Promoting the learning motivation of minority groups in technology firms through AI feedback

Overview and purpose

Large language models, drawing on available data, have the potential to mimic human intelligence, to infer patterns and logical connections and to generate responses that are similar to how a person might respond. This means that AI can offer tailored and human-like feedback in real time. Critical feedback, while important to support learning and career growth, may cause ambiguity in minority groups about whether it arises from accurate assessments of their performance or is due to their minority status in such environments. Prior beliefs can filter attributions such that the minority group believe that the feedback they receive may be due to their minority status in that setting rather than a commentary on their performance as individuals. Where not addressed, this may hold back a person’s learning and career growth. The purpose of this PhD project is to explore whether artificial intelligence can address feedback bias in technology settings.

Objectives

· To explore if AI can obviate attributional ambiguity of feedback and appraisals.

· To investigate the role of WISE feedback as a model in reducing feedback bias through AI.

· To understand recipients’ subjective assessments as to the accuracy of the feedback.

Significance of the project

The project will make a significant contribution to the HRM literature, specifically the performance management and diversity and inclusion literature. Through this project, we propose that AI can help women and ethnic minorities in the tech sector as feedback is less likely to be perceived as discriminatory when it arises from a neutral source, such as AI.

We propose that the PhD candidate will design and carry out a field study in a high-tech sector in order to explore the topic. The candidate will investigate the role of WISE feedback which sets high standards while simultaneously conveying that the receiver is capable of achieving them. The candidate will aim to accurately tap into the perceptions of feedback bias and investigate if and how AI might help alleviate such bias. Knowledge of quantitative design and analyses is an advantage.

Patterns of perceived feedback bias are likely to reinforce gender segregation and increase the potentiality for minorities to be under-represented, especially at higher levels. Feedback through AI may help because feedback is attributed as less discriminatory when it arises from a neutral source, such as AI. This PhD project does not delve into the question of whether AI systems or indeed male counterparts are objectively biased in the feedback they provide. Although there is evidence to show that this may be the case, this is beyond the scope of this study. This is because the question of whether the feedback is motivated by perceptions about the minority group (i.e discrimination), rather than the individual themself, does not arise.

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Its purpose is to provide research and education that combines academic excellence with positive impact on people, business and society. As a world leader in experiential learning and personalisation, joining NBS as a researcher is an opportunity to achieve your potential.

Applications for October 2024 intake closes on 1st August 2024 and applications for Jan 2025 intake closes on 1st October 2024