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Decoding the UK’s Stance on AI: A Deep Dive into Sentiment and Topics in Regulations

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Communication and Intelligent Systems (ICCIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 968))

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Abstract

Artificial intelligence (AI) is an innovative and remarkable technical advancement that has become an integral part of our lives, influencing every aspect of our existence. It is altering the structure of our everyday schedules and the way we operate in our professional environments. As we acclimate and gain further knowledge about this technology, it is imperative to acknowledge its profound impact on our lives. Due to the significant possible effects, it is crucial to have a deep understanding of its implications and be ready for any unexpected outcomes. It is essential to have regulatory guidance and proactive oversight in place for artificial intelligence. The UK, as a leading global entity, has taken a proactive stance in tackling both the ethical and operational aspects of AI. This study examines the legislative frameworks connected to artificial intelligence (AI) in the UK utilizing advanced approaches such as sentiment analysis and topic modeling. Our analysis reveals the UK’s equitable strategy toward AI, carefully considering its advantages in comparison to its obstacles. Important regulatory topics encompass ethics, data protection, transparency, and economic advancement. The sentiment analysis reveals a predominantly positive perspective, while emphasizing the importance of responsible employment of AI. This report illuminates the UK’s position on AI rules and serves as a benchmark for other regions to assess their AI initiatives.

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References

  1. Nilsson NJ (2012) John McCarthy. National Acad Sci 1–27

    Google Scholar 

  2. Kathleen W (2018) Artificial intelligence is not a technology, Forbes, Nov 1

    Google Scholar 

  3. Dwivedi DN, Anand A (2021) The text mining of public policy documents in response to COVID-19: a comparison of the United Arab Emirates and the Kingdom of Saudi Arabia. Public Gov/Zarządzanie Publiczne 55(1):8–22. https://doi.org/10.15678/ZP.2021.55.1.02

  4. Dwivedi D, Mahanty G, Vemareddy A (2021) How responsible is AI? Identification of key public concerns using sentiment analysis and topic modeling. Int J Inf Retrieval Res 12(1)

    Google Scholar 

  5. Hagendorff T (2020) The ethics of AI ethics: an evaluation of guidelines. Mind Mach 30(1):99–120. https://doi.org/10.1007/s11023-020-09517-8

    Article  Google Scholar 

  6. Maas MM (2018) Regulating for normal AI accidents, pp 223–228. https://doi.org/10.1145/3278721.3278766

  7. Box J, Data P (2019) Do you know what your model is doing? How human bias influences machine learning Elena Snavely—senior data scientist PHUSE UK connect 2019—Amsterdam machine learning in clinical research

    Google Scholar 

  8. Martinho A, Kroesen M, Chorus C (2020) An empirical approach to capture moral uncertainty in AI, pp 101–101. https://doi.org/10.1145/3375627.3375805

  9. Dwivedi DN, Patil G (2023) Climate change: prediction of solar radiation using advanced machine learning techniques. In: Srivastav A, Dubey A, Kumar A, Narang SK, Khan MA (eds) Visualization techniques for climate change with machine learning and artificial intelligence. Elsevier pp 335–358. https://doi.org/10.1016/B978-0-323-99714-0.00017-0

  10. Dwivedi DN et al. Benchmarking of traditional and advanced machine learning modeling techniques for forecasting in book visualization techniques for climate change with machine learning and artificial intelligence by Elsevier 2022. https://doi.org/10.1016/B978-0-323-99714-0.00017-0

  11. Dwivedi DN, Anand A (2021) Trade heterogeneity in the EU: insights from the emergence of COVID-19 using time series clustering. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie 3(993):9–26. https://doi.org/10.15678/ZNUEK.2021.0993.0301

  12. Dwivedi D, Kapur PN, Kapur NN (2023) Machine learning time series models for tea pest looper infestation in Assam, India. In: Sharma A, Chanderwal N, Khan R (eds) Convergence of cloud computing, AI, and agricultural science. IGI Global pp 280–289. https://doi.org/10.4018/979-8-3693-0200-2.ch014

