrán, Patricia Pesado, and Elsa Estevez, ‘Cloud Robotics for Industry 4.0: A Literature Review’ Cloud Computing, Big Data & Emerging Topics: 8th Conference JCC-BD&ET, (2020) 3–15.

162 Oscar Afonso, ‘The Oscar Goes to Horizontal Ellipsis Robots or Humans? Competition in a Directed Technical Change Model with Monetary Policy’ Economic of Innovation and New Technology, (2021).

163 Chiara Natalie Focacci, ‘Technological Unemployment, Robotisation, and Green Deal: A Story of Unstable Spillovers in China and South Korea (2008–2018)’ Technology in Society 64, (2021) 101504.

164 Divya Agarwal and Pushpendra S. Bharti, ‘Computation of Cause and Effect Relationship for Acceptance of Autonomous Mobile Robots in Industries’ Journal of Statistics and Management Systems 22, no. 2 (2019) 237–256.

165 Sihem Amer-Yahia, Senjuti Basu Roy, Lei Chen, Atsuyuki Morishima, James Abello Monedero, Pierre Bourhis, François Charoy et al, ‘Making AI Machines Work for Humans in FoW’ ACM Sigmod Record 49, no. 2 (2020) 30–35.

166 Felipe Tobar and Rodrigo González, ‘On Machine Learning and the Replacement of Human Labour: Anti-Cartesianism Versus Babbage's Path’ AI & SOCIETY 37 (2022) 1459–1471.

167 Ajay Agrawal, Joshua S. Gans, and Avi Goldfarb, ‘Exploring the Impact of Artificial Intelligence: Prediction Versus Judgment’ Information Economics and Policy 47 (2019): 1–6.

168 Chia-Hui Lu, ‘Artificial Intelligence and Human Jobs’ Macroeconomic Dynamics 26, no. 5 (2022) 1162–1201.

169 Edward W. Felten, Manav Raj, and Robert Seamans, ‘The Effect of Artificial Intelligence on Human Labour: An Ability-Based Approach’ Academy of Management Proceedings 2019, no. 1 (2019).

170 Tekin Birinci, ‘The Role of Artificial Intelligence and ICT on Economic Growth of G7 Countries’ International Journal of Innovative Technology and Exploring Engineering 8, no.8 (2019) 3251–3253.

171 Katya Klinova and Anton Korinek, ‘AI and Shared Prosperity’ Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, (2021) 645–651.

172 Andrea Gentili, Fabiano Compagnucci, Mauro Gallegati, and Enzo Valentini, ‘Are Machines Stealing Our Jobs?’ Cambridge Journal of Regions, Economy and Society 13, no. 1 (2020) 153–173.

173 Supra, note 175, Edward W. Felten, Manav Raj, and Robert Seamans