11 research outputs found
Does the End Justify the Means?:On the Moral Justification of Fairness-Aware Machine Learning
Despite an abundance of fairness-aware machine learning (fair-ml) algorithms,
the moral justification of how these algorithms enforce fairness metrics is
largely unexplored. The goal of this paper is to elicit the moral implications
of a fair-ml algorithm. To this end, we first consider the moral justification
of the fairness metrics for which the algorithm optimizes. We present an
extension of previous work to arrive at three propositions that can justify the
fairness metrics. Different from previous work, our extension highlights that
the consequences of predicted outcomes are important for judging fairness. We
draw from the extended framework and empirical ethics to identify moral
implications of the fair-ml algorithm. We focus on the two optimization
strategies inherent to the algorithm: group-specific decision thresholds and
randomized decision thresholds. We argue that the justification of the
algorithm can differ depending on one's assumptions about the (social) context
in which the algorithm is applied - even if the associated fairness metric is
the same. Finally, we sketch paths for future work towards a more complete
evaluation of fair-ml algorithms, beyond their direct optimization objectives
Using a Cognitive Architecture to consider antiblackness in design and development of AI systems
How might we use cognitive modeling to consider the ways in which
antiblackness, and racism more broadly, impact the design and development of AI
systems? We provide a discussion and an example towards an answer to this
question. We use the ACT-R/{\Phi} cognitive architecture and an existing
knowledge graph system, ConceptNet, to consider this question not only from a
cognitive and sociocultural perspective, but also from a physiological
perspective. In addition to using a cognitive modeling as a means to explore
how antiblackness may manifest in the design and development of AI systems
(particularly from a software engineering perspective), we also introduce
connections between antiblackness, the Human, and computational cognitive
modeling. We argue that the typical eschewing of sociocultural processes and
knowledge structures in cognitive architectures and cognitive modeling
implicitly furthers a colorblind approach to cognitive modeling and hides
sociocultural context that is always present in human behavior and affects
cognitive processes.Comment: To be published in ICCM Conference proceedings. 8 Pages, 1 figur
Streamlining models with explanations in the learning loop
Several explainable AI methods allow a Machine Learning user to get insights
on the classification process of a black-box model in the form of local linear
explanations. With such information, the user can judge which features are
locally relevant for the classification outcome, and get an understanding of
how the model reasons. Standard supervised learning processes are purely driven
by the original features and target labels, without any feedback loop informed
by the local relevance of the features identified by the post-hoc explanations.
In this paper, we exploit this newly obtained information to design a feature
engineering phase, where we combine explanations with feature values. To do so,
we develop two different strategies, named Iterative Dataset Weighting and
Targeted Replacement Values, which generate streamlined models that better
mimic the explanation process presented to the user. We show how these
streamlined models compare to the original black-box classifiers, in terms of
accuracy and compactness of the newly produced explanations.Comment: 16 pages, 10 figures, available repositor
Normative Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions
The rapid adoption of artificial intelligence (AI) necessitates careful
analysis of its ethical implications. In addressing ethics and fairness
implications, it is important to examine the whole range of ethically relevant
features rather than looking at individual agents alone. This can be
accomplished by shifting perspective to the systems in which agents are
embedded, which is encapsulated in the macro ethics of sociotechnical systems
(STS). Through the lens of macro ethics, the governance of systems - which is
where participants try to promote outcomes and norms which reflect their values
- is key. However, multiple-user social dilemmas arise in an STS when
stakeholders of the STS have different value preferences or when norms in the
STS conflict. To develop equitable governance which meets the needs of
different stakeholders, and resolve these dilemmas in satisfactory ways with a
higher goal of fairness, we need to integrate a variety of normative ethical
principles in reasoning. Normative ethical principles are understood as
operationalizable rules inferred from philosophical theories. A taxonomy of
ethical principles is thus beneficial to enable practitioners to utilise them
in reasoning.
This work develops a taxonomy of normative ethical principles which can be
operationalized in the governance of STS. We identify an array of ethical
principles, with 25 nodes on the taxonomy tree. We describe the ways in which
each principle has previously been operationalized, and suggest how the
operationalization of principles may be applied to the macro ethics of STS. We
further explain potential difficulties that may arise with each principle. We
envision this taxonomy will facilitate the development of methodologies to
incorporate ethical principles in reasoning capacities for governing equitable
STS
Manifestations of Xenophobia in AI Systems
Xenophobia is one of the key drivers of marginalisation, discrimination, and
conflict, yet many prominent machine learning (ML) fairness frameworks fail to
comprehensively measure or mitigate the resulting xenophobic harms. Here we aim
to bridge this conceptual gap and help facilitate safe and ethical design of
artificial intelligence (AI) solutions. We ground our analysis of the impact of
xenophobia by first identifying distinct types of xenophobic harms, and then
applying this framework across a number of prominent AI application domains,
reviewing the potential interplay between AI and xenophobia on social media and
recommendation systems, healthcare, immigration, employment, as well as biases
in large pre-trained models. These help inform our recommendations towards an
inclusive, xenophilic design of future AI systems