For Quality in Software Engineering we should all be Feminist - QUATIC conference

Editor's Notes

• #1 About me, my university, Norwegian University of Science and 15 min. Understanding computer science and gender 15 min. Interaction and mini break 5 min. Interventions 15 min. Research 15 min. Interaction and break 15 min. Conclusions, advice, and questions 15 min. letiziajaccheri.org
• #3 Who developed the agile manifesto?
• #8 We should all be feminists! Because as Author Adichie explains in her powerful and poetic ted talk, being a feminist means being convinced that women and men should have the same rights We should all be feminists | Chimamanda Ngozi Adichie | TEDxEustonhttps://www.youtube.com/watch?v=hg3umXU_qWc
• #9 Video https://www.youtube.com/watch?v=hg3umXU_qWc&t=648s 8 milioner ganger Den feministiske klassikeren "Et eget rom" er en nøkkel til Virginia Woolfs forfatterskap. Essayet er basert på en forelesning om kvinner og litteratur holdt i Cambridge i 1928.
• #10 The university of Pisa was funded in 1343 when scientists were looking at the stars and trying to count and understand. Once upon a time in 1960 the first italian computer was built in Pisa. It was built 500 meters from my house and even if nobody in my family was involved and even knew about that project, I like to think that that project, a cooperation betwen the university of Pisa and Olivetti was somehow the start of my professional path. When I graduated in 1988 I worked as a Programmer for two years and then I went back to school and I took a PhD Software Engineering Politecnico di Torino, I graduated almost 30 years ago in 1994. I was in Norway twice as exchange PhD student under the supervision of professor Conradi, one of the founders of software engineering internationally and in Norway
• #12 Quality of the process and quality of the software How do we understand gender and SE? gender as a social construction (as opposed to sex, which is by birth) (it: genere, sesso – no: gender as a non-binary term (as opposed to what it used to be: binary male vs female) - recent steps (correlated) in the women's rights movement #metoo started in 2017 as well as the LGBTQ rights movement, while the first gay movement started more than 100 years ago, The Netherlands was the first country to allow same-sex marriage in 2001. defining one's pronouns has become trendy (she/her) non-binary acceptance of gender intersectionality, that is, that our identities have several layers and they influence one another --> people who are not only women, but also LGBTQ and of color, are triply underprivileged and left out of rights not only in everyday life but in digital services as well. gender equality charts in the EU or over the world and Gender Gap Index education + social stereotypes --> girls' barriers to get into IT and CS due to women's lack, and the lack of diversity, in IT teams --> bias in designing products due to a lack of diversity, for ex. HCI, AI, etc. this creates usability problems, tech issues, discrimination, the spread of bias, etc. It is crucial to underline that taking gender into account in tech is not only a human rights issue, but an economic and technological need.
• #13 The software engineering (SE) community has focused on the gender gap that is indicative of broader societal biases but is only one dimension of the complex system of inequalities. https://www.unwomen.org/sites/default/files/2022-01/Intersectionality-resource-guide-and-toolkit-en.pdf Intersectionality Resource Guide and Tool Kit Interseksjonalitet er et analytisk rammeverk som brukes for å forstå hvordan ulike sosiale identitetskategorier, som kjønn, rase, klasse, seksualitet, funksjonshemming og mer, samvirker på komplekse måter og skaper unike opplevelser av diskriminering eller privilegier for individer. Begrepet ble først introdusert av den amerikanske juristen og akademikeren Kimberlé Crenshaw på slutten av 1980-tallet. Hun ønsket å forklare hvordan svarte kvinner opplevde diskriminering på en måte som var forskjellig fra både hvite kvinner og svarte menn, fordi de sto overfor både kjønns- og rasediskriminering samtidig. Interseksjonalitet ser derfor på hvordan ulike former for makt og undertrykkelse overlapper og påvirker hverandre. For eksempel kan en svart, funksjonshemmet, lesbisk kvinne oppleve diskriminering på grunn av alle disse identitetsaspektene samtidig, noe som gir henne en annen opplevelse enn noen som bare opplever diskriminering basert på én av disse identitetskategoriene. Interseksjonalitet er viktig for å forstå at sosiale problemer og urettferdighet ikke kan løses ved å se på hver identitetskategori isolert. Det krever en helhetlig tilnærming som tar hensyn til de ulike lagene av identitet og hvordan de påvirker hverandre.
• #14 during the Enlightenment, with thinkers like Mary Wollstonecraft
• #17 Who developed the agile manifesto?
• #18 Some numbers about ICT specialists in Europe and World and who are the ICT specialists In 2022, 9.4 million people in the EU worked as ICT specialists In the world 55.3 million in 2020 3 out of 4 companies have problems finding specialists with the right skills And how many ICT specialists are women? 2% in 10 years How many years will it take if we continue like this? eurostat https://ec.europa.eu/eurostat/statistics-explained/index.php?title=ICT_specialists_in_employment In 2023, 80.6 % of men were employed as ICT specialists in the EU against 19.4 % of women.
• #19 https://www.statista.com/statistics/1126823/worldwide-developer-gender/#:~:text=According%20to%20a%20global%20software,reality%20of%20software%20development%20jobs.
• #20 But why? What are the challenges? And in our projects we are trying to combat these challenges,
• #31 The Curb-Cut Effect is a vibrant illustration of how laws and programs designed to benefit vulnerable groups, such as the disabled or people of color, often end up benefiting all.
