Abstract
Undoubtedly, the use of algorithms, and Artificial Intelligence (AI) algorithms in particular, has numerous benefits. Fields such as
finance, healthcare, automotive, education, and recruitment, to name a few, have demonstrated successful application of AI algorithms. Conversely, cases of bad algorithms abound and lead to lost revenue, discrimination, disinformation, or even bodily harm.
Currently, we have surpassed the stage of just observing bad algorithms. New European regulations governing AI force organizations to manage the risks introduced by algorithms and convince the public about the proper functioning of algorithms. In this context,
can algorithms be rigorously audited to build public trust and if yes, how? This article aims to answer these questions by building
on an auditing framework for model risk management that controls for the novelty introduced by AI algorithms while connecting AI
algorithm audit with internal audit terminology.
finance, healthcare, automotive, education, and recruitment, to name a few, have demonstrated successful application of AI algorithms. Conversely, cases of bad algorithms abound and lead to lost revenue, discrimination, disinformation, or even bodily harm.
Currently, we have surpassed the stage of just observing bad algorithms. New European regulations governing AI force organizations to manage the risks introduced by algorithms and convince the public about the proper functioning of algorithms. In this context,
can algorithms be rigorously audited to build public trust and if yes, how? This article aims to answer these questions by building
on an auditing framework for model risk management that controls for the novelty introduced by AI algorithms while connecting AI
algorithm audit with internal audit terminology.
| Original language | English |
|---|---|
| Number of pages | 13 |
| Journal | Maandblad voor Accountancy en Bedrijfseconomie |
| DOIs | |
| Publication status | Published - 16 Sept 2022 |