In a world reliant on digital technology, the Henley Business School’s expert on the future of work, NAEEMA PASHA, looks at how the bias inherent in AI could be stifling creativity and keeping us in our comfort zones.
When Mark Twain said the best predictor of future behaviour is past behaviour, he could have been predicting how data science is used to build our world to match our needs and likes. But, do we want our needs matched all the time? If everything is carefully curated to our wishes, how will we stretch our curiosity and knowledge? We know that those times when pushed outside of our comfort zone we feel discomfort, but paradoxically can experience the best learning. Leaders may need to consider this as AI grows in depth and range. Our current world is now so reliant on digital technology, and it is making things smoother, but may also keep us in compartments that prevent creativity and even inhibit an ability to challenge.
In 2021, we may be surprised to know how much AI is part of our everyday lives. The number of smartphone users in Germany has grown in recent years, amounting to 60.74 million smartphone owners in 2020. In 2009 it was 6.3 million. We are also using our smartphones a lot more, many of us open our phones over 100 times a day, making several thousands of decisions based on what we look at. If you look at your smartphone you’ll see it’s packed with AI technology. From your Siri and Google assistants providing AI enabled voice recognition to respond to your questions on what the weather is, through to newer computer vision AI tech which will look at your photos and using pattern recognition technology offer you clever ways of identifying, sorting and adapting them. The apps on your phone are using AI to track your usage and offer you more closely matched solutions. Your phone AI and specifi cally the Machine Learning technology is using algorithms to start learning from the data it is given – based on all your data habits, this data collated often if you don’t even use the app. You might feel comfortable with the AI in Spotify offering you playlists based on your listening habits. You might be pleased that Amazon’s AI offers you products that fi t around your interests and shopping. However, if we pause to ask two things; are the decisions used in AI as clear as we would want, and, do we only want to get presented with what we have been socialised to having preferences for?
Let’s look at dating apps as an example. As we casually swipe or left or right on potential love matches, the AI will start to learn our preferences. It will also learn where we ‘rate’ in how we are swiped too. What the dating data shows that because there are innate biases in society, the app will respond to human prejudices such as race and age. As such some users may get shown to fewer or limited users and limit their chances of being matched. Dating apps are only responding to human prejudices and practices, they are not creating them. But there’s an uneasy feeling that AI is supporting our existing discriminatory practices. And the uneasy question is, should it actually be more challenging on stereotypes. It might be one thing in dating, but what about recruitment, if the AI feeds off historical inequitable race and gender practices a company could potentially be restricted in its growth. In short, if we allow AI to only give us what we want, are we as individuals and organisations still as able to be stretch beyond our comfort zone.
If we take this idea wider to solve the big challenges of our time, the most critical being the climate crisis. As organisations and countries make moves to a net-zero economy, AI is being harnessed at a greater level. Indeed, for such a multi-dimensional challenge of Climate AI, we must consider how can data behind be used fairly, ethically and transparently – and shared across nations. We would not want poorer nations to fare worse than richer countries on say data on fossil fuel usage, but we can’t know, unless the data is shared openly. Companies such as Fero Labs, in Germany uses AI and Machine Learning technology to work within factories to reduce waste, and improve energy efficiencies. As they point out, explainable and actional AI is what is needed to progress, as it is critical that such systems show biases are addressed. Climate AI needs to be deployed to make even faster and more accurate predictions – given the scale of the task ahead, for example in making climate simulations quicker. While we build complex Climate AI models we will need to also ensure that the AI is as explainable and bias free as possible, and we need to challenge the data so not to stay in a comfort zone – or we can end up with complexities we were not expecting. To plan ahead, we can look far back to the wisdom of the Marcus Aurelius, the Roman emperor and a Stoic philosopher for guidance, as he said, “Never let the future disturb you. You will meet it, if you have to, with the same weapons of reason which today arm you against the present.”
Naeema Pasha is Director of EDI, Careers Professional Development and Future of Work in Henley Business School where she also established World of Work (WOW) to explore future of work readiness. Her doctoral research looked at key factors that enable people to succeed against the backdrop of technological change; her recent research examines the range of infl uences on work, such as AI, Automation, Diversity & Inclusion and Quad-Generations.