Conversations surrounding AI often gravitate towards conspicuous harms, like algorithmic bias and job displacement, drawing attention from leaders and regulators.While strides have been made in addressing these concerns through regulations and ethics, there’s more to consider and these three key lessons will help leaders navigate AI’s complexities.The call for ethically developing and deploying AI systems is constant but often neglects contextual factors, treating AI in isolation.
This commitment requires cross-sector collaboration and incentive alignment around justice for people and the planet, challenging the competitive status quo.Don’t be fooled by the speed and scale that AI guarantees; complex problems require that we think and act slowly Approaching AI as a panacea akin to Procrustes’ bed, forcing all problems to fit a predetermined solution, risks oversimplification and neglect of systemic complexities.
Sustainable development demands a paradoxical blend of slowing down to understand systemic roots and speeding up to enact meaningful change. As Audre Lorde cautioned, relying solely on the master’s tools risks perpetuating existing power dynamics rather than effecting genuine transformation.In the coming decade, robotic surgeries may become routine, while AI advances could reshape job markets.Yet, it’s crucial to recognise that humans drive AI innovations, albeit amid widening power disparities between tech giants and other stakeholders.
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