By Rebecca Whitworth, Manager, Software Engineering at Red Hat. 

Artificial intelligence (AI) is an exciting area of technical development. As the tools and methodologies advance, many organisations are looking to use AI to improve business efficiencies, bring innovation to customers faster, gain actionable market insights and more.

However, the rush to put AI in place without always knowing what it can be used for, or how to use it, can lead to problems with the systems and the data itself. We have heard many stories of when AI makes the “wrong” decision due to built-in biases, and in some cases, the outcome can be life or death.

This is a challenge that open source project TrustyAI – built on Red Hat OpenShift and Kogito – is looking to address.

AI gone wrong

AI is essentially a set of priorities and decisions a system has been programmed to follow. And because humans are responsible for that programming, our flaws become the system’s flaws. This happens in two main ways. One is cognitive bias that comes from the person, or people, who train the data model and build bias into the AI logic. The second problem is a lack of data, or a lack of diverse data. For example, we’ve seen cases of datasets that are dominated by white males, meaning AI trained on this data filters out anyone who doesn’t fit these characteristics.

There is a growing amount of research about the transfer of bias from humans to AI. As AI becomes more prevalent, it is being used to decide critical life moments like whether you get into college, get a mortgage, qualify for a medical operation, and even for determining a prison sentence, which makes the negative outcomes easier to spot.

The data science community is very aware of the need to stop systems perpetuating bias, and all the big tech companies are now looking at this. IBM’s AI Fairness 360, Google’s “What-If Tool” and Amazon SageMaker Clarify all go to show how significant and competitive this field has become.

Identifying the problem

If you aren’t proactively looking for bias, you may not notice if the AI you are using for hiring is selecting people based on their gender and name instead of their technical skills and experience, for example. There hasn’t been a simple and reliable way to test the balance and accuracy of an AI model, so the first time a business knows it has an issue is when it gets some negative PR or someone brings a case against it. This black box opaqueness has also become a legal risk with the advent of the General Data Protection Regulation (GDPR), where anybody in Europe has the right to ask for their data and how it is being used.

TrustyAI is a standalone piece of software that plugs into any AI application, regardless of what environment or technology it’s running on. It introspects the system, looking at all the various inputs, and maps how these influence the outputs. That analysis is served up in easy-to-visualise charts, which make it clearer if any biases exist. The user can alter the values to see the effect of different criteria.

For example, with a rejected mortgage application, you’d expect a different outcome if the applicant had a higher credit rating or earned more money. But if TrustyAI finds that by only changing the applicant’s ethnicity or postcode you arrive at a different outcome, then it’s clear the system has bias. We call this Explainable AI — XAI for short.

TrustyAI in action

TrustyAI has been built with Kogito (an open source, end-to-end business process automation technology) and Red Hat OpenShift. So it is cloud-native and can be deployed to any system running on any environment. It is written in Java and integrates with Python, the language data scientists generally use, so a user only needs to open up a notebook and apply library functions to their datasets. There’s no reconfiguration required.

Open source has been critical to TrustyAI. Tapping into open communities has enabled us to develop at a faster pace. For example, KIE (Knowledge Is Everything), an open source community for business automation, has been a brilliant partner. Its repository hosts the TrustyAI code and its members continually perform testing, offer fixes and suggestions and share their datasets. Without this collaboration (our in-house project team is myself, three developers and one architect) progress would have been much slower.

We’re really excited about the impact that TrustyAI can have. Reputational management can make or break a business, and there’s a real clamour to mitigate the risks of AI. Understandably, businesses don’t want to slow down their adoption of AI, and TrustyAI helps give them the confidence to push forward, while also prioritising transparency and accountability.

As a project team, we are looking at how to apply TrustyAI across Red Hat’s portfolio to add strategic value — both internally and for our customers — while continuing our research on all things AI, from cognitive bias and ethics to developments in data science and algorithmic best practices.

About Red Hat

Red Hat is the world’s leading provider of enterprise open source software solutions and services, using a community-powered approach to deliver reliable and high-performing Linux, hybrid cloud, container, and Kubernetes technologies. Red Hat helps customers develop cloud-native applications, integrate existing and new IT, and automate, secure, and manage complex environments. Follow Red Hat on Twitter: @RedHat

By Allyson Kouao, Associate Solution Architect at Red Hat.

Less than two years after graduating university, Red Hat’s Allyson Kouao was shortlisted as a Rising Star by Everywoman in Tech Awards. Alongside outstanding achievements in her day job, the nomination also recognised her voluntary efforts in creating a new UK & Ireland chapter of Red Hat’s global community “Blacks United in Leadership and Diversity” (B.U.I.L.D.). Here she tells the story of how she did that, and what other budding community builders can learn.

