FARKLI ETİK KURAMLARININ KURUMSAL ETİK TUTUM VE DAVRANIŞ KURALLARINA ETKİSİ: BİR KARŞILAŞTIRMALI SEMANTİK ANALİZ

Year 2022, Volume: 7 Issue: IMISC2021 Special Issue, 33 - 47, 30.03.2022

Mehmet Murat Albayrakoğlu , Mehmet N Aydın

https://doi.org/10.54452/jrb.1026523

Abstract

Bu çalışmanın amacı çeşitli etik kuramlarının, bilgisayar ve veri derneklerine ait etik tutum ve davranış kuralları üzerindeki etkilerini incelemektir. Etkilerin arasındaki farkları ölçmek ve değerlendirmek için, dört Python kütüphanesi, difflib, gensim, nltk ve spaCy, ve bunlara ek olarak web-tabanlı ve lisanslı bir semantik benzerlik aracı olan Compare Text kullanılmıştır. Bilişim Teknolojisi (BT) profesyonelleri ve akademisyenlerine ait yedi bilişim ve veri derneğinin kuralları, beş farklı etik düşünce ekolünün betimlemeleri ile karşılaştırılmıştır. Bulgular tablolanmış, radar çizimleri biçiminde özetlenmiş ve sonuçları tartışılmıştır. Farklı kuramların, kurallar üzerinde bir miktar farklı etkilerinin olduğu saptanmıştır. Bunun yanısıra, bazı araçlar ile hesaplanan benzerlik yüzdelerinin diğerlerininkinden ciddi ölçüde farklı olduğu gözlemlenmiştir. Son olarak, mevcut çalışmanın katkıları ve sınırları ile gelecekteki çalışmalar için öneriler sunulmuştur.

Keywords

Bilişim ve veri dernekleri, compare text, etik tutum ve davranış kuralları, semantik analiz, etik kuramları

Supporting Institution

Çalışma için herhangi bir kurumdan destek alınmamıştır.

INFLUENCE OF DIFFERENT THEORIES OF ETHICS ON ORGANIZATIONAL CODES OF CONDUCT OR ETHICS: A COMPARATIVE SEMANTIC ANALYSIS

Abstract

The aim of this study was to investigate the influence of various theories of ethics on codes of conduct or codes of ethics of computing and data organizations. To quantify and evaluate the differences in influence, four Python libraries, namely difflib, gensim, nltk, and spaCy, and, in addition, a web-based proprietary semantic similarity tool, Compare Text, were used. The codes of seven computing and data organizations for Information Technology (IT) professionals and scholars were compared to the descriptions of five different schools of ethical thought through four different tools. The findings were tabularized, summarized in radar charts, and their implications were discussed: It was found that there are some differences of influence on the codes by different theories. However, the percentages of similarities calculated by each tool were observed to differ, on some occasions, considerably. Finally, contributions and limitations of the current work and recommendations for further studies were presented.

Keywords

Codes of conduct or ethics, Compare Text, computing and data organizations, Python, semantic similarity, theories of ethics.

References

• Cortical.io. (2021). Compare Text. Retrieved May 20, 2021, from https://www.cortical.io/freetools/compare-text/.
• CRAN. (2021). CRAN task view: Natural language processing. Retrieved June 05, 2021, from: https://cran.r-project.org/web/views/NaturalLanguageProcessing.html.
• Deigh, J. (2010). An Introduction to ethics. Cambridge.
• Donlevy, J. K., & Walker, K. D. (2011). Working through ethics in education and leadership: Theory, analysis, plays, cases, poems, prose, and speeches. Sense Publications.
• Gomaa, W. H., & Fahmy, A. A. (2013). "A survey of text similarity approaches.", International Journal of Computer Applications, 68(13), 13-18.
• Han, J., Kamber, M. & Pei, J. (2012). Data mining: Concepts and techniques, 3rd ed. Morgan Kaufmann.
• Ignatow, G. & Mihalcea, R. (2018). An introduction to text mining: Research design, data collection, and analysis. SAGE Publications.
• Leskovec, J., Rajaraman, A. & Ullman, J. D. (2020). Mining of massive datasets, 3rd ed. Cambridge.
• McKee, A. (2003). Textual analysis: A beginner's guide. SAGE Publications.
• Kumar, A., & Kumar, P. (2016). Mastering text mining with R. Packt Publishing.
• O'Leary, D. (2016). "Ethics for Big Data and Analytics." IEEE Intelligent Systems, 31, 81-84.
• NLTK. (2021). NLTK 3.6.2 documentation: Natural Language Toolkit. Retrieved June 21, 2021, from https://www.nltk.org/.
• Oxford-Munich Code of Conduct. (n.d.). Code of conduct. Oxford-Munich Code of Conduct for Professional Data Scientist. Retrieved May 19, 2021, from http://www.code-of-ethics.org/code-of-conduct/.
• Python. (2021). Python documentation—The Python standard library—Text processing services: difflib—Helpers for computing deltas. Retrieved June 06, 2021, from https://docs.python.org/3/library/difflib.html.
• Quinn, M. J. (2015). Ethics for the information age, 7th ed. Pearson.
• Rachels, J., & Rachels, S. (2015). The elements of moral philosophy, 9th ed. McGraw-Hill.
• Ratcliff, J. W. & Metzener, D. E. (1988). Pattern matching: The gestalt approach. Retrieved Nov. 11, 2021, from https://www.drdobbs.com/database/pattern-matching-the-gestalt-approach/184407970?pgno=5.
• Řehůřek, R. (2021a). GENSIM topic modeling for humans: What is Gensim? Retrieved on June 21, 2021, from https://radimrehurek.com/gensim/intro.html.
• Řehůřek, R. (2021b). models.tfidfmodel – TF-IDF model. Retrieved on June 25, 2021, from https://radimrehurek.com/gensim/models/tfidfmodel.html.
• Rozeva, A., & Zerkova, S. (2017). "Assessing semantic similarity of texts: Methods and Algorithms," in the Proceedings of the 43rd International Conference of Applications of Mathematics in Engineering and Economics, AIP Conf. Proc. 1910, 060012-1-060012-8.
• Shafer-Landau, R. (2012). The fundamentals of ethics, 2nd ed. Oxford.
• Shafer-Landau, R. (2019). Living ethics: An introduction with readings. Oxford.
• Shrestha, P. (2011). “Corpus-based methods for short text similarity.” Rencontre des Etudiants Chercheurs en Informatique pour le Traitement Automatique des Langues, June 2011, Montpellier, France. 2 (1), pp.297-302.
• spaCy. (2021a). spaCy: Industrial-strength natural language processing in Python. Retrieved July 01, 2021, from https://spacy.io/.
• spaCy. (2021b). spaCy: Trained Models & Pipelines. Retrieved July 01, 2021, from https://spacy.io/models.
• Vijaymeena, M. K., & Kavitha, K. (2016). "A survey on similarity measures in text mining." Machine Learning and Applications: An International Journal, 13(1), 19-28.
• Webber, F. E. S. (2016). Semantic folding: Theory and its application in semantic fingerprinting [White paper]. Retrieved May 20, 2021, from https://www.cortical.io/static/downloads/semantic-folding-theory-white-paper.pdf.