12. How can we better measure the value of trust?
Despite its importance, trust in the companies at the heart of our economies is not high. Only 58% of finance leaders feel the public has sufficient trust in business, and rebuilding trust means creating models of transparency and effective reporting. This includes improving self-measurement – and not just corporate balance sheets, but nonfinancial data as well. Improving the breadth of reporting won’t just rebuild trust with the public; it will also give businesses the self-knowledge to make tough strategic decisions.
Read more: Are you measuring everything you need to build trust?
13. Can corporations translate urgency into action on climate change?
There’s no getting away from it – one of the major drivers of climate change has historically been commercial activity. Keeping projected temperature rises within the 1.5 ̊C range recommended by the Paris Agreement means acknowledging this. Again, reporting and measurement is key. Unsurprisingly, different regions and sectors outperform others in the quality of their climate reporting and disclosures. But building trust and taking direct action is essential if we are to effectively manage climate change.
Read more: Can your board drive transformative action on climate change?
14. Can technology and trust evolve together?
The good news for organizations looking to enhance oversight into their own operations, and improve the way they communicate that trust to the public, is technological advancement. For instance, AI can provide organizations with much deeper, more insightful views of their data, allowing them to see and understand their own operations and identify and rectify critical regulatory, compliance, or other issues ahead of time.
Read more: How trust in technology is raising the bar in the audit industry
15. How can AI remove human bias rather than embed it?
Despite its benefits, AI comes with its own trust-based risks. While much of the paranoia around AI is overblown (no, robots aren’t coming to kill you – not yet, anyway), it is still a powerful and new technology and, if used improperly, could undermine trust in the system. For example, an AI fed on flawed data sets may replicate and exacerbate data flaws, worsening entrenched problems around things like gender or racial biases. Making the most of AI will mean being actively aware of these complications – and actively working to anticipate and mitigate them.
Read more: Why we need to solve the issue of gender bias before AI makes it worse