Humanist Discussion Group

Humanist Archives: March 9, 2024, 6 a.m. Humanist 37.484 - pubs cfp: failed epistemologies of AI

				
              Humanist Discussion Group, Vol. 37, No. 484.
        Department of Digital Humanities, University of Cologne
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        Date: 2024-03-08 20:12:15+00:00
        From: Philip Di Salvo <phildisa@gmail.com>
        Subject: CfP - The Failed Epistemologies of AI? Making Sense of AI Errors, Failures and their Impacts on Society

Dear colleagues,

I’m happy to share this CfP for a special issues in the Italian journal
Annals of the Fondazione Luigi Einaudi.
<https://www.annalsfondazioneluigieinaudi.it> 
I believe it could be of interest for many Internet studies scholars.

CfP: The Failed Epistemologies of AI? Making Sense of AI Errors, Failures
and their Impacts on Society – Annals of the Fondazione Luigi Einaudi

Editors:

    - Prof. Veronica Barassi (veronica.barassi@unisg.ch)
    - Dr. Philip Di Salvo (philip.disalvo@unisg.ch)

School of Humanities and Social Sciences
University of St. Gallen

Generative artificial intelligence tools and large language models are
gaining a prominent space in our society. Probably for the first time in
history, humans have now to relate and interact with technological systems
capable of producing and generating new content and knowledge mimicking
humans’ imagination, speech, and behaviors in ways that was not possible
before. This new state of things brings inevitably profound consequences
and potential sea changes for numerous social, scientific, and cultural
fields raising epistemological, ethical, political economical and
philosophical questions about the epistemologies of AI and the processes of
knowledge production of these systems. The race for AI innovation is being
framed with reference to the ‘superintelligence’ of our machines, their
processing power, their ability to learn and generate knowledge. In public
debate, AI technologies are admired for their powers, and feared for their
threats. Yet, we are increasingly confronted with the fact that these
machines make errors and mistakes, they are fallible and inaccurate, and
they are often culturally biased. From Generative AI technologies that
‘hallucinate’ and invent facts to predictive policing technologies that
lead to wrongful arrests, our world is quickly coming to terms with the
fact that the AI we are building is not only astonishing and incredibly
powerful, but often unable to understand the complexity of our human
experience and our cultural worlds. Research has shown that AI errors and
their problematic outcomes can’t be considered as mere coding glitches, but
as the direct expression of the structural inequalities of our societies
and they confront us with critical questions about our supposed
anthropocentric position as knowledge-creators.

The aim of this special issue is to gather scholars coming from different
fields of the social sciences and humanities to investigate how artificial
intelligence systems are challenging epistemological assumptions in various
societal areas and how the failures of such systems are impacting on
knowledge creation and diffusion in their areas of interest. Overall, the
special issue aims at overcoming dominant and hyped takes and narratives
around AI and its supposed (super)powers, and critically reflect on how we
can identify and learn how to coexist with the limitations of AI driven
knowledge production.

Possible topics include, but are not restricted to:

    - Impacts of AI Errors and Failures: Exploring the ways in which AI
    failures, inaccuracies and errors in AI impact human understanding,
    decision-making, and interpersonal relationships.
    - Cultural Limitations of AI Knowledge: Investigating how AI systems
    intersect with cultural norms, values, and belief systems, and assessing
    the limits to cultural diversity and inclusivity of these technologies.
    - Fake News and DeepFakes: Generative AI, democracy, disinformation, and
    the public sphere
    - Social Construction of AI Truth: Investigating how AI systems
    construct and perpetuate particular truths, shaping public
perceptions and
    influencing social narratives.
    - Bias and Discrimination in AI: Analyzing how inherent biases in
    training data, algorithms, and decision-making processes contribute to
    perpetuating social inequalities and reinforcing existing power
structures.

Submission procedure

    - We invite interested scholars to submit an abstract (300 words, 3 to
    5 keywords) by 24th of April, 2024 to
    editors@annalsfondazioneluigieinaudi.it, veronica.barassi@unisg.ch;
    philip.disalvo@unisg.ch.
    - The issue’s editors will review the abstracts and send
notifications of
    acceptance or rejection by the 8th of June, 2024.
    - The special issue will include up to 8 contributions among those
    received through the call for papers. Final papers (about 8000 words)
    will be due on 8th of December 2024. Please note that acceptance of
    abstracts does not necessarily imply acceptance of the paper for the
    special issue. For further information (including the aim and scope
of the
    Journal), please refer to the Journal’s website.


Best regards and thanks for your attention,


--

Philip Di Salvo, PhD
Researcher
<https://mcm.unisg.ch/en/personenverzeichnis/9479a240-e20b-4b1e-aaa4-249f2ca42eab>
| journalist <https://twitter.com/philipdisalvo>
Zürich


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