Bias – School of Informatics People and Culture

Have you ever been commsplained?

There I was, scrolling through LinkedIn, when a post stopped me in my tracks: ‘Have you ever been commsplained?’ It was a moment of instant recognition. ‘Commsplaining’ is real, and while subtle, it is a more common example of workplace dynamic than you might think.

We explain to everyone, all the time

Indeed, many a time, a colleague, who is not a comms professional, has tried to explain communications (or something about communications) to me. However, unlike the infamous notion of ‘mansplaining’ derived from the influential essay by Rebecca Solnit, the person wouldn’t necessarily be condescending. And time and again, colleagues make confident statements about comms-related issues to me, while being wrong. Or sometimes, very wrong.

We probably all do it with no malice and feel embarrassed when it’s pointed out that we are talking to an expert, or that we are wrong and why (again, different from the case of ‘mansplaining,’ which includes the ‘mansplainer’ not being embarrassed).

A photo of a black door in a white frame within a brick wall — Number 10 Downing Street
Photo by: Sergeant Tom Robinson
under the Open Government Licence version 1.0 (OGL v1.0).RLC/MOD

After all, most of us think we know, for example, what the government should be doing, what policies it should pursue, and in what timelines, even though hardly any of us have experience governing.

It’s the opposite of impostor syndrome: a cognitive bias that makes us believe we know more than we do, and since we don’t know what we don’t know – well, we are blissfully ignorant!

Lift them up, don’t bring them down

However, the problem with preaching to the experts is how it makes them feel – I certainly get irritated when it happens to me. It’s also not always obvious how to tactfully make the ‘commsplainer’ aware that what they are sharing is not news to me: on the contrary, I have already tried and tested the exact same idea. Most people who approach me in a professional context know that I work in comms. Still, they are ignorant of the fact that I must have knowledge about it, resulting from education, qualifications, and some 20 years of experience.

But it gets worse: when we tell people what they already know, positioning ourselves as experts, we might inadvertently make them doubt themselves or even underestimate their own expertise. Especially, if they perceive us as being in a position of power. In this case, it is no different to ‘mansplaining,’ or simply patronising anyone, who we have power over (even if perceived only).

Do we want our expert colleagues to feel they lack expertise? Or do we want to empower them to be even better at what they do?

Patience is everything

I chatted with a couple of academic colleagues about situations when I wouldn’t be given credit for my knowledge and expertise and instead be lectured, and they pointed out the obvious: ‘That’s probably because we are lecturers! So, it sounds like we are lecturing, but we do not mean to.’

It gave me something to consider. Most people don’t mean to be mean; they might just be wearing their ‘lecturer’s hat.’

The author of the meme I started my post with suggests that the best strategy to deal with ‘commsplaining’ is not to take it personally. Be respectful and kind and take the ’lecturing’ in good spirits.

Last summer the InfComms team hosted a summer intern, a lovely and inquisitive Aagoon, who asked me: ‘What is the most important skill in comms?’ Without a second thought, I said ‘patience.’

A lot of work in communications is done in the background, in the solitude of one’s office. The outcome (a story, a social media post, a newsletter, a paragraph in someone else’s comms, a website, a microsite, an ad, or a blog) can seem easy to create. But there is a lot of work behind the scenes to do research, ask questions, proofread, refine, re-write whole passages to ensure the message is accurate and appropriate, and so on. It can sometimes take weeks to develop one output. If the outcome seems ‘easy’ and not laboured then your comms colleagues have done their job right. But it might have required a lot of effort and knowledge. Just because you can’t see the work put into achieving something, it doesn’t mean someone didn’t work extremely hard for things to happen.

It’s a bit like seeing your GP, who takes one look at your results and diagnoses you. Easy! But it took years of study and experience to be able to do that.

Be like Lieutenant Columbo

A black and white photo of a middle-aged curly-haired man — Peter Falk as Lt Columbo, public domain

For a comms professional, patience isn’t just a virtue; it’s a necessity. You need to do, what I call ‘being Columbo’ (referring to the persistent and thorough detective portrayed by Peter Falk in the classic TV series): keep asking questions, until you are absolutely certain that you have all the knowledge you need to write your story accurately, and that you’re observing embargoes, not stepping on anyone’s toes, and not dumbing down the story.

But you also need a lot of patience to deal with outside pressures: deadlines, expectations, and yes, you guessed it, ‘commsplainers.’

We all communicate every day, so it’s easy to assume that we all have a level of expertise in comms. It may result in putting undue pressure on or having an unreasonable expectation of our colleagues working in comms roles.

