=Paper=
{{Paper
|id=Vol-3319/invited1
|storemode=property
|title=Ethics and Gender for Responsible Research and Innovation in AI (extended abstract)
|pdfUrl=https://ceur-ws.org/Vol-3319/invited1.pdf
|volume=Vol-3319
|authors=Francesca Alessandra Lisi
|dblpUrl=https://dblp.org/rec/conf/aiia/Lisi22
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==Ethics and Gender for Responsible Research and Innovation in AI (extended abstract)==
https://ceur-ws.org/Vol-3319/invited1.pdf
Ethics and Gender for
Responsible Research and Innovation in AI
Francesca Alessandra Lisi1,*
1
Dipartimento di Informatica, University of Bari “Aldo Moro" - Via E. Orabona 4, Bari, 70125, Italy
Abstract
This short paper is an extended abstract of the invited talk I gave at the BEWARE 2022 workshop. It
addresses two themes that are common to the Responsible Research and Innovation approach, and
to the EU guidelines for a Trustworthy Artificial Intelligence: Ethics and Gender. The talk (and this
paper) was intended to account for some recent research and dissemination activities concerning these
themes, to explore the interplay between the two, and to lay new foundations for AI Ethics inspired by
contemporary feminist theories.
Keywords
AI Ethics, Gender Studies, Responsible Research and Innovation, Trustworthy AI, Feminism
1. The need for a responsible approach to AI
Artificial Intelligence (AI) is the latest technological evolution which is transforming the global
economy, is playing a major role in the “Fourth Industrial Revolution”, and is deeply changing
our society. As such, it brings so-called disruptive technologies, namely those technologies
that have the potential to supersede an older process, product, or habit. In order to reveal
how these technologies can be socially and economically transformative, and to identify the
potential downsides of the ’disruption’ it might bring about, it is necessary to take a responsible
approach to them. Responsible Research and Innovation (RRI) is just the approach, promoted
by European Commission’s Science in Society programme in the context of the Horizon 2020
Strategy, that anticipates and assesses potential implications and societal expectations with
regard to research and innovation, with the aim to foster the design of inclusive and sustainable
research and innovation. RRI involves 5 thematic elements: Ethics, Gender, Public Engagement,
Education, Open Access. These concern holding research to high ethical standards, ensuring
gender equality in the scientific community, investing policy-makers with the responsibility
to avoid harmful effects of innovation, engaging the communities affected by innovation and
ensuring that they have the knowledge necessary to understand the implications by furthering
science education and the accessibility of scientific knowledge.
1st Workshop on Bias, Ethical AI, Explainability and the role of Logic and Logic Programming, BEWARE-22, co-located
with AIxIA 2022, University of Udine, Udine, Italy, 2022
*
Corresponding author, affiliated also to Centro Interdipartimentale di Studi sulle Culture di Genere (CISCuG) of the
University of Bari “Aldo Moro".
$ FrancescaAlessandra.Lisi@uniba.it (F. A. Lisi)
http://www.di.uniba.it/~lisi/ (F. A. Lisi)
0000-0001-5414-5844 (F. A. Lisi)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
The disruptive technologies brought by AI, although successfully applied in a growing number
of contexts, have turned out to suffer from a major problem: The lack of trust from adopters,
due to several flaws in the way these technologies are designed, developed and applied in risky
domains. As humans, we tend to trust more white-box systems than the black-box ones. So,
applications of machine learning/deep learning algorithms are the main source of concern.
At the EU level, the problem has been faced by the High-Level Expert Group on Artificial
Intelligence of the European Commission in the Ethics Guidelines for a Trustworthy AI [1].
According to the Guidelines, AI deserves trust if it is: (1) lawful, i.e. compliant with all applicable
laws and regulations; (2) ethical, i.e. not violating ethical principles and values; (3) robust, from
both a technical and social perspective. Diversity, non-discrimination and fairness are among
the requirements listed in the Guidelines. Fostering diversity, AI systems should be accessible
to all, regardless of any disability, and involve relevant stakeholders throughout their entire life
circle. In particular, algorithmic biases must be avoided, as they could have multiple negative
implications, from the marginalization of vulnerable groups, to the exacerbation of prejudice
and discrimination, e.g. based on gender or race.
The themes of Ethics and Gender are therefore common to RRI and Trustworthy AI. Indeed,
these are the subject of a wide variety of activities carried out by the AI community in the last
years. I will briefly describe some of them in the rest of the paper. The activities considered
here span from research to public engagement, since bridging the gap between science and
society is the ultimate goal of RRI. A particular emphasys is given to initiatives promoted by
the Italian Association for Artificial Intelligence (AIxIA), since the BEWARE 2022 workshop has
been held as part of the programme of the 2022 edition of the AIxIA conference.
