This project examines the scope for investigating the discourse surrounding public-facing decision-making algorithms using an interdisciplinary lens. Public-facing decision-making algorithms aim to increase productivity and enable more efficient and informed decision-making (Royal Academy of Engineering, 2017). Examples include the UK’s Covid-19 digital contact-tracing application and the Ofqual A Level automated algorithm used in 2020. These work without supervision and had impact on United Kingdom citizens (Kretzschmar et al., 2020; Kelly, 2021). Investigating trust in Autonomous Systems is critical for the development of future artificial intelligence technologies (Shahdar et al., 2018) as they become more relevant to our daily lives. The Trustworthy Autonomous Systems Hub adopts Devitt’s view that Autonomous Systems must be trustworthy by design and perception (2018).

The collection of opinions here will act as validation or signpost the need for alternative exploration (Bruch and Feinberg, 2017). To analyse the views expressed, computational linguistic methods – including topic modelling, sentiment analysis and emotion detection – may be deployed. These can be non-intrusive and cost-effective, as opposed to interviews or experiments (Rout et al., 2018). A source of data may be social media, notably Twitter, as many who have been affected by these algorithms offer opinions on this public-access site, providing a large data set that Twitter’s API can analyse in real-time (Kumar et al., 2014). 

However, these methods show limited awareness of the discursive and conversational ways in which opinions on decision-making algorithms are discussed on social media, which should be accounted for to understand this in further detail. Through initial work, popular computational linguistic methods’ shortcomings include issues with the classification of negation, sarcasm and irony (Mohammad, 2017) and difficulty in interpretation (Liu, 2010). A new approach may overcome these shortcomings by combining computational linguistic and sociolinguistic analytical methods, such as corpus linguistics and discourse analysis, where context plays an important role. Creating a hybrid approach to analysing the discourse surrounding public-facing decision-making algorithms may produce better quality insights to break down barriers to trust and adoption.

This project investigates the following questions:

1. How can current forms of computational linguistic analysis support the understanding of social media public discourses around decision-making algorithms? What are the benefits and shortcomings?
2. How can the discourse surrounding public-facing decision-making algorithms on social media be understood in more detail through the inclusion of discourse analysis and corpus linguistics alongside computational linguistic analysis?
3. In what ways can existing computational linguistic analysis be combined with qualitative linguistic analysis to mitigate the shortcomings of existing methods?

References

Devitt, S. K. (2018). Trustworthiness of autonomous systems. Springer, Cham.

Kelly, A. (2021). A tale of two algorithms: The appeal and repeal of calculated grades systems in England and Ireland in 2020. British Educational Research Journal.

Kretzschmar, M. E., Rozhnova, G., Bootsma, M. C., van Boven, M., van de Wijgert, J. H., & Bonten, M. J. (2020). Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study. The Lancet Public Health, 5, e452–e459.

Kumar, S., Morstatter, F., & Liu, H. (2014). Twitter data analytics. Springer.

Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of natural language processing, 2, 627–666.

Mohammad, S. M. (2017). Challenges in sentiment analysis. Springer.

Royal Academy of Engineering. (2017). Algorithms in decision-making (pp. 1-6). Retrieved from https://www.raeng.org.uk/publications/responses/algorithms-in-decision-making