Humanist Discussion Group

Humanist Archives: Jan. 19, 2022, 7:59 a.m. Humanist 35.473 - events: machine learning & AI; algorithmic technologies

              Humanist Discussion Group, Vol. 35, No. 473.
        Department of Digital Humanities, University of Cologne
                      Hosted by DH-Cologne
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    [1]    From: AEOLIAN Project <>
           Subject: AEOLIAN Workshop 3 (126)

    [2]    From: Dmitry Muravyov <>
           Subject: CfP: Blackboxed Futures: Multiple Temporalities of Algorithmic Technologies (Online Symposium) (65)

        Date: 2022-01-18 16:01:25+00:00
        From: AEOLIAN Project <>
        Subject: AEOLIAN Workshop 3

Dear List Members,

The AEOLIAN Network (Artificial Intelligence for Cultural Organisations) is
delighted to announce a Call for Participants in our next online workshop, which
will be hosted on ZOOM by Durham University on Thursday 27th January from 2pm to
4:30pm and Friday 28th January from 3pm to 5:30pm (UK time).

The workshop is free to attend, but registration is essential to receive the
joining information. Please visit our Events page to register:

Title: “What challenges do Machine Learning and AI raise in terms of privacy,
ethics, research integrity, reproducibility, and bias?”

The workshop, the third in a series, looks at privacy and the uses of AI. It
asks how far we, as users and as information professionals, trust AI both in
terms of how transparently algorithms are constructed, and what their creators
say about them. How far are we able to cut through promotional hype and evaluate
the affordances of AI for use in cultural heritage? How do we allow for
potential biases in the construction of algorithms? How should we advise
potential new users of such technologies?

The workshop will consist of two Keynote talks, one on each day, followed by
open discussion sessions, where participants will be encouraged to discuss
questions, and make recommendations for future action.

The Keynotes will be Jason R. Baron (University of Maryland) and Alexandra
Cristea (Durham University).
Alexandra Cristea (Thursday 27th January)

Bio: Alexandra I. Cristea is Professor, Deputy Head, Director of Research and
Head of the Artificial Intelligence in Human Systems research group in the
Department of Computer Science at Durham University. She is Advisory Board
Member at the Ustinov College, N8 CIR Digital Humanities team lead for Durham.
Her research includes web science, learning analytics, user modelling and
personalisation, semantic web, social web, authoring, and has written over 300
papers on these subjects. Her work on frameworks for adaptive systems has
influenced many researchers and is highly cited. She was classified within the
top 50 researchers in the world in the area of educational computer-based
research according to Microsoft Research. Recently she has taken giving back to
the community to a different level, leading the Empowering women in science
through mentoring and exchanging experiences (2021-22) (UK-Brazil Gender
Equality Partnership funded by the British Council), and co-leading the TechUP
project series (Bootcamp 2021) (2019-2020: training 100 women in computer
science from various (BAME) backgrounds).

Title: Bias in AI.

Abstract: Artificial Intelligence is a thriving area in Computer Science.
Especially trending is the sub-area of Machine Learning and Deep Learning,
including Data Analytics. However, the latter comes often with various forms of
bias. Bias in AI can be introduced in many forms, from data to methods and
algorithms, and it negatively affects people as well as research quality. It
also impacts upon an increasing amount of areas, including sensitive ones, such
as healthcare, law, criminal justice, hiring. Thus, an important task for
researchers is to use AI to identify and reduce (human or machine) biases, as
well as improve AI systems, to prevent introducing and perpetuating bias.
Aspects of Bias in AI range:
•              from statistical/theoretical perspectives –where bias should be
avoided with new algorithmic solutions, methodologically correct procedures
(e.g., bias induced by overlapping training/test set, historically inaccurate
time series, average accuracy results only in classification); sensitivity
analysis (including k-anonymity, l-diversity, t-closeness, k-safety,
k-confusability, t-plausibility) for structured/unstructured data, or ways of
quantifying uncertainty in deep learning, e.g., via adversarial learning,
generative models, invertible networks, meta-learning nets.
•              to human perspectives – where specific types of bias introduced
by data or methodology can do harm, such as in implicit racial, ethnic, gender,
ideological biases.
The former perspectives are to produce correct or optimised results, the latter
are to lead to conversational explanations and explainable AI, in view of GDPR
and increasing ethical concerns, and the move from symbolic AI to sub-symbolic
(deep) representations, with no direct answer to the classic AI questions of
‘Why’ and ‘How’. This includes the novel field of Machine Teaching, expanding on
the classical field of knowledge extraction from (shallow or, more recently,
deep) Neural Networks. This area should lead to novel insights into
accountability of AI. This talk will consider some of these aspects of Bias in
AI and lead to thoughts and possibly a wider discussion on the social impact of
Jason R. Baron (Friday 28th January)

