4 pillars for using AI responsibly in a skill-based organization

This article is brought to you thanks to the collaboration of The European Sting with the World Economic Forum.

Author: Jeroen Van Hautte, Co-Founder and CTO, TechWolf, Andreas De Neve, Co-Founder and CEO, TechWolf

• As the world moves towards skill-based workforces more companies are drawing on AI to support them with this.
• This makes it crucial for AI to be used responsibly: without clear oversight, systemic biases will worsen in workforces and employee trust will be lost.
• By following four key pillars for using AI responsibly, skill-based organizations can ensure a transparent approach that benefits employees and employers.

Jobs are changing too quickly for traditional workforce models to keep up. This has given rise to a new term, the ‘skill-based organization.’ Studies indicate that 90% of business executives are now experimenting with building a skill-based organization.

Moving from jobs to skills is complex. It involves unravelling roles to a granular, skill-based level. That takes time and there’s potentially missing data if an HR or learning system hasn’t been updated in a while. Plus, the number of skills that an organization has is approximately twice the number of roles and jobs.

Organizations have realized that keeping track of dozens of continuously evolving skills across thousands of workers is no feasible manual task. So they turn to AI. This is a step in the right direction and the only way we can truly embrace the potential of the skill-based organization. But, we must tread carefully.

When people’s livelihoods are on the line, the AI used to influence those lives must be as open, transparent and ethical as possible. Tales of AI-gone-bad are, unfortunately, all too common. Amazon had to cancel a recruitment AI model due to bias against women. More recently, concerns have been raised that hiring and screening algorithms discriminate against people with disabilities.

It’s vital that AI is used for good in the skill-based organization, or all of the effort put into shifting away from jobs will be for nothing. Forming the basis of this approach are four key pillars for using AI responsibly:

Pillar 1: Know your data sources

An AI tool is only as good as the data sources it works with. Give it inaccurate data and you’ll get inaccurate results. AI, working at scale in your organization, can drastically increase bias if it is modelled on biased or incomplete data. Before you start giving your AI skills data to train on, you must audit your data to ensure it’s as accurate and representative as possible.

Ultimately, not all of your data will be objective or equitable. For example, in our work at TechWolf, we’ve discovered that male employees tend to over-report their skills, whereas female employees tend to under-report them. If you fail to understand such contextual factors when using skills data, you’ll find that your system excludes important talent. Undermining the entire point of the skill-based organization.

When evaluating data sources, organizations should keep four properties in mind:

A final note on this, we mandate using non-invasive data sources to avoid infringing on employee privacy. Avoid using invasive data sources such as email, private chat or any other data sources that could be seen as prying. A good check for this is to ask yourself if you would be comfortable if your manager had access to the data sources you’re intending to use — your emails, your Slack messages and so forth.

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Pillar 2: AI’s decisions are explainable

Robert Jones went down in history when he drove off of a cliff edge while blindly following his sat-nav’s instructions. When acting on an AI’s recommendations, you must understand how it has come to its conclusion. Algorithms that can explain their reasoning are more trusted and suffer from less bias.

That’s why there’s a host of legislation aimed at enforcing a certain level of explainability. In the EU, GDPR provides employees with the “right to an explanation” when algorithms measure or evaluate aspects related to them based on automated data processing. The AI Act is also making its way through the European Parliament. In the US, the National Institute of Standards and Technology has published its Four Principles of Explainable Artificial Intelligence.

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How is the World Economic Forum ensuring the ethical development of artificial intelligence?

The World Economic Forum’s Platform for Shaping the Future of Artificial Intelligence and Machine Learning brings together global stakeholders to accelerate the adoption of transparent and inclusive AI, so the technology can be deployed in a safe, ethical and responsible way.

• The Forum created a toolkit for human resources to promote positive and ethical human-centred use of AI for organizations, workers and society.
• From robotic toys and social media to the classroom and home, AI is part of life. By developing AI standards for children, the Forum is creating actionable guidelines to educate, empower and protect children and youth.
• The Forum is bringing together over 100 companies, governments, civil society organizations and academic institutions in the Global AI Action Alliance to accelerate the adoption of responsible AI in the global public interest.
• The Forum’s Empowering AI Leadership: AI C-Suite Toolkit provides practical tools to help companies better understand the ethical and business impact of their AI investment. The Model AI Governance Framework features responsible practices of leading companies from different sectors that organizations can adopt in a similar manner.
• In partnership with the UK government, the Forum created a set of procurement recommendations designed to unlock public-sector adoption of responsible AI.
• The Centre for the Fourth Industrial Revolution Rwanda is promoting the adoption of new technologies in the country, driving innovation on data policy and AI – particularly in healthcare.

Contact us for more information on how to get involved.

Pillar 3: Employees own their data

Employees generate data throughout the work day, with every document written, project finished and course completed. Ideally, the data generated and skills inferred from this will be portable across organizations and used by individuals to grow their careers. But at the very least, individuals must have the final say on whether skills are part of their profile or not — and if AI can use it.

Key to this is highlighting (and delivering on) the benefits of using skills for workforce decisions. Taking this approach suits employees, with 79% stating that they’d be okay with having their skills data collected by employers and a further 14% open to it, depending on the purpose.

Pillar 4: Share the uses and benefits

Employees are open to sharing their skills data with their employers, but many only wish to do so if the benefits to them are clear. If they get fairer hiring, tailored work experiences and growth opportunities, they are happy to share data. Additionally, let employees know how their data is collected, from where and how the AI uses it. You might need to do a bit of upskilling around AI to ensure all employees understand these points.

The four pillars: holding up the skill-based organization

The world is moving towards a skill-based approach and that increases AI’s role in workforce decision-making. It’s not a matter of if, but when AI comes to the skill-based organization. Set the right foundations by integrating the four pillars into your AI strategy. Without them, everything will come crumbling down.

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