This month EPWS gives the floor to Silvana Badaloni, Associate Professor of Artificial Intelligence at the Department of Information Engineering at the University of Padua, Italy. In the field of Gender in Science, she was the scientific coordinator of the Unit University of Padova (Padua), partner of the FP7 EU GenderTIME (2013-2016).
Contact:
silvana.badaloni@unipd.it | www.unipd.it
EPWS: What made you want to go to science? How did you decide to choose your discipline and your particular field of research? Did you have an inspiring model (parent, relative, teacher, literature etc.)?
I started out on my scientific path when I decided to study Physics. I have always preferred scientific subjects. Probably both my father and my sister, even though not directly, influenced my attraction for maths and science.
EPWS: What do you work on? How important is your research topic for science development or society?
I developed my research under the sign of change, in three main steps:
I started my work at the School of Engineering, dealing with physical models of electrical discharges in the air and I became an outstanding expert spectroscopist of the plasma of discharges.
Motivated by my growing interest in Artificial Intelligence (AI), I took a DEA/Master at Ecole Nationale des Ponts et Chaussées (ENPC, a high level engineering school), Paris, in 1984, and since then I have experienced a new season in my scientific journey aimed at researching and teaching AI.
Due to my increasing gender awareness, the third step brought me to Gender in Science and to lead the FP7 European Project GenderTIME on behalf of the University of Padova in 2013. The acronym TIME stands for Transferring Implementing Monitoring Equality. The project, coordinated by Yvonne Pourrat (ECEPIE), sought to promote structural changes in Academic Institutions from a gender point of view.
The keywords that have guided my scientific adventure are: curiosity about the novelty, passion, autonomy, interest in social issues and, of course, scientific method.
EPWS: What is your greatest success as a researcher (and as a teacher if you teach), the one you are most proud of? Could you share the memory of a great personal satisfaction during your research career with us?
Change has also crossed my mind: I have dealt with the representation of change in a logical-symbolic framework. To do this, I have studied how to represent the notion of time in Intelligent Autonomous Systems. Moreover, in real applications, time is affected by vagueness and uncertainty. The most important work that I developed on this subject concerned the fuzzy extension of a temporal reasoning system. A great personal satisfaction for me was its publication in an excellent international journal [1].
EPWS: In which country/countries have you been doing research?
Mostly in Italy, even though working in the framework of a European project means sharing research all over Europe.
EPWS: What is your agenda for the coming months?
In my agenda, in the near future, there is the study of gendered innovations in the field of Information and Communication Technology. In a recent work, I addressed the problem of including the gender dimension within the content of Science, trying to answer the following questions: how can we develop a new Science that takes into account the gender dimension? How can we formulate new scientific questions, now that there is greater awareness that another science is possible?
Using the logical rules of argumentation, I have demonstrated that in order to produce new gendered innovation in all fields it is not enough merely to apply the “pinking” method, i.e. the stereotypical feminization of products. Rather it is necessary to radically change the underlying assumptions. Only a complete redefinition of method and research models, with new applications, new ways of observation, newly reformulated questions, can re-design science from a gender perspective.
A second problem concerns the fairness of algorithms. Are the tools, the algorithms and technologies that Computer Science develops and uses really gender-neutral? I analyzed Machine Learning algorithms to see whether they are fair from a gender point of view; I found many examples that revealed that these kinds of algorithms, because they are conceived as learning systems of classification, can often upload the gender biases endemic in the society. The problem arises mainly because little attention is paid to how data are collected, processed and organized, thus any bias is, substantially, a data-driven bias.
EPWS: Did you meet barriers (personal/social/structural) during your career as a scientific researcher? Did you benefit from mentoring?
Working in a School of Engineering, where the predominantly male, engineering population remains closed inside its self-referential old-boys’network, I came up against many barriers, both visible and invisible. This situation of perceived personal under-representation encouraged me to address the problem of the under-representation of women in Science in a more general way, analyzing gender statistics, promoting equal opportunities in scientific careers and challenging gender balance in decision-making bodies, seeking to create a Science that is no longer based on the myth of the universal neuter-male scientist, but rather is a gendered Science.
Mentoring? That can only be useful if there is at least one mentor!
EPWS: What is the situation of gender equality in your working field? In the countries where you have been working, were there gender equalities policies and did you experience their effects? What do you suggest for a better implementation of gender equality in science?
The problem of promoting gender equality in Academia can be addressed if, and only if, Institutions adopt severe and effective Gender Equality Plans (GEP) to reduce the under-representation of women in Science and to favour a different wellness in workplaces for everybody. The positive experience of the Athena SWAN Charter in the UK, which constrains institutions to do so, should also be adopted in Italy to change the situation, but at the moment this still seems to be a long way away from happening.
In the framework of GenderTIME, we addressed the problem of measuring Gender Equality in Academia. To do this, the efforts of our multi-disciplinary research group at the University of Padua were devoted to outlining a composite indicator of gender equality, UNIPD-GEI, specifically tailored to deal with the environment of Universities and Research Institutions. Based on the population index of EIGE (European Institute for Gender Equality), our conceptual model was defined in terms of seven domains: work, money, knowledge, time, space, power and health and was declined for Academic Institutions to drive data collection. Some of the indicators, such as those in the domains of knowledge and money, but mainly power, had values that revealed high levels of gender discrimination in scientific career paths. Methodological issues and results are reported in [2].
This Gender Equality Index could be disseminated at the European level for comparing Academic Institutions on the basis of a ranking that measures the gender equality item (not only the H-index).
EPWS: Did you experience networking between women scientists? Can you comment your answer and explain why yes or not?
Of course, I experienced networking between women scientists, and I believe this is fundamental for a woman scientist. I have belonged to the Italian Association Women and Science – Donne e Scienza – for many years. Currently, I am a member of the Advisory Board.
From 2009 to 2013 I was on the Board of Administration of the EPWS.
EPWS: If you could start again your life, would you choose again to be a scientist? What would you change?
Without any doubt I would choose to be a scientist. It’s a wonderful job. Even though I have recently retired, I am still continuing to do research with great passion and interest.
EPWS: Could you give a message to young European women scientists?
To young women scientists I suggest to be fully themselves, avoid adopting typically male models and just totally follow their curiosity, and passion. They should be proud of being women scientists.
References
1. S.Badaloni, M.Giacomin. (2006) “The algebra IAfuz: a framework for qualitative fuzzy temporal reasoning.” Artificial Intelligence. Vol. 170, Issue 10, pages 872-908, Elsevier, ISSN: 0004-3702
2. Badaloni S., Perini L. (eds) (2016) “A model for building a Gender Equality Index for academic institutions.” Padova University Press, ISBN: 9788869380983 http://www.padovauniversitypress.it/publications/9788869380983
Favourite LINKS:
https://genderedinnovations.stanford.edu