Dr. Ulrich Leicht-Deobald

The use of artificial intelligence (AI), for example, in personnel decisions at companies can lead to social injustice. The aim of our interdisciplinary project is to develop a methodology for designing 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 its technological implementation.

The advent of big data holds the promise that organisational decision-making may change from more intuitive types of reasoning toward more deliberate kinds of choices (George, Haas, & Pentland, 2014). In particular, in the field of human resource management, big data techniques have 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 to the more widespread use of big data in HR is the expected skepticism among 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 organisations can effectively apply big data techniques in their HR management. Thus, this project aims to understand the impact of 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 play in the effective use of big-data-driven analytics and in 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 and effectiveness criteria.