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Information about People and Culture activities and resources in the School of Informatics
Our School works best when everybody is heard, and nobody is left behind. We are always eager to find out how to improve our community, with regular official feedback opportunities. In the last two years the School Culture survey, Athena Swan focus groups, and the University’s Staff Engagement survey have been implemented. These have revealed some common issues that we should improve; and although some we can only raise to College or University, there is a great deal that we as a School can act upon.
Findings to date may indicate that we need to listen and respond further to the needs of our staff. We’re keen to hear voices from across the whole School, especially including those who may not have contributed their views yet.
Therefore, the School is contracting an external body, Advance HE, to do three things:
Analyse all available data to understand what workplace issues are happening;
Facilitate an independent consultation of our School community to understand why these issues are challenging;
Develop independent recommendations about improving staff and student experiences.
Practically, over the next couple of months, Advance HE will be conducting 8 focus groups – 2 for professional services staff, 2 for academic staff, 2 for research staff, and 2 for research students – as well as in-depth interviews with key senior staff members. All work is completely independent from the School leadership, with robust and proper privacy and ethics considerations in place.
Our School has kindly invested in this work to improve our culture. When you are invited to join a focus group, please grab the chance, even if – especially if – you typically don’t respond to surveys. It is a great opportunity to improve our effectiveness to work together as a productive and happy community. Let’s seize it!
As a PhD student in the School of Informatics, I’ve been researching gender bias in language and language technologies. Time and again, I’m surprised by how simple people try to make biased language. As Abeba Birhane stresses in her interview on the podcast The Good Robot, not everything can be conceptualized as a straightforward problem with a straightforward solution. Our cultures are dynamic and complex. Our languages evolve slowly over decades, but also change rapidly based on our relationships with the people we are speaking to or writing for. Moreover, language does not exist in a vacuum.
In the branch of Linguistics called Critical Discourse Analysis, language is studied in its context of use, considering how it legitimizes and maintains power, and how it incites social change. [1] Nevertheless, AI research approaches bias as a problem to be fixed, as if bias is an error that can be removed from a dataset, or a mistake that a model can be taught to avoid. In reality, however, bias is an ongoing challenge.
Bias changes with time, place, and culture. Bias will always be with us, because there is no universal, neutral, or objective perspective. We all are shaped by our own unique viewpoint, by our own experiences of the world.
We need to reframe research questions about bias in data and technology. Rather than focusing on removing bias, we need to better understand bias. We need to study how bias comes through in language and other types of data. We need to consider the risks bias poses and the harms bias may cause. Researchers such as Abeba Birhane and Kate Crawford are among a small but growing group of people in the computational research community trying to do this. There is a wealth of research in the Humanities and Social Sciences that the computational research community can look to; people have been studying and theorizing about language and bias for much longer than the existence of AI as a field. The School of Informatics has been an exciting place for me to research bias in language technologies because I’ve had the opportunity to talk about new ways to approach bias and ethical AI research with fellow PhD students like Nina Markl and Bhargavi Ganesh.
To reframe questions about bias in data and technology, we need a culture shift in the AI field. Currently, efficiency, convenience, and quantity drive dataset curation and model creation. Being the first to publish something is highly valued, so gathering data and building models quickly are done at the expense of critical approaches to dataset and model development. To gather data quickly, language and images are taken from the Internet without consent from the people who own them or are represented in them. Datasets are evaluated based on how large they are rather than how representative they are.
Instead, we need accuracy over efficiency, balance and representativeness over convenience, and quality over quantity. Then we will realize that bias comes not from the model or the data, but from us, people and society. Then we can focus on changing the power structures that cause harmful biases.
References
[1] For more on Critical Discourse Analysis, see Analysing Discourse: Textual Analysis for Social Research (Fairclough, 2003) and Uses of Heritage (Smith, 2006).
Our School undertakes a culture survey of all School members every two years. We don’t do this annually because we feel all this would result in is survey fatigue. We know that completing the survey takes some time! So why do it? We think the best incentive for completing the survey is evidence that the responses trigger changes that tackle the issues raised. Here is a summary of the main issues raised by the 2021 survey and how the School has responded.
