IEEE Technology and Society Magazine - June 2022 - 84

Commentary
Commentary
Responsibility, Recourse, and Redress:
A Focus on the Three R's of AI Ethics
Allison Gardner
School of Computing and Mathematics,
Keele University, Newcastle under Lyme,
Staffordshire ST5 4JH, U.K.
 In August 2020, students took to the streets
of London in a mass protest over their A-level results
(that determine entry to university), chanting " F*ck
the algorithm. " Their A-level grades had been determined
by an algorithm that depended on past school
performance, a system of teacher ratings, and for
smaller student groups, the use of Centre Assessed
Grades. Commendably Ofqual (the U.K. Government
Office of Qualifications and Examinations
Regulation) were transparent in their use of the algorithm
so that affected stakeholders were informed
of the algorithm's existence and the key details of
how it was developed. The design of the data set
produced an algorithm with biased outputs, which
hardcoded preexisting educational, policy, and societal
biases. Due to the large numbers of students, the
single " results day " output and the wider interests
of schools, teachers, parents, and universities, the
unfairness of the outcome was magnified. Add to
this that the students were, by definition, academically
proficient individuals, many of whom derived
from privileged and supportive backgrounds, which
meant that they had the awareness and capacity to
challenge. Hence, the impact of this biased algorithm
was not received silently and so it was hard to deny,
deflect, and dismiss. However, this is not always the
case, and many systems are deployed with similar
biases and harmful outcomes but the impact is dissipated
over time and individuals, and therefore not
so immediately obvious and easy to challenge. This
Digital Object Identifier 10.1109/MTS.2022.3173342
Date of current version: 14 June 2022.
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0278-0097/22©2022 IEEE
is particularly the case when the people impacted
do not have the advantages that enabled the A-level
students to challenge.
Consider the hypothetical case of an individual
subject to an algorithmic system decision that
decides whether disability support is denied or canceled.
Let us assume for this purpose that the final
decision is administered by a low-grade worker
rather than a qualified clinician or social worker.
The implication here is that the administrator is
subject to automation bias and thus defaults to the
artificial intelligence (AI) system, rendering the final
decision essentially automated. Such an individual
applying for support may not know that they were
subject to an automated decision-making system
and may not have the capacity, whether due to lack
of knowledge, wellness, finance, or time to investigate,
to seek advocacy or know what questions to
ask. Too busy trying to survive, they do not make a
fuss. They cannot challenge what they do not know
to exist. These systems can fly under the radar and
cause significant harm to citizens.
This statement explores the concepts of responsibility,
recourse, and redress that should, but frequently
do not enable citizens to exercise their rights
and to be fairly treated. It aims to define the concept
of responsible AI in a manner understandable to the
layperson, devoid of opaqueness, and linked to existing
human rights and equality laws. In reference to
recourse and redress, the statement argues that these
two areas are currently not adequately practiced
and are a neglected area in relation to AI ethics'
principles, standards, and regulations. As such, the
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