Dr Fiona McNeill

Heriot-Watt University

Can you tell us about your journey to your current role now?

I was really interested in Artificial Intelligence (AI) and practical applications of it – I did a Masters and then a PhD in AI so that’s my background. Because of the way that university departments work, I ended moving in to the Computer Science (CS) department as a computer scientist. AI is a branch of CS but I don’t have the common or traditional CS background as a lot of my colleagues do.

When I did my PhD, I was interested in automated Maths – that was the research group I was in – and I became increasingly interested in the ways we can apply automated reasoning techniques to data and data mismatches. I was particularly looking at when you’re trying to communicate automatically and people have different ontologies – different ways of viewing the world and there are failures of reasoning – how can figure out where mismatches are occurring. For example, if I ask you about the names, dates of birth and addresses of employees, and you have records about first names, surnames, ages and postcodes of staff, we can figure out that you have information that’s probably useful for me, even though it’s organised differently, using different terminology and not completely overlapping. People are really great at this – finding shared meanings – but machines are really rubbish at this. If things aren’t identical, communication doesn’t work.

Since finishing my PhD, I’ve moved in to more mainstream database work. My central research question if you like, is: if I want to get as much data as possible as quickly as possible out of multiple data sources that probably use different terminology, different structures, different levels of detail, and that I might not even know that I will want to get data from until run time, how can I do that?  Data is extracted from databases using queries, but to do this, you need to set up exactly what the query will be like prior to run-time, so you can’t access data dynamically without prior planning. When you want to extract from a database using a query, you need to often know how a database is formatted, know a lot about the database and speak its language and if you request data that doesn’t match that, you’re not going to get an answer back. I’m interested in how we can be more flexible than that.

I’m particularly interested in the crisis management domain. In this domain, decisions need to made quickly and there’s lots of data out there but decision-makers have often made poor decisions, based on limited data, because they weren’t able to access all the data they needed or only had part of the picture. It’s old school in this field because they tend to request data from a particular person in an organisation, who then gives it to them. Also, as databases are getting really, really large it is increasingly difficult for humans to know what is in there.

I’m interested in machine support for data sharing and how you can help organisations communicate when their data may be expressed very differently. People can be very cautious about sharing and often gate keep data, which is a challenge, you know, ‘this is my data, this is your data’.

Crisis management does a lot of planning ahead to try and foresee what actions they will need to take and what data they will need to assess the situation, which is very important, but it doesn’t always work out this way because crises are incredibly complex, you always need data you didn’t anticipate – and then you need to go out and find it, often under immense pressure. That’s what I’m interested in.


Is diversity and gender equality important in this work, what do women bring?

It isn’t necessarily women, you need good people doing good things!

I wouldn’t say I’ve experienced a lot of overt hostility in my work as a woman. I feel like I’m quite confident but I’m very aware of being a minority often in my line of work and even if you’re OK with this on many levels, it does affect how you see yourself and how others see you.

I’ve definitely had instances of sexism – for example, I’ve been in a conference room and people have asked me to go and get the coffee not realising I was a participant! This is actually quite a common experience for women that they’re assumed to be more junior than they are or in more administrative roles rather than decision-making.

In crisis management, where I’m interested in how tech can support this work, it is male-dominated but I must say it is much less dominated than other areas I hang out, like data and computing conferences and events, where you may see around 20% women but in crisis management it’s more like 30 – 40%. Still not equal! To be honest, when I’m the only women or one of say, two or three women, in a meeting or room, I don’t tend to notice. Then when I leave that and go to a work space more gender equal, it’s a shocking contrast and you realise that it has had an impact on you. You realise then that the other space isn’t what we should be going for, and that the one with greater parity is, well, a lot nicer.

These are observations I’ve had more and more as I’ve got older. I’ve done more reading around it and you do wise up as you get older, I guess. Plus, the data is telling us that we’re not in an equal society and that this is negatively impacting women.


Can you tell us more about the different plates you have spinning, what other work are you involved in?

So, data sharing in crisis management is the main research focus for me.

I’m also interested in multilingual work – how do you bring together data when it’s in different languages, it’s a complex question! I’m currently doing a small project with a Gaelic college.

There is a tech problem that English has saturated lexical resources and for other languages, a lot of development needs to be done and for minority languages, these resources can be non-existent. I think that representing minority languages is so important for data, as currently they’re pushed out and ignored. So that’s something I’m really interested in: how do minority languages fit in to the data boom!

I do a lot of things not explicitly related to the data work I do, such as outreach work around women and girls in STEM.

I’ve very interested in computing in schools, which is not great at the moment, as the numbers taking computing up has gone down and then that’s even worse when we look at girls. It was abysmal anyway and now it’s gone even lower. Over the last few years, there’s been a lot of intervention work to get people in to STEM, particularly women and girls. The results are – it’s not happening. We’re not seeing the results that we forecasted. It’s a huge problem for the economy because the technical industry is huge in Scotland and there’s so much missed opportunity. CS is so accessible and can help build really bright futures, so it’s really frustrating. So I will be starting research around what is going on – how are schools helping young people to make decisions, what decisions are young people making and why, what are the misconceptions that young people have. I’m on a lot of panels and we talk about these questions loads and to be honest, there’s a lot of guess work going on – which is making a lot of logical conclusions, don’t get me wrong, but isn’t necessarily grounded in data and research.


