Ethical Implications of Predictive Risk Intelligence

  • Tilimbe Jiya De Montfort University

Abstract

This paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. Thepaper covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews.Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelligence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as social media sites. Also, there are issues relating to the transparency and accountability of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In response to these issues,the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Responsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees.This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society.

Published
2019-02-20
How to Cite
Jiya, T. (2019). Ethical Implications of Predictive Risk Intelligence. ORBIT Journal, 2(2). https://doi.org/10.29297/orbit.v2i2.112