20 research outputs found
Towards decolonising computational sciences
This article sets out our perspective on how to begin the journey of
decolonising computational fields, such as data and cognitive sciences. We see
this struggle as requiring two basic steps: a) realisation that the present-day
system has inherited, and still enacts, hostile, conservative, and oppressive
behaviours and principles towards women of colour (WoC); and b) rejection of
the idea that centering individual people is a solution to system-level
problems. The longer we ignore these two steps, the more "our" academic system
maintains its toxic structure, excludes, and harms Black women and other
minoritised groups. This also keeps the door open to discredited pseudoscience,
like eugenics and physiognomy. We propose that grappling with our fields'
histories and heritage holds the key to avoiding mistakes of the past. For
example, initiatives such as "diversity boards" can still be harmful because
they superficially appear reformatory but nonetheless center whiteness and
maintain the status quo. Building on the shoulders of many WoC's work, who have
been paving the way, we hope to advance the dialogue required to build both a
grass-roots and a top-down re-imagining of computational sciences -- including
but not limited to psychology, neuroscience, cognitive science, computer
science, data science, statistics, machine learning, and artificial
intelligence. We aspire for these fields to progress away from their stagnant,
sexist, and racist shared past into carving and maintaining an ecosystem where
both a diverse demographics of researchers and scientific ideas that critically
challenge the status quo are welcomed.Comment: A version of this work will appear in the Danish Journal of Women,
Gender and Research (https://koensforskning.soc.ku.dk/english/kkof/) in
December 202
Towards Decolonising Computational Sciences
This article sets out our perspective on how to begin the journey of decolonising computational fi elds, such as data and cognitive sciences. We see this struggle as requiring two basic steps: a) realisation that the present-day system has inherited, and still enacts, hostile, conservative, and oppressive behaviours and principles towards women of colour; and b) rejection of the idea that centring individual people is a solution to system-level problems. The longer we ignore these two steps, the more âourâ academic system maintains its toxic structure, excludes, and harms Black women and other minoritised groups. This also keeps the door open to discredited pseudoscience, like eugenics and physiognomy. We propose that grappling with our fi eldsâ histories and heritage holds the key to avoiding mistakes of the past. In contrast to, for example, initiatives such as âdiversity boardsâ, which can be harmful because they superfi cially appear reformatory but nonetheless center whiteness and maintain the status quo. Building on the work of many women of colour, we hope to advance the dialogue required to build both a grass-roots and a top-down re-imagining of computational sciences â including but not limited to psychology, neuroscience, cognitive science, computer science, data science, statistics, machine learning, and artifi cial intelligence. We aspire to progress away fromthese fi eldsâ stagnant, sexist, and racist shared past into an ecosystem that welcomes and nurturesdemographically diverse researchers and ideas that critically challenge the status quo
The Lost Art of Mathematical Modelling
We provide a critique of mathematical biology in light of rapid developments
in modern machine learning. We argue that out of the three modelling activities
-- (1) formulating models; (2) analysing models; and (3) fitting or comparing
models to data -- inherent to mathematical biology, researchers currently focus
too much on activity (2) at the cost of (1). This trend, we propose, can be
reversed by realising that any given biological phenomena can be modelled in an
infinite number of different ways, through the adoption of an open/pluralistic
approach. We explain the open approach using fish locomotion as a case study
and illustrate some of the pitfalls -- universalism, creating models of models,
etc. -- that hinder mathematical biology. We then ask how we might rediscover a
lost art: that of creative mathematical modelling.
This article is dedicated to the memory of Edmund Crampin
The Surveillance AI Pipeline
A rapidly growing number of voices have argued that AI research, and computer
vision in particular, is closely tied to mass surveillance. Yet the direct path
from computer vision research to surveillance has remained obscured and
difficult to assess. This study reveals the Surveillance AI pipeline. We obtain
three decades of computer vision research papers and downstream patents (more
than 20,000 documents) and present a rich qualitative and quantitative
analysis. This analysis exposes the nature and extent of the Surveillance AI
pipeline, its institutional roots and evolution, and ongoing patterns of
obfuscation. We first perform an in-depth content analysis of computer vision
papers and downstream patents, identifying and quantifying key features and the
many, often subtly expressed, forms of surveillance that appear. On the basis
of this analysis, we present a topology of Surveillance AI that characterizes
the prevalent targeting of human data, practices of data transferal, and
institutional data use. We find stark evidence of close ties between computer
vision and surveillance. The majority (68%) of annotated computer vision papers
and patents self-report their technology enables data extraction about human
bodies and body parts and even more (90%) enable data extraction about humans
in general
Power to the People? Opportunities and Challenges for Participatory AI
Participatory approaches to artificial intelligence (AI) and machine learning
(ML) are gaining momentum: the increased attention comes partly with the view
that participation opens the gateway to an inclusive, equitable, robust,
responsible and trustworthy AI.Among other benefits, participatory approaches
are essential to understanding and adequately representing the needs, desires
and perspectives of historically marginalized communities. However, there
currently exists lack of clarity on what meaningful participation entails and
what it is expected to do. In this paper we first review participatory
approaches as situated in historical contexts as well as participatory methods
and practices within the AI and ML pipeline. We then introduce three case
studies in participatory AI.Participation holds the potential for beneficial,
emancipatory and empowering technology design, development and deployment while
also being at risk for concerns such as cooptation and conflation with other
activities. We lay out these limitations and concerns and argue that as
participatory AI/ML becomes in vogue, a contextual and nuanced understanding of
the term as well as consideration of who the primary beneficiaries of
participatory activities ought to be constitute crucial factors to realizing
the benefits and opportunities that participation brings.Comment: To appear in the proceeding of EAAMO 202
Automating Ambiguity: Challenges and Pitfalls of Artificial Intelligence
Machine learning (ML) and artificial intelligence (AI) tools increasingly permeate every possible social, political, and economic sphere; sorting, taxonomizing and predicting complex human behaviour and social phenomena. However, from fallacious and naive groundings regarding complex adaptive systems to datasets underlying models, these systems are beset by problems, challenges, and limitations. They remain opaque and unreliable, and fail to consider societal and structural oppressive systems, disproportionately negatively impacting those at the margins of society while benefiting the most powerful. The various challenges, problems and pitfalls of these systems are a hot topic of research in various areas, such as critical data/algorithm studies, science and technology studies (STS), embodied and enactive cognitive science, complexity science, Afro-feminism, and the broadly construed emerging field of Fairness, Accountability, and Transparency (FAccT). Yet, these fields of enquiry often proceed in silos. This thesis weaves together seemingly disparate fields of enquiry to examine core scientific and ethical challenges, pitfalls, and problems of AI. In this thesis I, a) review the historical and cultural ecology from which AI research emerges, b) examine the shaky scientific grounds of machine prediction of complex behaviour illustrating how predicting complex behaviour with precision is impossible in principle, c) audit large scale datasets behind current AI demonstrating how they embed societal historical and structural injustices, d) study the seemingly neutral values of ML research and put forward 67 prominent values underlying ML research, e) examine some of the insidious and worrying applications of computer vision research, and f) put forward a framework for approaching challenges, failures and problems surrounding ML systems as well as alternative ways forward