Dr. Ulrich Leicht-Deobald

Summary

The use of artificial intelligence (AI), for example in the context of decisions pertaining to personnel in companies, can lead to social injustice. The aim of our interdisciplinary project is to develop a methodology for the designing of fair AI applications. This methodology will help stakeholders configure artificial intelligence for specific purposes in a socially acceptable way, and will make it possible to train software developers in ethical topics. The project combines philosophical, technical and social science issues: What does fairness mean? How is fairness perceived? How can fairness be implemented in AI? In doing so, the project connects the ethical discourse on AI with the technological implementation of AI.

Funding Agency

Swiss National Science Foundation (SNSF)

Programme

National Research Programme 77 'Digital Transformation'

Summary

The advent of big data holds the promise that organizational decision-making may change from more intuitive types of reasoning toward more deliberate kinds of choices (George, Haas, & Pentland, 2014). Particularly, in the field of Human Resource (HR) Management, big data techniques offer the potential to improve many HR functions, such as recruitment, retention, and performance management (The Conference Board, 2015). Despite this potential, HR practitioners have been reluctant to implement more refined analytical approaches. One major obstacle for the more widespread use of big data in HR is the expected skeptical reaction of the workforce. As of now, we have little systematic knowledge on how employees will perceive their employers" big data-enhanced monitoring and measurement activities, but drawing from research in management fields with a more mature big data literature (such as marketing), it seems likely that employee trust in their employer will play a key role in whether organizations can effectively apply big data techniques in their HR management. Thus, this project aims to understand the impact of a big data-driven workforce analytics on employees" trust in their employer. Drawing from the literature on HR control practices (Weibel et al., 2015), we expect that three main contingencies will shape the association between employees" perception of the use of big data-driven HR analytics and their trust in the employer: (1) the bundle of metrics and predictive analytics used by HR; (2) the implementation of legal requirements by the employer (particularly data and privacy protection laws); and (3) ethical guidelines on what is being measured for what reason, and how individuals" data are dealt with. We will study these influences using four modules including the following steps: (1) Interviews with experts on big data from both academia and practice who will serve as a `trust in big data" sounding board for the entire duration of the project; (2) a quantitative survey of 1,200 Swiss companies on their big data practices; (3) in-depth case studies of leading companies in the field; and (4) a factorial survey that will allow us to test causal hypotheses derived from modules 1-3. Our research project will generate systematic and relevant knowledge in three areas: First, we contribute to trust and human resources management theory by testing how and under which conditions big data-driven HR analytics influence employees" trust in their employer. Second, we contribute to HR management practice by describing the role HR departments could be playing in the effective use of big data-driven analytics, and how HR departments could contribute to the implementation of legal regulations and ethical stakeholder dialogue. Third, we analyze how legal regulations and ethical guidelines should be adapted to meet both legitimacy as well as effectiveness criteria.

Funding Agency

Swiss National Science Foundation (SNSF)

Programme

National Research Program (NRP) 75 `Big Data"