Computational text analysis has become a far more widely used method of text analysis in the modern years. There are several benefits to computational text analysis. Using this technique, the root of a problem within both unstructured or structured data can be identified, trends and limits can be recognised, and digital experiences can be enhanced (Haije, 2019). In addition to these advantages, once the system behind the computational analysis has been trained to a sufficient level, the process becomes significantly efficient and quick (Haije, 2019). 

In comparison to computational text analysis, human text analysis has been used in the past and is currently used either in addition to or to replace machine text analysis. The benefits to human text analysis include the ease of commencement. Once a topic and dictionary have been established, the reading and writing of annotations can begin almost immediately. In addition, the interpretations and capabilities of humans have been trained and influenced during our every-day life by all the encounters we experience. Humans also have the benefit of being able to interpret anomalies with a higher success rate, such as irony (Wonderflow, 2019).

Though human text analysis does display some benefits, there are also many limitations that make computational analysis more easily accessible in the modern age. Consistency is often lacking in human text analysis, especially without repeating the process, as humans often evaluate things differently based on their mood (Wonderflow, 2019). Human memory can also present a constraint on the competence and speed of human text analysis. Text analysis often involves many firm definitions and parameters, the ability to remember these terms can hinder the process (Wonderflow, 2019).. Additionally, in comparison to computational text analysis, human text analysis can be a slow method due to manual input. 

With the benefits and limitations of computational and human text analysis in mind, I chose to document my own experiences with text analysis. Google Ngram Viewer is a tool that can be used to analyse terms used in literature and its relevance over time. I used this site to research the terms “anxiety” and “depression” over the years of 1800-2019. While the results showed a general increase, there was a peak in the use of the word “depression” in the 1930s. After the realisation that this was not related to mental health, and was instead referencing the Great Depression, one of Google Ngram’s complications became clear: context is not taken into account when analysing words. In addition, since Google Ngrams only documents written texts, much of the material from the world is unable to be assessed.

There are many tools on the internet that can provide basic computational text analysis. These instruments can be web-based applications, like voyant, or python-based, like Mallet. Either way, there are many ways to begin text analysis processes, and even more ways to enhance them. 

References 

Haije, E. G. (2019). What is Text Analytics? And why should I care? Retrieved May 23, 2022, from https://mopinion.com/what-is-text-analytics-benefits/ 

Reardon, J. (2020). “Text Analysis: An Overview”. METODHOLOGY. Retrieved May 23, 2022, from https://metodhology.anu.edu.au/index.php/content/text-analysis/ 

Wonderflow. (2019). What are the pros and cons of human text analysis – Part 2. Retrieved May 23, 2022, from https://www.wonderflow.ai/blog/what-are-the-pros-and-cons-of-human-text-analysis-part-2 

One ethical issue that arises with the continued emergence of AI and machine learning concerns privacy and surveillance. The data collected on a user by consistent use of “free” – that is, systems that are paid for by external parties to the user – services on the internet creates a data trail that can be used by AI and machines. These details can be employed to manipulate search results and promote corporations, potentially in an unethical fashion. 

Another ethical concern manifests in what is unknown in the Artificial Intelligence (AI) systems. With machine learning being the basis of a good portion of AI systems, it’s fundamental that we understand what we both do and do not know about their functions. While the originating system and its parameters are created by a human supervisor, the process will eventually evolve to be automated. At this point, the full processing activity is unknown even to the creator of the AI.

This discussion highlights the issue of responsibility. The European Group on Ethics in Science and New Technologies (2018) upheld that accountability, liability, and the rule of law were basic requirements that applied to new technologies. The debate of when accountability transfers from the creator of the machine to the machine itself has sparked several new discussions.

Bias is an immediate ethical issue in many aspects of digital humanities, Artificial Intelligence (AI) and machine learning are no exception. Müller (2020) outlined three main forms of bias in AI: bias based on a cognitive feature of a person that becomes embedded in the machine system, cognitive bias based on human interpretation of information, and systematic bias.

The ethical implications of AI and machine learning also extend to and envelop areas such as employment. Digital automation presents an opportunity for easily duplicatable and consistently affordable options to replace human thought and cognitive processing tasks (Bostrom & Yudkowsky, 2014). The issue of enhanced levels of unemployment as a consequence of digital automation poses a previously untraversed ethical dilemma: what happens to the millions of workers whose jobs become irrelevant?

To conclude, Artificial Intelligence (AI) and machine learning now provide a fundamental basis for a considerable portion of modern society’s activities and systems. The ethical issues that arise due to these developments include privacy and surveillance monitoring, unknown functionalities, debates over responsibility, bias in systems, and issues of unemployment. The likelihood that modern society will continue to form a reliance on AI and machine learning makes understanding these ethical concerns even more instrumental.

References:

Bostrom, N., Yudkowsky, E. (2014). “The Ethics of Artificial Intelligence”. The Cambridge Handbook of Artificial Intelligence, Cambridge, 316-334. 

European Group on Ethics in Science and New Technologies (2018). “Statement on Artificial Intelligence, Robotics and ‘Autonomous’ Systems”, European Commission. 

Müller, V. C. (2020). “Ethics of Artificial Intelligence and Robotics”, The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University.