However, when you’re at the receiving end of such pressures, you need a lot of patience to listen and explain what is and isn’t possible, and more importantly, what is and isn’t good practice. But, on the other hand, if someone takes the time to come to you with their ideas, even if they sound like lecturing, consider listening and harnessing their enthusiasm. Use the opportunity to share your knowledge and expertise to manage their expectations and teach them something new.

If you find yourself advising the expert, take a moment to reflect: are you offering new insight, or might you be ‘commsplaining’? Perhaps start with giving some kudos to your comms colleague for their effort and expertise before sharing your ideas. Make them feel like the expert that they are and listen to their words of wisdom. After all, they have been doing it for a little while longer than you. Appreciating them will take you a long way and maybe will be the beginning of a beautiful friendship.

Collectively, we can shift our workplace dynamics toward mutual respect and understanding.

Disclaimer 1: Sometimes people will just be patronising and malicious. Don’t dwell on them.

Disclaimer 2: I asked ELM to proofread the final copy of this blog (and used some of its proposed improvements)

About the author: Kasia Kokowska is the Marketing, Communications and Outreach Manager at the School of Informatics, at the University of Edinburgh. She has an MA in Journalism and Social Communications and an MSc in science Communications and Public Engagement. She’s a member of STEMPRA and CIPR.

Equality, diversity and inclusion in AI research – why should we care, and what can we do about it?

Research in AI is an increasingly exciting and fast-paced environment, with many new interesting features and applications available at a wider scale. However, it is also the topic of heavy criticism for often failing to represent and serve minority groups, which have historically been underrepresented in conversations about technology. Being PhD students in the CDT in NLP, we think it is extremely important to keep up with issues regarding equality, diversity and inclusion (ED&I), both to improve our own work but also to be critical about new advancements in the field.

Because of that, we are currently hosting a reading group in ED&I once a month, open to all postgraduate students and staff from the School of Informatics.

Anyone involved can choose a paper which they think is of interest, no matter whether it is their own work or not. Although attendees are encouraged to read the paper beforehand, this is not a requirement as we start with a ~15 min presentation. Afterwards, an informal group discussion follows, which allows everyone to comfortably express their ideas and ask questions. For the past few months, the sessions have had a very friendly atmosphere and we have learned a lot from each other about how to be more mindful researchers.

Through the ED&I reading group, we’re hoping to raise some awareness on how issues relating to equality, diversity and inclusion can impact current AI research, but also how AI research can have consequences in areas which have a direct or indirect impact on society. We also aim to foster a welcoming and inclusive environment where researchers can share and discuss their ideas on how AI research is impacting our society. We hope that attendees leave with thoughts on how their choices as a researcher can make a difference for people who have often been left out of the conversation about AI and how their choices can change that.

From the past few sessions, we have learned a lot from all the people who have presented and whom we have shared a discussion with! Our past sessions have covered:

Everyone has an accent by Nina Markl and Catherine Lai: a review on how “accent” and “accented” terminology is currently being used in speech research, inviting to be more mindful about terminologies being used.
Exploring Sources of Racial Bias in Automatic Speech Recognition through the Lens of Rhythmic Variation by Li-Fang Lai and Nicole Holliday : A study on how African American voices is under-recognised consistently by Automatic Speech Recognition systems and how this could be caused by differences in speech rhythm between Black and White American speakers of English.
Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models by Eddie L. Ungless, Björn Ross, and Anne Lauscher: A study on how large image models such as DALLE and Stable Difussion (mis)represent non-cisgender identities, and proposed solutions to the problem.

Our next session will be on Tuesday 30th April and will be covering issues related to the use of deep learning to identify transgender and gender diverse patients from electronic health records (A deep learning approach for transgender and gender diverse patient identification in electronic health records).

With AI being an exciting and constantly evolving area of research, we believe that issues of equality, diversity and inclusion are more important than ever for researchers to be aware of, even if their own topic of research is not directly linked to them.

If you are a researcher at the School of Informatics, we hope you’ll join us the last Tuesday of every month from 1-2pm for engaging presentations and fruitful discussions. Let’s all learn from each other! We usually meet in G.03, with the exception of 30th April, where we will meet in IF 1.15.

Artemis and Ariadna

Subscribe to inf-edi-reading-group@mlist.is.ed.ac.uk for notifications on next sessions

Apr 22, 2024

Culture consultation

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!

Apr 3, 2024

Reframing “Bias” in AI Research

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).

Apr 23, 2023

Category: Bias