As transparency is integrally linked to trust and accountability, an IT system should help users understand how it is designed and how it works by making its inner logics and functions visible.
- Find technological and social mechanisms to share the inner-workings of your system with users and those being served by its use.
- Help users understand the inner logics and functions of your system, including the classification systems, taxonomy, access controls, etc.
- Make users and how users interact with an IT system visible.
Transparency may have contradictory meanings. In computer science and IT studies, it can refer to a condition of invisibility, namely that a system can be used without question or need to think about the processes within. In ethics, transparency means to have the inner workings of a process visible so that anyone using it can understand the steps involved and their implications.
Rather than an ethical principle, some theorists argue that transparency is a key ethically “impairing” or “enabling” factor than impacts on principles such as accountability, consent, privacy, anonymity hence allowing more control over our data (Turilli and Floridi 2009). Others argue that regardless of its presence in data regulations, transparency is often naively treated as a panacea for ethical issues arising from new technologies (Crawford 2016; Neyland 2016; Raymond 2014).
According to the European Data Protection Supervisor (EDPS), transparency is one of the essential elements for developing responsible and sustainable data-driven technologies urging that “individuals must be given clear information on what data are processed, including data observed or inferred about them; better informed on how and for what purposes their information is used, including the logic used in algorithms to determine assumptions and predictions about them.” (EDPS 2015). It is widely claimed that the GDPR is putting transparency via a ‘right to explanation’ clause at the heart of data protection. However, doubts are raised about the legal basis and feasibility of such a right highlighting the limitations of the GDPR’s mandate (see Wachter et al 2017a,b; Edwards and Veale 2017).
Ananny, M. and Crawford, K. (2016). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society: 1-17.
Crawford K (2016) Can an algorithm be agonistic? Ten scenes from life in calculated publics. Science, Technology & Human Values 41(1): 77–92.
Edwards, Lilian and Veale, Michael (2017) Slave to the algorithm? Why a ‘right to an explanation’ is probably not the remedy you are looking for. Duke Law and Technology Review, 16 (1). pp. 1-65. ISSN 2328-9600 [Link]
European Data Protection Supervisor (EDPS), Meeting the challenges of Big Data: A Call for Transparency, User Control, Data Protection by Design and Accountability.[OPINION 7/2015] (2015).
General Data Protection Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC [Link]
Neyland D (2016) Bearing accountable witness to the ethical algorithmic system. Science, Technology & Human Values 41(1): 50–76.
Raymond A (2014) The dilemma of private justice systems: Big Data sources, the cloud and predictive analytics. Northwestern Journal of International Law & Business, Forthcoming. Kelley School of Business Research Paper No. 2014-22.
Perng, S.Y. and Büscher, M., (2015). Uncertainty and transparency: augmenting modelling and prediction for crisis response. The 12th International Conference on Information Systems for Crisis Response and Management (ISCRAM) [Link]
Wachter, S., Mittelstadt, B., & Floridi, L. (2017a). Transparent, explainable, and accountable AI for robotics. Science Robotics, 2(6) [Link]
Wachter, S., Mittelstadt, B. and Floridi, L. (2017b) Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. Int. Data Priv. Law [DOI]
Weitzner, D. J., Abelson, H., Berners-Lee, T., Hanson, C., Hendler, J., Kagal, L., Waterman, K.K. (2006). Transparent accountable data mining: New strategies for privacy protection. MIT-CSAIL Technical Report. Cambridge, MA: MIT. [Link]