NLP and Ethic Articles
Knowledge Graphs: Natural Language Processing and Ethics (2020-2024). Author: Milena Jael SILVA. Ecolonical LAB

Natural Language Processing and Ethics: Curated Articles (2020-2024)

This curated corpus offers a comprehensive collection of scholarly articles published between 2020 and 2024 that examine the ethical dimensions of Natural Language Processing (NLP). As NLP technologies rapidly evolve and become integral to sectors such as healthcare, social media, and autonomous systems, the ethical challenges associated with their deployment have garnered increasing attention. The collection critically explores how these technologies, capable of processing and interpreting vast amounts of textual data, raise important ethical concerns regarding privacy, bias, consent, and accountability. By bridging technical advancements with ethical considerations, these articles provide an in-depth analysis of how NLP reshapes industries and societal dynamics while highlighting the importance of embedding responsible practices in its development and use.

The works included in this corpus address a wide array of critical topics, from ethical implications in clinical NLP applications to the societal impact of large language models. The selected studies analyze the consequences of deploying AI and NLP technologies in public, commercial, and healthcare domains, underscoring the pressing need for robust ethical frameworks. By incorporating case studies, theoretical discussions, and empirical research, this corpus aims to foster a nuanced understanding of how NLP can be both a transformative tool and a potential source of ethical risks.

Key themes explored in this corpus include:

  • Bias in Large Language Models: Examination of how large language models can propagate and amplify societal biases, and the mitigation techniques aimed at reducing these biases in NLP systems.
  • Ethical Concerns in Clinical Applications: Analysis of the ethical dilemmas arising from the use of NLP in healthcare, focusing on patient privacy, consent, and the reliability of AI-driven clinical decision-making tools.
  • Privacy and Consent in Textual Data Processing: Exploration of the ethical challenges surrounding the collection and use of large datasets for NLP, particularly concerning user consent and the potential for data misuse.
  • Accountability and Transparency in AI-Driven Systems: Insights into the accountability of NLP systems in automated decision-making processes, particularly in sectors like law, healthcare, and social media moderation.
  • The Societal Impact of NLP and AI Technologies: Consideration of the broader societal implications of NLP technologies, including their role in shaping discourse, influencing behavior, and reinforcing power dynamics.
  • Ethical Data Governance and NLP: Discussions on the ethical considerations of managing and governing textual data used in training NLP systems, with a focus on ensuring fair and equitable access to data.

2024

  1. Deroncele-Acosta, Angel; Bellido-Valdiviezo, Omar; Sanchez-Trujillo, Maria De Los Angeles; Palacios-Nunez, Madeleine Lourdes; Rueda-Garces, Hernan; Brito-Garcias, Jose Gregorio. (2024). Ten Essential Pillars in Artificial Intelligence for University Science Education: A Scoping Review. Sage Open, 14(3), 21582440241272016. https://doi.org/10.1177/21582440241272016
  2. Ferrario, Andrea; Sedlakova, Jana; Trachsel, Manuel. (2024). The Role of Humanization and Robustness of Large Language Models in Conversational Artificial Intelligence for Individuals With Depression: A Critical Analysis. JMIR Mental Health, 11, e56569. https://doi.org/10.2196/56569
  3. Garikapati, Divya; Shetiya, Sneha Sudhir. (2024). Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape. Big Data and Cognitive Computing, 8(4), 42. https://doi.org/10.3390/bdcc8040042
  4. Lahat, Adi; Sharif, Kassem; Zoabi, Narmin; Patt, Yonatan Shneor; Sharif, Yousra; Fisher, Lior; Arow, Mohamad; Levin, Roni; Klang, Eyal. (2024). Assessing Generative Pretrained Transformers (GPT) in Clinical Decision-Making: Comparative Analysis of GPT-3.5 and GPT-4. Journal of Medical Internet Research, 26, e54571. https://doi.org/10.2196/54571
  5. Luqman, Muhammad; Faheem, Muhammad; Ramay, Waheed Yousuf; Saeed, Malik Khizar; Ahmad, Majid Bashir. (2024). Utilizing Ensemble Learning for Detecting Multi-Modal Fake News. IEEE Access, 12, 15037-15049. https://doi.org/10.1109/ACCESS.2024.3357661
  6. Rathore, Archit; Dev, Sunipa; Phillips, Jeff M.; Srikumar, Vivek; Zheng, Yan; Yeh, Chin-Chia Michael; Wang, Junpeng; Zhang, Wei; Wang, Bei. (2024). VERB: Visualizing and Interpreting Bias Mitigation Techniques Geometrically for Word Representations. ACM Transactions on Interactive Intelligent Systems, 14(1), 3. https://doi.org/10.1145/3604433
  7. Tummala, Purnima; Roa, Ch. Koteswara. (2024). Exploring T5 and RGAN for Enhanced Sarcasm Generation in NLP. IEEE Access, 12, 88642-88657. https://doi.org/10.1109/ACCESS.2024.3416692