  13. Bolander T (2019) What do we lose when machines make the decisions? J Manage Governance 23(4):849–867. https://doi.org/10.1007/s10997-019-09493-x

    Article  Google Scholar 

  14. Holzinger A, Haibe-Kains B, Jurisica I (2019) Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data. Eur J Nucl Med Mol Imag 46(13):2722–2730. https://doi.org/10.1007/s00259-019-04382-9

    Article  Google Scholar 

  15. Chikkamath M, Dwivedi D, Hirekurubar RB, Thimmappa R (2023) Benchmarking of novel convolutional neural network models for automatic butterfly identification. In: Shukla PK, Singh KP, Tripathi AK, Engelbrecht A (eds) Computer vision and robotics. Algorithms for intelligent systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7892-0_27

  16. Gupta A, Dwivedi DN, Shah J (2023) Overview of money laundering. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_1

  17. Dwivedi D, Batra S, Pathak YK (2023) A machine learning based approach to identify key drivers for improving corporate’s esg ratings. J Law Sustain Dev 11(1):e0242. https://doi.org/10.37497/sdgs.v11i1.242

  18. Dwivedi et al. Computer vision use case: detecting the changes in the amazon rainforest over time book by Apple Academic Press series on digital signal processing, computer vision and image processing in 2023

    Google Scholar 

  19. Gupta A et al (2021) Climate change monitoring using remote sensing, deep learning, and computer vision. Webology 19(2):2022. Available at: https://www.webology.org/abstract.php?id=1708

  20. Manjunath C, Dwivedi DN, Thimmappa R, Vedamurthy KB (2023) Detection and categorization of diseases in pearl millet leaves using novel convolutional neural network models. In: Future farming: advancing agriculture with artificial intelligence vol 1, pp 41. https://doi.org/10.2174/9789815124729123010006

  21. Gupta A et al (2021) Understanding consumer product sentiments through supervised models on cloud: pre and post COVID. Webology 18(1):406–415. Available at: https://doi.org/10.14704/web/v18i1/web18097

  22. Dwivedi DN, Anand A (2022) A comparative study of key themes of scientific research post COVID-19 in the United Arab Emirates and WHO using text mining approach. In: Tiwari S, Trivedi MC, Kolhe ML, Mishra K, Singh BK (eds) Advances in data and information sciences. Lecture notes in networks and systems, vol 318. Springer, Singapore. https://doi.org/10.1007/978-981-16-5689-7_30

  23. Dwivedi DN, Pathak S (2022) Sentiment analysis for COVID vaccinations using Twitter: text clustering of positive and negative sentiments. In: Hassan SA, Mohamed AW, Alnowibet KA (eds) Decision sciences for COVID-19. International series in operations research and management science, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-030-87019-5_12

  24. Alghamdi R, Alfalqi K (2015) A survey of topic modeling in text mining. Int J Adv Comput Sci Appl 6(1):147–153. https://doi.org/10.14569/ijacsa.2015.060121

  25. Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42(1–2):177–196. https://doi.org/10.1023/A:1007617005950

    Article  Google Scholar 

  26. Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407

    Article  Google Scholar 

  27. Asmussen CB, Møller C (2019) Smart literature review: a practical topic modeling approach to exploratory literature review. J Big Data 6(1). https://doi.org/10.1186/s40537-019-0255-7

  28. Gottipati S, Shankararaman V, Lin JR (2018) Text analytics approach to extract course improvement suggestions from students feedback. Res Pract Technol Enhanced Learn 13(1). https://doi.org/10.1186/s41039-018-0073-0

  29. Bagheri E, Ensan F, Al-Obeidat F (2018) Neural word and entity embeddings for ad hoc retrieval. Inf Process Manage 54(4):657–673

    Google Scholar 

  30. Benedetto F, Tedeschi A (2016) Big data sentiment analysis for brand monitoring in social media streams by cloud computing. In: Studies in computational intelligence vol 639. https://doi.org/10.1007/978-3-319-30319-2_14