2. AI and Ethics
The debate around AI Ethics is inherently multidisciplinary and interdisciplinary. It often
includes the viewpoint of Law experts, as done in a couple of initiatives born within the
EU-funded COST Action “DIGital FORensics: evidence Analysis via intelligent Systems and
Practices” (DigForASP)1 during 2021. The former event, entitled “DigForEthics: Conversations
on Digital Forensics, Ethics, Law and Artificial Intelligence”, was organized by Viviana Mascardi
from the University of Genova, Italy, and Juan Carlos Nieves from Umeå University, Sweden.2
Besides invited talks by prominent researchers like Virginia Dignum and Carles Sierra, it
featured roundtables with the aim of discussing the relevance of the seven requirements for a
Trustworthy AI. The latter event, organized by Chiara Gallese Nobile from Eindhoven University
of Technology, The Netherlands (now in Trieste, Italy), David Billard from the University of
Applied Sciences in Geneva, Switzerland, Elena Falletti from Carlo Cattaneo University LIUC,
Italy, and myself, was devoted to “Automated Decision Making, AI systems and individual
rights”.3 The conference analyzed the ethical, legal, and technical issues of those systems used
to take a decision by automated means without any human intervention.
1
DigForASP (https://digforasp.uca.es/) explores the potential of the application of AI (in particular, Automated
Reasoning) in the Digital Forensics field. The Action started in Sept. 2018 and will end in March 2023.
2
For a brief report see: https://www.cost.eu/when-artificial-intelligence-meets-digital-forensics-ethics-and-law/
3
Report here: https://www.cost.eu/artificial-intelligence-systems-and-individual-rights-where-are-we-heading-to/
AI Ethics is not just a subject for exciting debates across disciplines. It should also be of
practical interest for AI researchers and developers, since ethical issues are raised by many AI
applications. Chatbots are among the most critical ones. These are tools aimed at simplifying
the interaction between humans and computers, typically used in dialogue systems for various
practical purposes, e.g., in customer service. Their behavior may heavily influence a user and
may reflect some bias, e.g., against women. It is therefore important that these systems are built
upon ethical foundations. Drawing from practical philosophy, it can be argued that AI-based
systems could develop ethical decision-making and judgment capabilities by learning from
experience. This has been inspiration for the work reported in [2] that addresses the problem of
evaluating the ethical behaviour of AI-based chatbots in an online customer service chat point
w.r.t their institution/company’s codes of ethics and conduct. Here, the proposed approach
combines two logic-based AI techniques, Answer Set Programming (ASP) and Inductive Logic
Programming (ILP), for defining the detailed ethical rules that cover real-world situations from
interactions with customers over time [3, 4]. ASP is appropriate for representing and reasoning
with ethical rules because it can deal with norms and exceptions, whereas ILP can automatically
generate those ethical rules that are difficult to encode manually. The approach is implemented
in the form of modules for a Multi-Agent System (MAS), which includes a component for text
extraction from dialogues [5].
A major problem in AI Ethics is the current lack of benchmarks for evaluating and assessing
the adherence of AI systems to the ethical standards and the norms of their environment, as
stressed also during the discussion at the AAAI Spring Symposium 2021 on implementing AI
Ethics.4 A collection of unethical scenarios, with reference to different application domains,
would help a lot. In order to address this open issue, the MAS prototype presented in [5] has
a great potential for future implementations of ethical chatbots in different domains. Indeed,
simulation by means of a MAS might provide better insights into the dynamics of a corresponding
real-world system, and to assess the practical challenges and limitations of building such a
system. However, the learning module in the MAS needs data to train the proposed customer
service chatbot. This is still a major weakness in technical solutions for AI Ethics which leverage
some machine learning algorithm.
