A pragmatic perspective on AI transparency at workplace

Abstract

Recently, artificial intelligence (AI) systems have been widely used in different contexts and professions. However, with these systems developing and becoming more complex, they have transformed into black boxes that are difficult to interpret and explain. Therefore, urged by the wide media coverage of negative incidents involving AI, many scholars and practitioners have called for AI systems to be transparent and explainable. In this study, we examine transparency in AI-augmented settings, such as in workplaces, and perform a novel analysis of the different jobs and tasks that can be augmented by AI. Using more than 1000 job descriptions and 20,000 tasks from the O*NET database, we analyze the level of transparency required to augment these tasks by AI. Our findings indicate that the transparency requirements differ depending on the augmentation score and perceived risk category of each task. Furthermore, they suggest that it is important to be pragmatic about transparency, and they support the growing viewpoint regarding the impracticality of the notion of full transparency.

Effect of Transparency of Algorithmic Performance Management on Proactive Work Behavior

“…Firstly, the findings confirm the positive relationship between transparency of algorithmic performance management and motivation to improve performance. Algorithmic transparency has been found to increase the level of trust users have in the system (Logg et al, 2019), meaning that employees are able to learn from it rather than simply generalizing information inductively (Al-Sulaiti et al, 2023). Transparency enables algorithms and employees to work together in a positive way.…”

“…This also reduces the willingness of employees to take proactive actions (Gal et al, 2020). Transparency can ensure fair outcomes in the performance management processes of organizations, reduce information asymmetries, ensure accountability, and enable individuals to take responsibility for their actions (Al-Sulaiti et al, 2023). At the same time, opacity can undermine employees' trust in algorithms and make it harder to determine responsibility, as they may withhold important information or behavior for fear of damaging their interests (Langer & König, 2023).…”