  31. Samuel J, Rahman MM, Ali GMN, Samuel Y, Pelaez A, Chong PH, Yakubov M (2020) Feeling positive about reopening? New normal scenarios from COVID-19 US reopen sentiment analytics. In: IEEE Access, vol 8, pp 142173–142190. Available at SSRN: https://ssrn.com/abstract=3713652

  32. Alonso ME, González-Montaña JR, Lomillos JM (2020) Consumers concerns and perceptions of farm animal welfare. Anim: Open Access J MDPI 10(3). https://doi.org/10.3390/ani10030385

  33. Gupta A, Dwivedi DN, Shah J (2023) Financial crimes management and control in financial institutions. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_2

  34. Gupta A, Dwivedi DN, Shah J (2023) Overview of technology solutions. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_3

  35. Gupta A, Dwivedi DN, Shah J (2023) Data organization for an FCC unit. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_4

  36. Gupta A, Dwivedi DN, Shah J (2023) Planning for AI in financial crimes. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_5

  37. Gupta A, Dwivedi DN, Shah J (2023) Applying machine learning for effective customer risk assessment. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_6

  38. Gupta A, Dwivedi DN, Shah J (2023) Artificial intelligence-driven effective financial transaction monitoring. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_7

  39. Gupta A, Dwivedi DN, Shah J (2023) Machine learning-driven alert optimization. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_8

  40. Gupta A, Dwivedi DN, Shah J (2023) Applying artificial intelligence on investigation. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_9

  41. Gupta A, Dwivedi DN, Shah J (2023) Ethical challenges for AI-based applications. In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_10

  42. Gupta A, Dwivedi DN, Shah J (2023) Setting up a best-in-class AI-driven financial crime control unit (FCCU). In: Artificial intelligence applications in banking and financial services. Future of business and finance. Springer, Singapore. https://doi.org/10.1007/978-981-99-2571-1_11

  43. Gupta A, Dwivedi DN, Jain A (2021) Threshold fine-tuning of money laundering scenarios through multi-dimensional optimization techniques. J Money Laundering Control. https://doi.org/10.1108/JMLC-12-2020-0138

  44. Gupta A, Dwivedi DN, Shah J, Jain A (2021) Data quality issues leading to suboptimal machine learning for money laundering models. J Money Laundering Control. https://doi.org/10.1108/JMLC-05-2021-0049

  45. Dwivedi D, Vemareddy A (2023) Sentiment analytics for crypto pre and post covid: topic modeling. In: Molla AR, Sharma G, Kumar P, Rawat S (eds) Distributed computing and intelligent technology. ICDCIT 2023. Lecture notes in computer science, vol 13776. Springer, Cham. https://doi.org/10.1007/978-3-031-24848-1_21

  46. Dwivedi D, Patil G (2022) Lightweight convolutional neural network for land use image classification. J Adv Geospatial Sci Technol 2(1):31–48. Retrieved from https://jagst.utm.my/index.php/jagst/article/view/31

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Authors and Affiliations

  1. SAS Middle East FZ-LLC, Dubai Media City-Business Central Towers, Dubai, UAE

    Dwijendra Nath Dwivedi

  2. Department of Analytical and Applied Economics, Utkal University, Orissa, India

    Ghanashyama Mahanty

Authors
  1. Dwijendra Nath Dwivedi
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  2. Ghanashyama Mahanty
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Corresponding author

Correspondence to Dwijendra Nath Dwivedi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Electrical and Electronics Engineering, National Institute of Technology Uttarakhand, Srinagar, Uttarakhand, India

    Vivek Shrivastava

  3. Department of Computer Science, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Ashish Kumar Tripathi

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Dwivedi, D.N., Mahanty, G. (2024). Decoding the UK’s Stance on AI: A Deep Dive into Sentiment and Topics in Regulations. In: Sharma, H., Shrivastava, V., Tripathi, A.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2023. Lecture Notes in Networks and Systems, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-97-2079-8_11

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