In a recent Lexxion Delphi publication tackling Cyberwarfare, AI and Democracy and Digital Platforms , Gapsquare’s CEO & CTO share how artificial Intelligence, machine learning and other technologies are profoundly changing how we live today.
These new technologies & developments are at the forefront of conversations about future workplace dynamics and are set to revolutionise and streamline inclusivity in many industries.
Unfortunately, it is increasingly clear that old biases are being built into AI and machine learning.
As Zara Nanu, Gapsquare’s CEO argues in Delphi, emerging technology is based on existing biased data, and therefore mirror these biases.
Take, for example, Amazon’s scrapped AI recruiting tool, which was created to optimise hiring by observing patterns in successful CVs and looking for these in prospective applicants. Because the bulk of previously successful CVs came from men– reflecting the male dominance in the tech industry – the tool learnt to hire people from this same demographic.
For the team at Gapsquare, now is the time to ensure AI is less biased, more inclusive and fair and represents the best of what it means to be human. AI needs to open doors for a wider range of people and it needs to learn to look beyond old approaches and bias-filled data.
To achieve this, argue the Gapsquare team, we must start by improving diversity within the tech sector through a combined effort between educational systems, governments and HR in tech industries in order that these technologies are being created by diverse people and reflect unbiased data.
We also need to feel as though we can trust AI in order to fully reap its benefits, and this requires transparency. AI giants must be transparent about their processes, from data collection to evaluating their outcomes. They must be clear about how they define success, and admit to AI’s shortcomings.
In a unique step forward for the tech world, we at Gapsquare use machine learning to help explain pay gaps and pay inequality; our software looks at which business units and job roles contribute to a company’s pay gap, in order to recommend how to approach closing these gaps. As we begin to add more data to the tool – such as performance reviews and exit interviews – we enable our systems to make more intelligent decisions surrounding career progression. In other words, we are using tech to make workplaces operate in a more human way.
We are not trying to replace the human component of pay and reward decision-making. We believe the process of co-working with AI can free up time for existing staff to tackle higher-value problems. It’s about streamlining the process, rather than replacing jobs with machines.