Students
The survey saw 201 responses which was mainly completed by research postgraduate students. Response rates from undergraduate and taught masters students were low. We’d like to see a significant increase in responses from all categories of student to the 2023 survey. We hope the brief reports on each of the main issues identified in the survey will encourage more participation in the survey so we can have a clearer view of issues where things have improved and where we still need more work. The main issues we identified were the following.
Workload
This is the clearest and most pressing issue that comes up in several different contexts and is seen as contributing to other issues identified in the survey. Issues arise around the number, scale, and coordination of deadlines for coursework:
We use information from weekly reps meetings and Staff-Student Laison meeting to identify courses where workload is seen as an issue by students. These are reviewed and several courses have had the number and scale of courseworks reduced as a consequence of these reviews.
We have begun to make better use of the academic year by reconsidering the pattern of deadlines. Coursework-only courses can set deadlines beyond week 11 to make use of the early weeks of Semester 2 and the revision period prior to the main exam diet. This reduces deadline congestion and makes better use of the available weeks of study.
We are consulting now on reorganising the schedule the final-year project: deciding on a topic, preparing for the project and working on the project. Our goal is to avoid having the project run concurrently with other courses and permit a longer period of full-time work on the project.
Issues around deadline congestion are difficult to resolve. One approach we have considered is to have courseworks that span multiple courses to reduce the number of courseworks undertaken simultaneously.
Communication
Many responses point out that the respondents feel like the School is spamming them on multiple channels. Indiscriminate use of whole year mailing lists, multiple emails in the same day, inconsistent use of channels across courses all contribute to this feeling:
The move to LEARN ultra has started work in the School on how best to use the new structures. One opportunity is to establish a more consistent policy on the messaging related to individual courses.
We are considering the use of stricter moderation on the large and indiscriminate email lists (e.g.,
We are also actively considering options to request journaled messages on some of the more active lists.
Community and Caring
This is a somewhat more controversial topic since there is a minority view that questions whether the School should care about community and caring but the majority feel the School should attempt to build a caring community. In this area we have:
Initiated the development of basic training in Equality, Diversity and Inclusion oriented to students to help engender a more open, respectful dialogue in the School that will help counter the perceived difficulties some students experience in expressing their views to other students.
The variability in sense of community experienced by PhD students is also problematic. The School is considering how best to engender a stronger sense of community across all PhD students.
Timing of Events
Some student respondents raise the issue of the timing of events that assume students are always available. The School will now endeavour to ensure that events are more sympathetically timed.
Bullying and Harassment
Overall the level of bullying and harassment is low in the School. However, the School will endeavour:
To make reporting mechanisms clear and more clearly anonymous to respond to the expressed lack of knowledge on how to report bullying and harassment.
The School is aware the EUSA is promoting active bystander training for some societies’ members. The School is exploring how to make such training more widely available to all students.
Mental Health and Wellbeing
This is seen as a major deficiency. The time delay and lack of mental health and wellbeing provision is problematic for most respondents. These services are provided university-wide so there is little the School can do directly in terms of increasing the supply of services but we are exploring ways we can reduce demand:
Exploring how to reduce stress levels among our students. Better management of coursework loads (see earlier) are an important route to reducing stress.
Increasing the number of mental health first aiders in the School. This is not a long-term fix but having a wider trained group improves accessibility to prompt help and increases awareness and sensitivity to the issue in the School.
Our new expert student support staff will help ensure students receive prompt and consistent support for mental health issues. The School believes this is a significant improvement over the current situation. The switch to the new system takes place over the summer.
Staff
The survey saw 185 responses which is a significantly higher response rate than the student survey. This has a good spread across all staff categories and levels of seniority.
Workload
This is one of the clearest and most consistent issues across all staff related to students and is related to increases in student numbers. It is seen a major contributor to poor wellbeing, stress and mental health issues.
Action to reduce the volume of assessed coursework mentioned above has a direct impact on staff workload. This work is continuing, and consultation on managing the final year project workload is underway.
Action has been taken on admissions more effectively to control admissions of taught students and growth in student numbers has been brought under control.
Community and Caring
The School is a large organisation and building an effective and caring community is challenging.
Continuing to strengthen the role of the Institutes provides smaller communities for some categories of staff and students that are still evolving, particularly post-COVID.
Strong staff networks are also seen as good mechanism to encourage communities with common interests.