Bio: Jason R. Baron is a Professor of the Practice in the College of Information
Studies at the University of Maryland.  Previously, he served as the first
appointed director of litigation at the US National Archives and Records
Administration, and before that as a trial lawyer and senior counsel at the
Department of Justice. In those capacities, he acted as lead counsel on landmark
lawsuits involving the preservation of White House email, and also played a
leading role in improving federal electronic recordkeeping policies. Mr. Baron
is a recipient of the international Emmett Leahy Award for his achievements in
records and information management, including co-founding a Legal Track at the
US National Institute of Standards and Technology Text Retrieval Conference
(TREC), to evaluate the efficacy of machine learning methods as used in legal
practice.  He served as lead editor of the book Perspectives on Predictive
Coding and Other Advanced Search Methods for the Legal Practitioner (2016), and
is the author of over 100 published articles on e-discovery, electronic
recordkeeping, and information governance. Mr. Baron received his B.A. magna cum
laude with honors from Wesleyan University, and his J.D. from the Boston
University School of Law.

Title: Challenges in Providing Access To The Digital Universe: Are Algorithms
The Answer?

Abstract:  The sheer volume of electronic and digital records in archives and
other cultural institutions is already overwhelming the ability to provide
meaningful access to patrons and the public at large.  In the United States, the
National Archives currently holds over a billion pages of White House emails and
attachments going back to the 1980s, only a fraction of which are publicly
available.  Machine learning techniques, properly applied, may be useful in
searching for relevant records and filtering those records for personal
information and other sensitive content.  But what are the privacy-related and
other obstacles we presently encounter in using AI methods?  Can we trust the
algorithms used to open up archival collections?  And if we don’t trust AI, does
going digital mean for all practical purposes going dark for many decades to

Thank you,

Katie Aske

Dr Katherine Aske (she/her)
Research Assistant, AEOLIAN<> and
AURA<> Projects
School of Social Sciences and Humanities, Loughborough University
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        Date: 2022-01-18 10:20:14+00:00
        From: Dmitry Muravyov <>
        Subject: CfP: Blackboxed Futures: Multiple Temporalities of Algorithmic Technologies (Online Symposium)

Dear all,

We are inviting you to submit your abstracts to the online symposium on
algorithms, temporalities, and futures. See the CfP below.

Call for Papers: Blackboxed Futures: Multiple Temporalities of Algorithmic
Technologies (Online Symposium, March 19)

Algorithmic technologies are nowadays proliferating in various sectors of
the economy and, more generally, in society. Yet, while their widespread
development already occupies several areas of contemporary life, their
material configuration often remains opaque and difficult to comprehend,
especially when it comes to how algorithms shape the futures of people and
societies at large. Often, algorithms and AI technologies are conceived by
their users and creators as “magic” that is beyond comprehension — an
understanding that has a range of political and cultural implications for
society (Campolo & Crawford, 2020) and has been consequently recognized in
the theorizations of economy and politics (Pignarre and Stengers, 2012).
Questions of vital scholarly and political importance emerge – what
future(s) do algorithmic technologies offer for society, who is included in
them and left out, how they can be scrutinized and resisted? Do we witness
a “temporal stasis in an age of automated media” (Andrejevic, Dencik &
Trere, 2020) or a “speculative time-complex” (Avanessian & Malik, 2016)?

In this symposium, we invite scholars from critical media, cultural,
science and technology studies, as well as adjacent fields, to further
reflect upon the imagined futures of algorithmic technologies and multiple
temporalities enrolled in their continuous enactment. We suggest focusing
on algorithmic temporalities by thinking of them both in terms of related
discursive practices and issues of algorithmic design. Addressing both
theoretical and empirical matters of algorithmic temporalities, this
symposium aims to shed light on how our thinking about time vis-a-vis
algorithmic technologies spreads — or meets resistance — in different
social and political contexts.

The symposium will take place online on *March 19*.  We invite papers that
reflect (but are by no means limited) upon the following themes:

– discussions of multiple temporalities related to algorithmic technologies
and the governance regimes of which they are a part of

– grassroots and top-down imaginaries of futures as they pertain to the
(mis)use of algorithms

– historical accounts exploring interconnections between time and
algorithmic technologies

– political-economic accounts of temporal regimes associated with
algorithmic technologies

– decolonial computation and decolonization of algorithms

– creative and speculative approaches seeking to address the issues of
algorithmic futures

Please, send your abstracts (250-500 words) and a short bio to the
following email: no later than February 20. The
results of the selection process will be published on March 1.

Postgraduate Symposium, Critical Media Studies, HSE, Moscow
Dmitry Muravyov, Artyom Kolganov, Aleksey Pereyaslov, Elizaveta Panina,
Yujie Chi

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