3. AI and Gender
The relationship between AI and Gender is particularly strained. Several documents delivered
by international organizations like UNESCO, see [6, 7, 8], over the past few years have stressed
the risks of AI for Gender Equality. In particular, it has been shown that current AI-based
technologies can not be considered an enabling factor for reaching the fifth5 of the Sustainable
Development Goals (SDGs) in the Agenda 2030 launched by United Nations in 2015 [9].6 Thus,
the Gender theme deserves the greatest attention. An increasing number of initiatives have
been recently undertaken, some more focused, other devoted to the broader theme of Diversity
4
https://aaai.org/Symposia/Spring/sss21symposia.php#ss06
5
SDG 5: Gender Equality and Women Empowerment
6
See also the book “L’Intelligenza Artificiale per lo Sviluppo Sostenibile” freely available at https://www.cnr.it/sites/
default/files/public/media/attivita/editoria/VOLUMEFULL14digitalLIGHT.pdf
& Inclusion, inside or outside the AI community. Among scientific organizations in the AI field,
the Association for Advances in Artificial Intelligence (AAAI) shows to be the most sensitive
to these aspects and most active in organizing events for raising awareness of them in the
scientific community. Since 2020, the annual AAAI conference hosts an entire programme of
workshops on Diversity and Inclusion. At the national level, starting from 2020 as well, AIxIA
has organized several events, among which the online conferences “Questioni di genere in
Intelligenza Artificiale”7 and “Intelligenza Artificiale e impatti di genere: una questione urgente”
8 were targeted to the general public and to the corporate management, respectively. The
former, organized and chaired by myself, took place on November 25, 2020, as part of the annual
conference of AIxIA, and was the first Italian event of the kind. In the latter, held on July 13,
2021, I was invited to give a talk on the role of Diversity & Inclusion principles to implement
the vision of Trustworthy AI. The challenges of Diversity in AI were also discussed in my talk
at a conference on digital technologies, AI and gender issues organized by Ines Crispini from
the University of Calabria on March 7, 2022, under the patronage of AIxIA, together with the
Società Italiana per l’Etica dell’Intelligenza Artificiale (SIpEIA).9
It has been already mentioned that AI, although it can act as a catalyst to achieve the goals
of the Agenda 2030, it may also trigger inequalities that turn out to act as inhibitors on SDG
5 (and related SDGs). There are a few projects that are trying to counter-balance the many
shorcomings of AI-based technologies with respect to the gender dimension, by developing
applications that can effectively support the achievement of SDG 5. Among them, there is
the proposal of using AI techniques for studying gender power relations, namely the ways in
which gender shapes the distributions of power at all levels of society. These relations are
among the most persistent patterns in the distribution of power, and strengthen inequalities
between women and men. The set of roles, behaviours and attitudes that societies define as
appropriate for women and men (’gender’) can be the cause, consequence and mechanism of
power relations, from the intimate sphere of the household to the highest levels of political
decision-making. Wider structures and institutions can also shape the distribution of power
by reinforcing and relying on gender roles. In the project, funded by the University of Bari in
2022,10 institutional documents have been analyzed with NLP algorithms in order to detect
these patterns at the linguistic level. The basic idea is that the discovered patterns can be further
elaborated with logic-based AI techniques such as ILP to derive policy rules. In this project I am
the Key Area Person for the team of computer scientists and mathematicians, which includes
the NLP researchers Marco de Gemmis and Pierpaolo Basile among the others.
The relationship between AI and Gender can be read also in the opposite direction: What
can Gender Studies do for AI? Particularly interesting and challenging is the promotion of
a so-called Gendered Innovation in AI. Gendered Innovations harness the creative power of
sex, gender, and intersectional analysis for innovation and discovery.11 The adoption of these
approaches may add valuable dimensions to research, as shown for many disciplines ranging
7
https://aixia2020.di.unito.it/program/questioni-genere-ia
8
https://aixia2021.disco.unimib.it/satellite-events-ita
9
https://sipeia.diag.uniroma1.it/wp-content/uploads/2022/02/Ines-Crispini.pdf
10
https://www.uniba.it/it/ricerca/centri-interdipartimentali/culture-di-genere/portlet-generali/HESeeds_abstract_
eng1.pdf
11
http://genderedinnovations.stanford.edu/index.html
from Medicine to Engineering. In the case of AI, this means, e.g., that the awareness of the
problems that affect the fairness of many algorithms, notably ML algorithms, should push
towards truly innovative ways of doing research in the field. In particular, the next step should
be to address the problem of how the gender dimension can be taken into account in the content
of the scientific production from a methodological as well as an application point of view. In
[10], starting from a critical analysis of logical rules underlying the scientific method, it is
shown that a gendered science can be developed if we start formulating new scientific questions
with the awareness that another science is possible. So, it is not only the case that AI brings
disruptive technologies but also the case that other disciplines (e.g., Philosophy) may act as an
innovation driver for AI, and help overcome some limits of current AI research and practice.
4. Feminism-inspired foundations for AI Ethics
An interesting direction of research for a RRI approach to AI is the one that explores the interplay
between the two themes of Ethics and Gender. This should be founded upon an interdisciplinary
debate which involves AI researchers/practitioners and scholars from other disciplines, notably
Philosophy as already mentioned. In particular, it is noteworthy that recently there has been
an increasing interest in contemporary feminist theories to face some open issues in AI Ethics,
notably as the starting point for solutions aimed at addressing the challenges of diversity,
non-discrimination and fairness in Trustworthy AI.
The term Feminism denotes a range of socio-political movements and ideologies that aim to
define and establish the political, economic, personal, and social equality of the sexes. It has
provided Western women with increased educational opportunities, the right to vote, protections
against workplace discrimination, and the right to make personal decisions about pregnancy.