Ada Lovelace Day (celebrated on the second Tuesday of October) is always a welcome moment to remind ourselves there is still a lot to do to leverage women working in technology. October is also Black History Month where I live in the UK. The timing may be coincidental, but it should also prompt us to look at diversity in all its guises; true progress is when we include all groups.

As a black woman working in technology, I have discovered the impact of community efforts on both fronts. By bringing people with shared and disparate interests together, progress can be made further, and faster. I started at Red Hat shortly after graduating from university. Other than being a STEM Ambassador for the university, I didn’t have much experience of outreach, so I needed to figure it out as I went along.

Here are my top tips for others looking to build a community.

See what else exists

You are never completely alone. Look around your organisation and sector to see what other communities are already out there that you can buddy with. In my case, there was already a global B.U.I.L.D. community out of the US, the first employee-led movement at Red Hat to organise around improving leadership opportunities and career pathways for black colleagues. Not only did their brand equity open doors for me; they remain a constant source of advice. We also have an active Pride community at Red Hat, and its leaders advised me on how to get the new chapter rolling.

Articulate the opportunity

Sometimes, it’s obvious a problem exists. Other times, data can help make your case. There is no shortage of diversity and inclusion research, and most large organisations are now legally required to report on their own progress. That said, some people can find numbers too abstract and easy to overlook. Nothing hits home like a real-life recount. Find these, and amplify them. For example, we ran a campaign called 14 Days to a More Knowledgeable You in which we shared daily content – through articles, podcasts and personal stories from B.U.I.L.D. members – to share personal experiences and insights on a range of topics.

Start small, build slow

Sustainable change is best done from the bottom up. So don’t rush it. Build your army of advocates, conversation by conversation. For one thing, it’ll allow you to gauge interest levels. But more importantly, people who feel marginalised may be reluctant to join formal groups.

Welcome everyone

When I first established B.U.I.L.D in the UK, I was asked: “Can I join? I’m not black.” The answer I advocate is ‘yes’, because the journey to inclusion is a team effort – not solely that of the underrepresented group – as we continuously work on empowering, supporting and becoming more knowledgeable versions of ourselves.

In B.U.I.LD. UKI, the input of our non-black colleagues has been crucial in understanding how to further our reach and influence more effectively. As a leader of a community, you need to demonstrate that same appetite to engage beyond your own group. At Red Hat, I am a member of a variety of communities, including our Neurodiversity, Pride and Women’s Leadership communities.

And then there’s the practical benefit of inclusivity; the bigger your community, the louder your voice, and the more you can do.

Balance out the heavy

Even if change is the name of the game for your community, don’t let it override enjoyment. Celebration is important too, and can be a very powerful motivation technique. Any manager will tell you that people having fun are more engaged and productive. The same goes for a community.

The social calendar at B.U.I.L.D has always been a key part of what we do. It includes film clubs, quizzes, cook-alongs and an online space where we can share music, writing, recipes… giving everyone the choice to get involved in the ways they choose to.

For those who feel isolated or lack confidence, these events and forums are often the easy introduction they need. Others may be attracted to the fun first, and the supporting context after. The key is to get the balance right. That will be unique for each community. Flexibility matters too; striking the right tone for the social and political moments of the time. Listening to your members is how you find those sweet spots.

Don’t obsess over end goals

A vision for the future is inspiring; defining an end-state can limit your creativity. For starters, end-states usually mean numbers, which can feel abstract and a bit random, especially the further to the future you look. They can also be misleading. For example, it is important to keep a close eye on organisations that parade their recruitment diversity statistics, but fail to share retention and career progress data.

Objectives can be subjective too. You might spend more time as a community defining your end goals than actually doing anything about them. The world changes pretty fast; what may be important to your community now could be superseded next year. Therefore, anchoring yourself to a set of targets may limit your freedom to move in new directions.

Overall, a community should be about creating and sharing experiences; overcoming challenges as one, advocating for each other, celebrating together, being in the moment.

Make the commercial case

Many organisations talk a good game about supporting their community groups, but some may need a prod on how to actually back that up. Workplace communities are an integral part of employee welfare, and are critical to productivity and profitability too. They should be funded and resourced to give them the best chance of making a positive difference.

If an organisation is tone deaf to their moral obligations, then there is plenty of evidence on the commercial benefits that active communities bring. Find the proof points that make your community a compelling business case, and present it like any other investment opportunity.

Be kind to yourself

If your community falls short of your expectations, that’s ok. It’s good to have expectations sometimes, so you know where to continue to develop. Even if you have decided that building a community isn’t for you, think about the new contacts you’ve made and all the transferable skills you’ll have developed; communication, marketing, project management, stakeholder relations, and leadership, to name a few.