Individual initiative such as yoga classes and the concert series also provide mechanisms that encourage interaction and socialising across all staff.
Equality, Diversity, and Inclusion
Currently the main focus in the surveys is on gender issues but the School is aware of wider EDI issues.
New training in EDI impact assessment will be used to ensure that all new policies are assessed for EDI impact.
EDI impacts will be documented and followed up by the People and Culture Committee.
Data on EDI impact on promotion will be gathered and analysed systematically to provide a good evidence base for further action.
Bullying and Harassment
Some bullying is experienced, particularly by more junior staff and between academic and other staff. Bullying is clearly unacceptable.
School will establish a confidential channel to report bullying and will publicise policies and reporting channels widely.
Progression and Promotion
There is a feeling that decision taking lacks transparency and support for development to enable promotion/progression.
School is organising additional training for Line Managers to enable them better to support the development needs of staff.
School is working to provide clearer career route mapping.
Mental Health and Wellbeing
Issues around mental health and wellbeing are closely related to workload.
The workload model is explicit and transparently implemented. This is still becoming fully established. As it beds in we anticipate being better able to identify under-resourcing and the need to recruit to better resource under-resourced activities.
School is working with the wider University to increase staff access to mental health services.
There are disproportionately few women enrolling for undergraduate degrees in computing in the UK. Despite constituting 50.5% of the UK population and 57% of college graduates in the UK, only 19% of the technology workforce are women. The statistics for staff within our school align well with the national figures, with women constituting 56% of our professional services staff but only 20% of our academic staff. The disparity is lower in our student population: 27% of taught students (24.2% of undergraduates and 36% of post-graduate) and 22% of research students identified as female.
The decisions made by pupils in the last two years of high school is a key contributor to this disparity. The female to male ratio for first year STEM undergraduates across the UK hovers around 1, but its breakdown across disciplines reveals wide variation across the sciences (Figure 2 shows a detailed breakdown for interested readers). About one in four Computer Science (or Engineering) undergraduates identifies as female. Those who identified as ‘other’ when given a 3-way choice of gender (about 0.4% of UK’s population) make up about 0.2% of first year undergraduates in the UK; within our school the numbers are significantly better than the national average (0.7% of taught students and 2% of research students).
Most of the interventions designed and delivered in the UK [2,3], to reduce gender disparity in STEM, have been targeted at high school students. Specifically, focussing on female pupils to educate them about the benefits of choosing careers in science, via mechanisms such as the Stimulating Physics Network [1]. It appears that female pupils choose non-mandatory STEM subjects [9,10] in secondary schools when they:
believe that they are `good’ at it;
appreciate the value of science;
are embedded in a micro-culture that values and discusses science [7]; and
are exposed to role models provided they do not conform to STEM stereotypes [4,5].
The UK has spearheaded studies related to 3, under the umbrella of ‘science capital’ [6].
Paradoxically, there is evidence [8] that gender disparity in engineering and technology is inversely related to national gender equality. That is, countries with higher percentages of women engineers (around 40%) tend to have a poor global gender gap index. E.g. Algeria, Tunisia, U.A.E., Turkey, Indonesia, Vietnam.
In summary, with only 20-25% of undergraduates in Engineering and Computer Science being women, the status quo precludes the majority of women in the UK from gaining the skills needed for lucrative tech jobs (Figure 1). It is a large and complex issue. What can we at the School of Informatics, as the powerhouse of computer science in the UK, do about this?
My view is that we could aim to overcome this disparity at three different levels:
we are probably large enough to effect change by leading and organising effort at the national level (spawning something like the SPN but specifically for computing) while liaising with government (e.g. Scottish Parliament, Department for Education);
locally, we could identify appropriate [5] role models within the school who connect with schools to keep female pupils (and their teachers) informed of the impact of their choice of subjects to society and their own careers; and finally. For example through the Informatics Tutoring Scheme.
every one of us should actively contribute to an inclusive environment within the school. Although this sounds obvious and trivial, we continue to hear about scope for improvement, in this regard, via our student surveys.
What do you think?
Fig 2. Ratios of full-time female to male students in first year undergraduate (left) vs graduate (right) programmes in the year 2020-2021. Data from HESA.
