The Long Road Toward Tracking the Trackers and De-biasing: A Consensus on Shaking the Black Box and Freeing From Bias

George Bouchagiar

Review of European Studies, 2019, vol. 11, issue 1, 27

Abstract: Automated decision making is both promising and threatening. Processing the biggest data possible may lead to societal advances but also violate human rights. There is, then, an acute need to protect individuals without impeding major benefits. Non-human agents may be biased; and they may not lend themselves to easy explanations. Instead of focusing on interpreting models, there seems to be a shift toward a concept of risk assessments. Opaque systems are aimed at predicting, or forecasting, future situations. This challenges human values and ethical principles. Even though incorporating ethics in machines is an old subject of legal discussion, consensus has not yet been reached; for theories and values may be controversial. This paper examines whether there could be an agreement on fundamental principles. A commonly understood basis could allow for fair and proportionate mechanisms to address crucial aspects of partiality and opacity in automated decision making. It could trigger a shift toward a concept of ‘tracking the trackers’ and a discussion on a ‘right to an unbiased decision maker’.

Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.ccsenet.org/journal/index.php/res/article/download/0/0/38253/38771 (application/pdf)
http://www.ccsenet.org/journal/index.php/res/article/view/0/38253 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:resjnl:v:11:y:2019:i:1:p:27

Access Statistics for this article

More articles in Review of European Studies from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().