And beyond all that, there’s the personal triumph to take away — that you are contributing and making a difference. In a world where empathy and emotional intelligence are increasingly being seen as hallmarks of future leaders, the experience will stand you in great stead.

About Red Hat

Red Hat is the world’s leading provider of enterprise open source software solutions and services, using a community-powered approach to deliver reliable and high-performing Linux, hybrid cloud, container, and Kubernetes technologies. Red Hat helps customers develop cloud-native applications, integrate existing and new IT, and automate, secure, and manage complex environments. Follow Red Hat on Twitter: @RedHat

While Ada Lovelace lives on as an inspiration to women working and researching in STEM subjects through the activities of Ada Lovelace Day, her impact can also be seen in the literature. Using tools available from Digital Science, Simon Linacre looks at the ever-increasing amount of research surrounding one of science’s most fascinating figures.

Firstly, a confession: before starting work for Digital Science in early 2022, I didn’t know who Ada Lovelace was. I knew the name, and may have known she had been a scientist of some description, but I had no idea what she had achieved or when she had achieved it. Put it down to my preference for the arts in my education, a patriarchal society or just sheer ignorance, but I had no idea what an inspirational figure she was. Of course, now I know a lot more having worked with Digital Science in its support of Ada Lovelace Day, but I wanted to know more. As someone who has worked in scholarly communications for most of their career, what does her legacy look like in the current literature?

At first glance, it is pretty significant. According to the Dimensions linked database – which covers 131 million publications – there are over 15,000 mentions of ‘Ada Lovelace’ in those publications, as well as 74 policy documents and 29 patents. There are even mentions in eight grant applications. Looking at who is doing the mentioning, it includes some major institutions in scientific research, including Oxford, Cambridge and University College London.

Having said that, most publications originated in the US and not the UK, which shows that her influence has not been limited to her country of birth.

This wider influence is also in evidence when it comes to research categories. While it is unsurprising that the most common research category with mentions of her name is Information and Computing Sciences (716 mentions), not far behind are Philosophy and Religious Studies (486), Language, Communication and Culture (271) and Human Society (240). Lovelace has clearly had quite a broad spectrum of influence, far outside of her ‘home’ of computing sciences.

And the influence appears to be growing. As you can see from the chart, despite a lull around 2018, interest in Ada Lovelace has been growing steadily in recent years, with some acceleration more recently when we look at total outputs of publications that mention her name.

Perhaps one reason for her popularity is the proportion of articles that mention her being open access, with 79% of them being free to read online.

Focusing again on the present day, Ada Lovelace has also made somewhat of a splash online. When we look at Altmetric data, we see that the increase in mentions trackable on the internet have also seen rapid growth since 2016, the year after her bicentenary, and especially in the last couple of years. In 2021 alone (see below) there were over 8,000 recorded instances, including 6,662 Twitter mentions, 1,028 Wikipedia mentions and 312 news items.

So, who was Ada Lovelace? These days, she is best known for being the first person to publish what would today be called a computer program.

Throughout her childhood she was fascinated by machines, and at the age of 17, she was introduced to the engineer and inventor, Charles Babbage, and his general purpose mechanical computer, the Analytical Engine. Lovelace and Babbage became life-long friends, and Lovelace came to understand the Analytical Engine in depth.

When Italian mathematician Luigi Menabrea published an article about the Analytical Engine in French, she translated it into English, correcting some errors as she went. Babbage suggested that she added her own footnotes, which she did, tripling the length of the paper.

In these footnotes, Lovelace wrote a set of instructions for the calculation of Bernoulli Numbers. Although Babbage had written fragments before, her more elaborate program was complete and the first to be published. She also speculated on the future capabilities of the Analytical Engine, suggesting that it could be used to create original pieces of music and works of art, if only she knew how to program it. She recognised the enormous potential of machines like the Analytical Engine and her vision bears a striking resemblance to modern computer science.

Lovelace’s work was truly ground-breaking and her achievements become even more impressive when one remembers that she was working from first principles with only Babbage’s designs and descriptions to guide her and no working computer to tinker with. Yet, the importance of her paper was not recognised until Alan Turing’s work on the first modern computers in the 1940s.

As we can see from the data, Lovelace is now a widely cited and mentioned mathematician who is increasingly influencing a broad range of STEM and social science areas. Not only does the legacy of Ada Lovelace live on, it has never been bigger or more important.

About Digital Science

Digital Science is a technology company serving the needs of scientific research. We offer a range of scientific technology and content solutions that help make scientific research more efficient. Whether at the bench or in a research setting, our aim is to help to simplify workflows and change the way science is done. We believe passionately that tomorrow’s research will be different – and better – than today’s. Follow Digital Science on Twitter: @digitalsci