• #32 Photo was taken at a gathering in ADA when a cohort of female students started recently Look at the enthusiasm - this is the future - these are future workers Perhaps our future colleagues Many times I have been criticized
• #33 Numbers for bachelor master phd – in Norway we are doing a bit better 29%
• #34 Examples from ADA Calling the applicants, Welcome day, March 8 - Women's Day, Network lunches, Programming courses, social activities such as Mountain hiking, PhD party, Invite high school girls from all over the country, Presentations and workshops, Personal meeting with role models, Break down stereotypes and bias, hire women role models This is answer to question about what works Explain briefly what these projects have done and achieved since 1997 Measures such as https://www.ntnu.no/ada https://www.ntnu.edu/idun Eugain.eu https://www.ntnu.edu/smartcities/craft https://liu.se/en/news-item/erasmus-projektet-women-stem-up-tilldelas-4-1-miljoner-kronor https://womencourage.acm.org/2023/ https://www.abelia.no/50techkvinner/
• #35 And then there is the big Q To slide 9: And then there is the big Q – the Q that makes many people uncomfortable. The Quota. And I understand that. If you look at the deprecating language that is connected to it, there is every reason for discomfort. Who wants to be called “a token woman”, in German “Quotenfrau”, “une femme imposée par les quotes” in French. They sound like downright insults. The quota should not have to exist. We should be able to do without. But the fact of the matter is that it works, and that we will not reach our goals without it. In fact, am “a token woman” myself. I became one of the last female professor in Norway thanks to the quota systems before such positions became un legal.
• #36 But why? What are the challenges? And in our projects we are trying to combat these challenges, Examples from EUGAIN Calling the applicants, Welcome day, March 8 - Women's Day, Network lunches, Programming courses, social activities such as Mountain hiking, PhD party, Invite high school girls from all over the country, Presentations and workshops, Personal meeting with role models, Break down stereotypes and bias, hire women role models This is answer to question about what works Explain briefly what these projects have done and achieved since 1997 Measures such as Information Mentoring Network Anti bias training
• #37 Background and challenges: see slide By bringing the focus and awareness to diversity and equality, among other aspects also in gender, with projects like IDUN, we aim to keep our faculty attractive for our students and staff. Because we all strive and grow best in a diverse environment with a great variety of role models around us. Our ultimate goal is to give an important impulse towards a cultural change. That encompasses the power structures, communication processes and awareness of the gender related challenges. The IDUN project has been named after Idun Reiten, Professor Emerita, the first female professor at the current IE faculty. IDUN began August 1, 2019, with the goal that by the end of 2022, we will see an increase to at least 15 - 20% women at all levels.
• #41 How to have more girls choosing Informatics as their higher education studies and profession. How to retain female students and assure they finish their studies and start successful careers in the field. How to encourage more female Ph.D. and postdoctoral researchers to remain in the academic career and apply for professorships in Informatics departments. How to support and inspire young women in their careers and help them to overcome the main hurdles that prevent women to reach senior positions in the field. How to leverage from the experience of the partners in the network to tackle the previous challenges and achieve progress across more institutions and countries, and positive results that are sustained on the longer term. The 38 COST Members are: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Republic of Moldova, Montenegro, The Netherlands, The Republic of North Macedonia, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom.
• #43 M. Burnett et al., "GenderMag: A method for evaluating software’s gender inclusiveness", Interact. Comput., vol. 28, no. 6, pp. 760-787, 2016. It enables software practitioners (e.g., developers, managers, UX professionals) find gender-inclusivity "bugs" in their software, and then fix the bugs they find. M. Vorvoreanu, L. Zhang, Y. Huang, C. Hilderbrand, Z. Steine-Hanson, and M. Burnett. 2019. From Gender Biases to Gender-Inclusive Design: An Empirical Investigation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, New York, NY, USA, Paper 53, 1–14. https://doi.org/10.1145/3290605.3300283
• #44 Examples of community smells are Organizational Silo effects (overly disconnected sub-groups) or Lone Wolves (defiant community members). Why should we care for communication? https://www.youtube.com/watch?v=d7TGWOaeujU Gender Diversity and Inclusion and Software Engineering (team, process) one developer can decide to work in isolation
• #48 Measuring price discrimination and steering on e-commerce websites typically involves detecting whether different users receive different prices or product recommendations based on factors such as location, browsing behavior, or device type. Researchers use several methods to assess this: Web Scraping: Automated tools or scripts gather pricing information from e-commerce websites at various times, from different locations, or using different devices (e.g., mobile vs. desktop). This helps to compare whether prices differ across these variables. A/B Testing: Creating different user profiles with varying characteristics (such as IP addresses from different geographic regions) allows researchers to compare the prices shown to different profiles and detect any patterns of discrimination or price steering. Cookie and Tracking Analysis: E-commerce websites often use cookies to track user behavior. By analyzing how prices change when cookies are deleted or a user browses in "incognito" mode, it becomes possible to measure the effect of tracking on pricing or steering. Controlled Experiments: Simulating various user interactions with the site (such as searching for certain products or clicking on specific ads) can reveal whether the site adjusts its pricing or product recommendations based on past behavior. Data Collection and Statistical Analysis: After gathering data on prices shown to various users, statistical analysis is used to determine whether price discrimination or steering exists. This involves comparing average prices and product recommendations across different user groups or conditions.