In some communities, Feminism has also succeeded in challenging pervasive cultural norms
about women. Since the late 20th century, the scope of Feminism has become broader and
broader, with the creation of ethnically specific or multicultural forms of feminism, such as
Black Feminism and Intersectional Feminism. These recent evolutions of Gender Studies are
having an increasing impact in AI so that they can be considered as an inspiration for new
foundations of AI Ethics. In the following I will mention some notable examples of this trend.
First, I would like to mention D.A.K.I.N.I., a multidisciplinary and transdisciplinary perfor-
mance that aims at investigating and creating dialectic bridges between the field of AI and
contemporary feminist theories (in particular, Cyberfeminism and Posthuman Feminism).12
It stems from an idea of the collective AjaRiot of performing arts in co-production with the
Nordisk Teaterlaboratorium (NTL – Odin Teatret, DK) and was internationally developed in
2018 and 2019, also by seeking advice from several AI experts, myself included as a AIxIA
delegate. In D.A.K.I.N.I. there are several citations of Donna Haraway’s Cyborg Manifesto
[11], e.g., the awareness that in a world ridden by the informatics of domination, women have
to deal with the question of their involvement with technology, and face its complexity. The
questions to answer are therefore: How can AI serve feminist theories, and life? How can
women take control of it? What happens after they do so? What do women want to pass
on to future generations? Through a mix of artistic languages (physical theatre and dance-
12
https://ajariotcollective.com/d-a-k-i-n-i/
theatre, video projections, photographs, interviews, merging of ancient and electronic music,
sound experiments) the performance touches several crucial aspects of the relationship between
women and technology: (i) the under-representation of women in ICT, both in education and
in professions, (ii) their de-facto exclusion from the processes of design, development and test
of digital technologies, (iii) the gender biases in machine learning algorithms and automated
decision making systems. The ultimate message of the performance is the importance of having
role models, here represented as a kind of dea-ex-machina (the goddess Dakini, symbol of
femininity and women empowerment in Hinduism and Buddhism), to encourage women to
take active part in the digital revolution. The epilogue of the performance reflects the thought
of Rosi Braidotti [12], e.g., her claim: “I see the posthuman turn as an amazing opportunity
to decide together what and who we are capable of becoming, and a unique opportunity for
humanity to reinvent itself affirmatively, through creativity and empowering ethical relations,
and not only negatively, through vulnerability and fear”.
Leaving the Arts and going back to more technical subjects, the work of Catherine D’Ignazio
and Lauren Klein on so-called Data Feminism presents a new way of thinking about data science
(and data-driven AI) that is informed by the ideas of intersectional analysis [13]. Data Feminism
goes far beyond gender. It deals with power, by analyzing who has it and who doesn’t, and how
those differentials of power can be challenged and changed. This awareness of the existence
of gender-based power relations (as in the project mentioned in the previous Section) raises
several questions that the data scientist should answer during his/her professional pratice: Data
science by whom? Data science for whom? Data science with whose interests in mind? In their
book, D’Ignazio and Klein show how challenges to the traditional male/female binary can help
detect and discard other hierarchical (and empirically wrong) classification systems, such as the
ones resulting from data science for harm (e.g., to discriminate, police, and surveil).
Finally, particularly interesting is the position of Virginia Dignum in [14]. She starts with
a general discussion of current conceptualisations of AI followed by an overview of existing
approaches to governance and responsible development and use of AI. Then, she reflects over
what should be the bases of a social paradigm for AI and how this should be embedded in
relational, feminist and non-Western philosophies, in particular the Ubuntu philosophy. Notably,
her proposal does take into account the requirement of robustness, as typically done in socio-
technical systems and in line with requirements for a Trustworthy AI.
5. Conclusions
A responsible approach to research and innovation in AI calls for multidisciplinarity and
interdisciplinarity. In particular, the relationship with Law and Philosophy can be two-way. On
the long run, due to the disruption brought by AI, the interlinking between these and other
fields will become tighter and tighter. It will be unavoidable to overcome the current separation
between Humanities and Sciences and Engineering. Indeed, Education - one of the other RRI
pillars besides Ethics and Gender - plays a prominent role in this transformation, as also testified
by some pilot experiments in academic curricula such as the class of “Gender Knowledge and
Ethics in Artificial Intelligence” at the School of Engineering of the University of Padua (see
[15] for a report of the experience done by the teachers Silvana Badaloni and Antonio Rodà).
Acknowledgments
This paper is partially based upon work from the Italian project “Future AI Research (FAIR)”,
funded by PNRR. The work contributes to the goals of WP 6.5 “Legal and ethical acceptability
of Symbiotic AI applications” of the spoke 6 “Symbiotic AI”, lead by the University of Bari.
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