Bias

How applying basic psychological virtues could help advance science

Science and spirituality are often not compatible, because spirituality usually entails believing in God or a supernatural being that often does not reside in the materially oriented scientific way of thinking.

Image (From Canva @prerna-madans-team)

Religions were primarily developed to guide people in their everyday matters, and to pass down values of harmony and not division, such as compassion, acceptance, humility, kindness, forgiveness, and refraining from injury or revenge against others. Over time, the application of these virtues in the smallest matters of life has diminished. However, if we return to practising psychological virtues now, I believe great progress can be made, including in science. I will illustrate this using an example from my work.


Since 2018, together with my colleagues at UCL, Oxford University and the University of Edinburgh, I have been working on a meta-analysis (which in the words of Richard Dawkins – is “an analysis of analysis”) determining the association between oral hormonal contraceptives (OCs) and depression in healthy women. Following the strict criteria, we included 14 studies with a total of 2.5 million pill users. The results of the analysis showed that in users of contraceptive pills, the incidence of being diagnosed with clinical depression increased by 27%, and the incidence of being prescribed antidepressants increased by 25% compared to non-users. Depression is one of the most commonly reported side effects of any hormonal contraceptives (see the Lowdown), including pills, patches, intra-uterine devices, such as coils, injections, implants, and rings.


Now let’s look at what we found over so many years of delving deeper into the topic:


Very early in our research, we collated a list of prescribed contraceptive pills, and their patient information leaflets. We noted that depression or anxiety are reported in ‘1 in 10’ or ‘Very Common’ categories for almost all types of contraceptive pills. However, we also noted that the 2011 Cochrane Review on this topic has not mentioned depression a single time. The omission of this important data remains unclear and questionable, especially from a body such as Cochrane.
We also noticed how language around the reported side effects in more recent pills has been changed. When reviewing the patient information leaflet for the latest contraceptive pills (e.g., Opill), the terms depression or anxiety are not listed, but are replaced with the term “nervousness”. There are several plausible explanations for this. One is that the trials were conducted on women who already had these as pre-existing conditions (depression, anxiety), in which case, these don’t need to be categorised as a new side-effect. A second possible explanation is that all mental health effects were combined into a single descriptor “nervousness”, which is reductive of the border and more serious mental health side effects of contraception.


A lot could be said for the widespread push-back and lack of acknowledgment of women’s experiences regarding their health, even by the choice of scientific methods. Perhaps one of the most important discoveries while performing our meta-analysis was how little a layperson would know about the methods used in the studies. Almost all studies that showed no effect between OCs and depression used unclear methodological choices. For example, three (21%) of studies treated women as “non-users” if they have been on birth control for less than one month, three months, or six months. This is ambiguous because women become users of contraception the very day they first take the pill or a hormonal contraceptive. This is almost unimaginable in any other type of drug testing. Furthermore, these women were  grouped into “non-users”, which could vastly underestimate the incidence of depression in OC users.


In yet another large study, the methodological decisions became unclear when the authors excluded the category of women who had symptoms of depression combined with symptoms of anxiety. The reasoning seemed to be that women should show only one or the other form of the disorder, again something that is not entirely clear and runs counter to modern diagnostics, which treats the respective diagnoses as two sides of the same psychological coin. In adolescents who have the strongest association between depression and hormonal contraceptives (and as data suggests, persisting and irreversible), it is a common occurrence to experience depression coupled with anxiety. But again, why are such choices made in research that should be sensitive and not omissive of the very experience they are striving to study?


Our meta-analysis was not the first, but the second in the world on this topic. A group of researchers had already performed a meta-analysis in 2021 using a type of statistical method that, for reasons too complex to list here, is completely inappropriate for the data studied. For example, the authors in this meta-analysis made statistical conclusions, unsupported by the data – the authors claimed: “We provide quantitative evidence on experimental data that hormonal contraceptive use is relatively safe regarding the effect on depressive symptoms”, but the authors provided no evidence on the safety of hormonal contraceptives because they did not analyse any safety data! This sort of ambiguity should have been picked up by the reviewers at the least.


If we consistently apply the principles that are not favourable to human experience and wellbeing, and even negate them, we only drive ourselves deeper into confusion and to put it bluntly – “hell”. It has been over 60 years since the pill was first created, and the evidence for hormonal contraception causing a great deal of mental and physical suffering to many of its users is incontrovertible and overwhelming. In the words of Viktor Frankl, “The ultimate freedom given to human beings, at any moment in life, is one’s attitude in any given circumstances. Life is not primarily a quest for pleasure, as Freud believed, or a quest for power, as Alfred Adler taught, but a quest for meaning.” What, then, is the meaning of pretending we cannot see the extent of true suffering for contraceptive users? If we didn’t pretend up until now, we could have been at a different reality of contraceptive science. And this is how I see that “spirituality and science as one”.


Our meta-analysis is due for submission in The Lancet Obstetrics, Gynaecology, & Women’s Health in May 2025.

 

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.

 

Related content

If you haven’t, you should also read two great blogs about the impostor syndrome, written by Andrea and Eillidh:

Psst – don’t listen to me!

Am I even good enough to have imposter syndrome??


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: 

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 


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!


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).