What do you think are the biggest challenges, from your perspective?

I feel strongly that this all comes down to socialisation. We are socialised to believe that certain people do certain things. We’re taught to think that certain people do STEM and certain people do CS – and CS in particular is very male-dominated but not only that, it’s dominated by a particular ‘kind’ or image of maleness, geeky, White or Asian, no social skills – all these stereotypes are very dominant.

Last year, I worked on the Royal Society of Edinburgh (RSE’s )Tapping All our Talents report and I was focused on the schools and early years stuff. It was very clear that as soon as children are in early years, they have certain ideas about what people should be doing. In fact, there is strong evidence that these ideas are taught right from birth, through what toys children are playing with, what they are dressing up as. This is not coming from the child, they are taught to the child, but after a while it does come from the child too as they’ve learnt that’s what is expected from them. We are steering girls from STEM very, very young and this is based on socialised stereotypes on what girls like and what they should do – they aren’t given STEM toys to play with. CS is an interesting case because it used to be very female-dominated when it was low status and low paid and then it became more high status and paid and women got pushed out, as with other industries. During the computing boom in the 1980s when computers were introduced to the home, they were very heavily marketed towards boys as well and girls didn’t have access.

The lack of women has a lot to do with how girls perceive themselves…and also, if you’re the only girl in a classroom of boys, you’re probably not going to want to go in to that discipline and that is maybe the ‘conscious’ element of choice. You’re really breaking social norms if you enter that space and you also don’t see yourself reflected in your peers – this is maybe the ‘subconscious’ element. So there’s that mixture of conscious and subconscious reasoning. I think these are the main problems and they are not easy to solve.

It is so hard to get the approach right. Even in regards to this project – I do think it’s a difficult balance, getting those celebratory and critical elements in when it comes to gender equality. For example, on Ava Lovelace day we’re told at work to be really upbeat but there also needs to be context, and conversations about oppression and so on. I understand that if you make it too focused on those difficulties it can be off-putting for people but it’s so important to think about that balance.


What would you recommend to women and girls?

Computing is very broad, it’s a huge world and there’s all sorts of creativities. I don’t have a traditional background and I’m not interested particularly in coding or programming. I like philosophy, design, AI. There’s all kinds of options for all kinds of people, you don’t need to fit the classic mould of what you think a computer scientist and data scientist should be.

It’s a really important message – you can do a computing degree without any computing qualifications. I have several friends without a computer degree, one of my friends senior in computer had a PhD in philosophy!


Lots of great messages there – what holds us back?

If you actually look at what it’s like working in tech, it’s pretty hostile for women, particularly the gaming industry. It makes women feel like they don’t belong. It’s sort of a self-fulfilling prophecy.

There are a lot of common barriers as well, like women’s contributions not being valued and women being spoken over and not included.

So on the one hand I’m like ‘come on, you can do it!’ and on the other hand it’s like ‘what am I encouraging them in to!’ We’re never going to solve the issues around women in STEM if we don’t push back and fight but that requires a lot of pioneers, who are excellent women, but it’s very hard work being in that constant fight mode. Doing that on top of your career, especially in the face of opposition, is stressful and you’re probably not going to achieve if you don’t take on those roles. It’s really difficult.


Is there anything you wish you could do more of in your work?

I’m really focusing on women in STEM and education. I feel like most of the things I want to do, I can do them, so it’s not an opportunity issue. It’s a time issue – I’d love to spend more time on research and developing my classes I teach.


It sounds like you find your current work supportive. Are they supportive of you as a woman?

Mostly, yes. I’m very lucky with the people I work with but I also deliberately position myself in environments that align with my values. I work with a lot of men as well as women, who are worried about the current situation and want to see change happen. Not all my colleagues, though. When I organise, say, women’s events there is sometimes some complaining. I also notice inequalities among students, where male students will react negatively to women’s events and women will get stick for going. Some of that exists at a staff level.

I’ve worked in some pretty toxic environments before but I’d say in general, we’re on a good track where I work.


Is there anything you look forward to?

Ooh – I really enjoy collaborative work, both in my data and social.

There are two projects I’m looking at the moment, one focused on refugees and one focused on favelas in Rio. Getting funding for that sort of work is difficult and I’m passionate about work that is oriented towards social good and stuff that makes a difference – that sounds really cheesy! (laughs)

To be really satisfied in your life, I do think you need that ‘big vision’ that drives you.


What do you do when the working day is over?

I travel a lot, I love the carefreeness of it. I have two kids and every year, I take one of them (we take it in turns) travelling and we go too far flung places. My daughter is 11 and we took the train to Turkey at Easter. The year before I went jungle trekking with my son. I love that one-to-one time, it’s a totally different relationship and travelling is more relaxed when there’s just me and one of them.


Do you have a hero or heroine?

I would say the women that have gone before me, carving out careers in male-dominated environments – right from Ada Lovelace and Mary Wollstonecraft to my senior colleagues at work.  Things are far from perfect now but we’ve come a long way, and a lot of that is due to brave, pioneering women who put up with a lot to be able to do the work they love, blazing a trail for those of us coming after, and challenging the social narrative around what a woman could and should do.


Image of Dr Fiona McNeill
Picture by Lesley Martin

There’s all kinds of options for all kinds of people, you don’t need to fit the classic mould of what you think a computer scientist and data scientist should be.

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