Machine Capacity of Judgment: An interdisciplinary approach for making machine intelligence transparent to end-users
Andrei Ciortea and
Technology in Society, 2022, vol. 71, issue C
Intelligent machines surprise us with unexpected behaviors, giving rise to the question of whether such machines exhibit autonomous judgment. With judgment comes (the allocation of) responsibility. While it can be dangerous or misplaced to shift responsibility from humans to intelligent machines, current frameworks to think about responsible and transparent distribution of responsibility between all involved stakeholders are lacking. A more granular understanding of the autonomy exhibited by intelligent machines is needed to promote a more nuanced public discussion and allow laypersons as well as legal experts to think about, categorize, and differentiate among the capacities of artificial agents when distributing responsibility. To tackle this issue, we propose criteria that would support people in assessing the Machine Capacity of Judgment (MCOJ) of artificial agents. We conceive MCOJ drawing from the use of Human Capacity of Judgment (HCOJ) in the legal discourse, where HCOJ criteria are legal abstractions to assess when decision-making and judgment by humans must lead to legally binding actions or inactions under the law. In this article, we show in what way these criteria can be transferred to machines.
Keywords: Machine Capacity of Judgment; Responsibility; Transparency; Agency; Artificial agents; Autonomy (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:71:y:2022:i:c:s0160791x22002299
Access Statistics for this article
Technology in Society is currently edited by Charla Griffy-Brown
More articles in Technology in Society from Elsevier
Bibliographic data for series maintained by Catherine Liu ().