Hi Preet,

there are some study investigate fairness perceptions of applicants when being "treated" by an AI, e.g.

Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018). 'It's reducing a human being to a percentage' perceptions of justice in algorithmic decisions. Proceedings of the 2018 Chi conference on human factors in computing systems,

Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2021). The dark sides of people analytics: reviewing the perils for organisations and employees. European Journal of Information Systems, 1-26.

Further there is a huge debate around AI ethics, bias and discrimination that should concern any organizational AI application that has implications for people

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

Kordzadeh, N., & Ghasemaghaei, M. (2021). Algorithmic bias: review, synthesis, and future research directions. European Journal of Information Systems, 1-22.

And finally the concept around "explainable AI" that has evolved as a result of the black box implications of some models (mainly neural networks and deep learning):

Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019). Explainable AI: A brief survey on history, research areas, approaches and challenges. Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part II 8,

Doran, D., Schulz, S., & Besold, T. R. (2017). What does explainable AI really mean? A new conceptualization of perspectives. arXiv preprint arXiv:1710.00794.

Finally, AI has a lot of applications in the non-human field, for instance, for predictive maintenance (e.g., prediction of machine breakdown, anomaly detection) and the whole IoT field (adding sensors to every toothbrush) that allows companies to directly measure customer behavior.

That having said, I have a course on Business Analytics and a few things I learned:

1) Many organizational tasks and informational needs are causal (e.g., why do customers leave? How can we increase sales? What are consequences of a compain or change in product features?). For these scenerios, traditional methods are better suited instead of Machine learning. The most relevant approaches are field experiments/AB testing, interrupted time series, survial analysis, Regression analysis with proper controls or instrumental variables

Pearl, J. (2019). The seven tools of causal inference, with reflections on machine learning. Commun. ACM, 62(3), 54-60.

2) Since AI is a hype, not everything is AI and it makes sense to distinguish AI from machine learning (ML)

3) For most organizational data, rather simple ML approaches (e.g., tree models, support vector machines) work better than neural networks.

Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on tabular data? arXiv preprint arXiv:2207.08815.

Hope that helps

Holger

Yes, there have been several research studies conducted on the impact of AI on employees based on organizational behavior. Some of the subtopics that you can consider for your term paper are:

The positive and negative employee experiences due to AI adoption and the creation of AI-based jobs1.

The impact of AI on organizational creativity and innovation2.

How AI can improve the employee experience in the workplace3.

The individual and organizational value derived from AI when using the technology improves employees' sense of competency, autonomy, and relatedness4.

The identity fears that employees perceive when AI is increasingly applied and how to address them5.

These subtopics can help you narrow down your research and provide a more focused approach to your term paper.

1- Impact of Artificial Intelligence on Employees working in Industry 4.0 Led Organizations. DOI:10.1108/IJM-03-2021-0173.

2- Artificial Intelligence Capability and Organizational Creativity: The Role of Knowledge Sharing and Organizational Cohesion. doi.org/10.3389/fpsyg.2022.845277.

3- How Can Artificial Intelligence Improve Organizational Psychology?: A Systematic Review.

4- Achieving Individual — and Organizational — Value With AI.

5- The rise of artificial intelligence – understanding the AI identity threat at the workplace. https://link.springer.com/article/10.1007/s12525-021-00496-x.

Kind regards

Here are some other recent papers :

Article AI Decision Making with Dignity? Contrasting Workers’ Justic...

Article Artificial intelligence in the work context

Conference Paper Empowering People in Human-Robot Collaboration: Bringing Tog...

Article Utilizing artificial intelligence to support analyzing self-...

Article Behavioural artificial intelligence: an agenda for systemati...

https://doi.org/10.1016/j.jretconser.2023.103376