Responsible Data Science

Excerpt

An increasing fraction of research reported in BISE (Business & Information Systems Engineering) is data-driven. This is not surprising since torrents of data are vigorously changing the way we do business, socialize, conduct research, and govern society (Hilbert and Lopez 2011; Manyika et al. 2011; White House 2016). Data are collected on everything, at every time, and in every place. The Internet of Things (IoT) is rapidly expanding, with our homes, cars, and cities becoming “smart” by using the collected data in novel ways. These developments are also changing the way scientific research is performed. Model-driven approaches are supplemented with data-driven approaches. For example, genomics and evidence-based medicine are revolutionizing the understanding and treatment of diseases. From an epistemological point of view, data-driven approaches follow the logic of the new experimentalism (Mayo 1996; Chalmers 2013) in which knowledge is derived from experimental observations, not theory. Information systems which exploit the combination of data availability and powerful data science techniques dramatically improve our lives by enabling new services and products, while improving their efficiency and quality. However, there are also great concerns about the use of data (van der Aalst 2016a, b). Increasingly, customers, patients, and other stakeholders are concerned about irresponsible data use. Automated data decisions may be unfair or non-transparent. Confidential data may be shared unintentionally or abused by third parties. Each step in the “data science pipeline” (from raw data to insights and knowledge) may create inaccuracies, e.g., if the data used to learn a model reflects existing social biases, the algorithm is likely to incorporate these biases. These concerns could lead to resistance against the large-scale use of data and make it impossible to reap the benefits of data science. Rather than to avoid the use of data altogether, we strongly believe that data science techniques, infrastructures and approaches need be made responsible by design. Over the last year the first author has been leading a Dutch initiative called Responsible Data Science (RDS), cf. http://​www.​responsibledatas​cience.​org/​. In the context of RDS, there are research projects and regular meetings to discuss new ways to make data science more responsible. We believe that the insights obtained from these discussions are also relevant for the BISE community. The data-driven nature of today’s (business) information systems makes it essential to incorporate safeguards against irresponsible data use already in the requirements and design phases. …