IEEE Technology and Society Magazine - June 2022 - 86

Commentary
specifically. Hence, good practice would be to
ensure that contingency plans are in place when
such outcomes are identified. It is here that we redirect
the focus on the remaining two of the three " R "
of recourse and redress. One aspect of responsible
AI is that stakeholders involved in developing and
deploying the AI system should be answerable for
any unexpected outcomes. The definition in the previous
section, however, stops short from determining
to whom such stakeholders are answerable.
Ultimately, when addressing the question from
a human rights approach, a responsible AI system
should be answerable to the individuals that are
impacted. It is here that we address the importance
of transparency and that been subject to algorithmic
outcomes that are unfair and harmful. Such a process
could be termed informed recourse in that it informs
those adversely affected how they can obtain help
and remediation of unfair consequences. In choosing
to develop and deploy a system, it is therefore
incumbent on developers to design a parallel process
whereby impacted stakeholders can seek
recourse.
In the context of AI, recourse can be determined
as the mechanisms by which a stakeholder (either
influencing or impacted) informs responsible persons
or organizations of an unexpected, unfair, or
unsafe outcome [7]. A literal interpretation of the
term is outlining in a public and transparent manner
the " person, organization, or course of action
that can be turned to for help or protection " [8]. In
the example of a denial of service or payment, there
must be clear guidance identifying the responsible
stakeholders, how to contact them, and the right,
under relevant regulation, to challenge and ask for
reconsideration of the decision process and rectification
if an error has been committed.
Ustun et al. [9] define actionable recourse as " the
ability of a person to obtain a desired outcome from
an AI prediction model. " In explaining further, they
indicate that a model that provides recourse should
allow an impacted person, for example, someone
who has been denied a loan, to change the input
variables of the model in a manner that guarantees
an equitable likelihood of approval. They further
indicate that models that allocate goods or services
should be universally accessible to all citizens and
built in a manner that provides recourse [9]. An
important point is that transparency and explainability
do not in themselves classify as recourse
86
mechanisms though they do provide information
that enables the correct formation of questions and
challenges by which recourse can be requested and
actioned.
The design of a system that enables actionable
recourse supports a human-centered approach to
AI development and actively embeds the concept of
human agency. Developing technical solutions and
incorporating human agency for actionable recourse
should not produce a process that is difficult or
onerous and thus inhibits the citizen from seeking
recourse. This is particularly important as the citizen
or group of citizens that are subject to unfair outcomes
are frequently those that already experience
additional societal hurdles to achieving equitable
treatment. A good example of mechanisms intended
to empower users but becomes burdensome are the
processes that some cookie consent banners employ
that require users to individually reject hundreds of
advertising cookies rather than a simple " yes/no, "
" accept all, " or " reject all " [10]. Such mechanisms
or " dark patterns " are not responsible and indicate
a lack of trustworthiness of those abusing the spirit
of the law [11].
Defining technical solutions for recourse in AI systems
is not a fully defined field, indeed a neglected
aspect of the AI ethics principles and standards.
Greater focus should be given to these approaches.
Additionally, note that actionable recourse only provides
a local solution to a singular output and should
in and of itself not be considered a full solution for
addressing algorithmic bias and unfair outcomes.
Such technical approaches do not address the
underlying systemic issues relating to structural bias
and unfairness that occur within society and business
culture particularly and do not address necessarily
the impact of the original AI outcome, which
in some cases can be severe [12].
Finally, we can consider recourse to a legally
protected interest. Legal recourse may be available
when other forms of recourse do not exist or have
been denied or exhausted. Although legal recourse
is typically found in legislation, regulation, or
common law, other quasi-legal mechanisms for
recourse may exist, such as whistleblowing, that
enables open or anonymous reporting of unethical
and unsafe processes beyond standard complaints
procedures [13]. Legal recourse is often linked to
claims for recompense of harms caused by unfair
outcomes. These actions extend the concept of
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