2023

  1. Ahmed, Muhammad Shamim; Kornblum, Daisy; Oliver, Dominic; Fusar-Poli, Paolo; Patel, Rashmi. (2023). Associations of Remote Mental Healthcare With Clinical Outcomes: A Natural Language Processing Enriched Electronic Health Record Data Study Protocol. BMJ Open, 13(2), e067254. https://doi.org/10.1136/bmjopen-2022-067254
  2. Doyal, Alexander S.; Sender, David; Nanda, Monika; Serrano, Ricardo A. (2023). ChatGPT and Artificial Intelligence in Medical Writing: Concerns and Ethical Considerations. Cureus Journal of Medical Science, 15(8), e43292. https://doi.org/10.7759/cureus.43292
  3. Fitzpatrick, Natalie K.; Dobson, Richard; Roberts, Angus; Jones, Kerina; Shah, Anoop; Nenadic, Goran; Ford, Elizabeth. (2023). Understanding Views Around the Creation of a Consented, Donated Databank of Clinical Free Text to Develop and Train Natural Language Processing Models for Research: Focus Group Interviews With Stakeholders. JMIR Medical Informatics, 11, e45534. https://doi.org/10.2196/45534
  4. Jackson, Sally M.; Daverio, Margherita; Perez, Silvia Lorenzo; Gesualdo, Francesco; Tozzi, Alberto E. (2023). Improving Informed Consent for Novel Vaccine Research in a Pediatric Hospital Setting Using a Blended Research-Design Approach. Frontiers in Pediatrics, 8, 520803. https://doi.org/10.3389/fped.2020.520803
  5. Reichenpfader, Daniel; Muller, Henning; Denecke, Kerstin. (2023). Large Language Model-Based Information Extraction from Free-Text Radiology Reports: A Scoping Review Protocol. BMJ Open, 13(12), e076865. https://doi.org/10.1136/bmjopen-2023-076865
  6. Teodorescu, Horia-Nicolai L.; Pirnau, Mironela. (2023). Twitter’s Mirroring of the 2022 Energy Crisis: What It Teaches Decision-Makers – A Preliminary Study. Romanian Journal of Information Science and Technology, 26(3-4), 312-322. https://doi.org/10.59277/ROMJIST.2023.3-4.05

2022

  1. Altuntas, Erkin; Gloor, Peter A.; Budner, Pascal. (2022). Measuring Ethical Values With AI for Better Teamwork. Future Internet, 14(5), 133. https://doi.org/10.3390/fi14050133
  2. Gloor, Peter; Fronzetti Colladon, Andrea; Grippa, Francesca. (2022). Measuring Ethical Behavior With AI and Natural Language Processing to Assess Business Success. Scientific Reports, 12(1), 10228. https://doi.org/10.1038/s41598-022-14101-4
  3. Hacohen-Kerner, Yaakov; Manor, Natan; Goldmeier, Michael; Bachar, Eytan. (2022). Detection of Anorexic Girls in Blog Posts Written in Hebrew Using a Combined Heuristic AI and NLP Method. IEEE Access, 10, 34800-34814. https://doi.org/10.1109/ACCESS.2022.3162685
  4. Liscio, Enrico; van der Meer, MichielHere’s the continued structure with the remaining references for 2022 and the additional years, organized as requested:“`html
  5. Liscio, Enrico; van der Meer, Michiel; Siebert, Luciano C.; Jonker, Catholijn M.; Murukannaiah, Pradeep K. (2022). What Values Should an Agent Align With? An Empirical Comparison of General and Context-Specific Values. Autonomous Agents and Multi-Agent Systems, 36(1), 23. https://doi.org/10.1007/s10458-022-09550-0
  6. Malins, Sam; Figueredo, Grazziela; Jilani, Tahseen; Long, Yunfei; Andrews, Jacob; Rawsthorne, Mat; Manolescu, Cosmin; Clos, Jeremie; Higton, Fred; Waldram, David; Hunt, Daniel; Vallejos, Elvira Perez; Moghaddam, Nima. (2022). Developing an Automated Assessment of In-Session Patient Activation for Psychological Therapy: Codevelopment Approach. JMIR Medical Informatics, 10(11), e38168. https://doi.org/10.2196/38168

2021

  1. Guzhva, Oleksiy; Siegford, Janice M.; Kolstrup, Christina Lunner. (2021). The Hitchhiker’s Guide to Integration of Social and Ethical Awareness in Precision Livestock Farming Research. Frontiers in Animal Science, 2, 725710. https://doi.org/10.3389/fanim.2021.725710
  2. Jackson, Sally M.; Daverio, Margherita; Perez, Silvia Lorenzo; Gesualdo, Francesco; Tozzi, Alberto E. (2021). Improving Informed Consent for Novel Vaccine Research in a Pediatric Hospital Setting Using a Blended Research-Design Approach. Frontiers in Pediatrics, 8, 520803. https://doi.org/10.3389/fped.2020.520803
  3. Luitse, Dieuwertje; Denkena, Wiebke. (2021). The Great Transformer: Examining the Role of Large Language Models in the Political Economy of AI. Big Data & Society, 8(2), 20539517211047734. https://doi.org/10.1177/20539517211047734

2020

  1. Clarke, Natasha; Foltz, Peter; Garrard, Peter. (2020). How to Do Things With (Thousands of) Words: Computational Approaches to Discourse Analysis in Alzheimer’s Disease. Cortex, 129, 446-463. https://doi.org/10.1016/j.cortex.2020.05.001

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