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Towards an Equitable Digital Society: Artificial Intelligence (AI) and Corporate Digital Responsibility (CDR)

Lookup NU author(s): Professor Karen ElliottORCiD, Dr Tasos SpiliotopoulosORCiD, Dr Magdalene Ng, Dr Kovila Coopamootoo, Professor Aad van Moorsel



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


In the digital era, we witness the increasing use of artificial intelligence (AI) to solve problems, while improving productivity and efficiency. Yet, inevitably costs are involved with delegating power to algorithmically based systems, some of whose workings are opaque and unobservable and thus termed the “black box”. Central to understanding the “black box” is to acknowledge that the algorithm is not mendaciously undertaking this action; it is simply using the recombination afforded to scaled computable machine learning algorithms. But an algorithm with arbitrary precision can easily reconstruct those characteristics and make lifechanging decisions, particularly in financial services (credit scoring, risk assessment, etc.), and it could be difficult to reconstruct, if this was done in a fair manner reflecting the values of society. If we permit AI to make life-changing decisions, what are the opportunity costs, data trade-offs, and implications for social, economic, technical, legal, and environmental systems? We find that over 160 ethical AI principles exist, advocating organisations to act responsibly to avoid causing digital societal harms. This maelstrom of guidance, none of which is compulsory, serves to confuse, as opposed to guide. We need to think carefully about how we implement these algorithms, the delegation of decisions and data usage, in the absence of human oversight and AI governance. The paper seeks to harmonise and align approaches, illustrating the opportunities and threats of AI, while raising awareness of Corporate Digital Responsibility (CDR) as a potential collaborative mechanism to demystify governance complexityand to establish an equitable digital society.

Publication metadata

Author(s): Elliott K, Price R, Shaw P, Spiliotopoulos T, Ng M, Coopamootoo K, van Moorsel A

Publication type: Article

Publication status: Published

Journal: Society

Year: 2021

Volume: 58

Pages: 179-188

Online publication date: 14/06/2021

Acceptance date: 27/05/2021

Date deposited: 18/06/2021

ISSN (print): 0147-2011

ISSN (electronic): 1936-4725

Publisher: Springer Nature


DOI: 10.1007/s12115-021-00594-8


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