Digital Innovation & Entrepreneurship
exposed to unconventional ideas. Professor Lifshitz said: “Managers ask their teams to think outside the box but give them tools that keep them within it. Breakthrough ideas need systems built for exploration, not just efficiency.” By building an algorithmic layer on top of Google Search, researchers created an exploration- focused tool that deliberately surfaced more diverse concepts. People using this tool generated twice as many distinct ideas as those using a standard search and overall creativity scores rose significantly, with experts benefiting the most. “It’s not just about using algorithms to produce more ideas,” said Professor Lifshitz. “It’s about optimising human-machine collaboration for innovation.” One way to do this is to add “strategic friction” to the process. Behavioural scientists at WBS found that forcing people to wrestle with AI tools, rather than accepting the first response that seemed ‘good enough’, encouraged them to evaluate their findings and
promoted innovation. Professor Chater said: “The goal isn’t to make work painful. It’s to ensure people use enough effort to reuse the information they have found effectively and keep learning.” 5 . Algorithmic bias. Amazon was forced to scrap its AI recruitment tool in 2018 after discovering the software was biased. Because the model had been trained on 10 years of hiring data, it ‘learned’ that men were more likely to be given senior jobs at Amazon and penalised CVs that included the word women’s. This included downgrading graduates from two all-women’s colleges. This kind of bias doesn’t just penalise individuals from under- represented groups. It harms companies by preventing the best candidates being promoted to positions where they can benefit the organisation and may alienate workers to the point where they leave the firm altogether. Human oversight can play a vital role – for good or bad. If the worker
has an unconscious bias, they could reinforce the flaw in the algorithm. However, human operators can also learn to recognise the bias and adjust their behaviour accordingly. To facilitate this, companies should ensure staff are properly trained and create a framework to provide them with feedback on how they and the AI are performing. These decision-makers should be penalised for approving bad choices made by the AI and rewarded for making good decisions. Anh Luong, Assistant Professor of Business Analytics, said: “We found that it was human decision- making that improved through this process, not the decisions made by the AI model. “At this stage at least, people continue to be better at adapting than AI systems.”
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Sustainable Development Goals
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