We also need to ask ourselves to what extent is it desirable or ethical to relinquish control of key operational decisions in workplaces to AI. The potential longer term future of travel raises numerous ethical questions, for example: how far do we want the world of work to change?; how desirable is it?; what will the impacts be on worker health and wellbeing, particularly in those parts of the job market projected to be most affected?; in the event of being able to predict future injuries and ill-health attributable to work doing with increasing accuracy, how should such insight be used?; what will it mean for future recruitment to jobs?; and in the event of being able to predict future wrongdoing with increasing accuracy, what will it mean for regulation?
The challenges facing the world of work of the rise in so called artificial narrow intelligence are tangible and real now. In the event that such technologies advance further over the coming decades and the emergence of artificial general intelligence systems becomes a reality, such challenges are only likely to grow. Given prevailing views of the chances of this happening, it makes sense for the world of work to plan ahead. For example, if we are facing challenges now in trying to predict the likely courses of action that systems built around artificial narrow intelligence might take, then this is only likely to grow with the emergence of systems built around artificial general intelligence, where the range of possible decisions that might be taken are likely to be significantly greater and the systems are likely to be even more unpredictable. The emergence of artificial general intelligence will raise the bar with respect to the sorts of questions society needs to ask; questions of a more philosophical nature come into focus, such as: can AI systems be moral agents?, if so, how should we hold them accountable?, how do we prevent them from acquiring morally objectionable biases and discriminating?
A Royal Society policy project on machine learning sought to investigate the potential of machine learning over the next 5 to 10 years for the UK and the barriers to realising the potential. The work identified key areas where action was thought to be needed, these included: 1) the creation of a data environment that draws on open standards and open data principles, 2) the building of a skills base and research environment that can provide the human and technical capital to both apply and further develop machine learning, and 3) the creation of governance systems to address key social and ethical challenges.
All of the challenges identified in this series of blogs will need to be given due consideration over the coming decades.
This is the end of this series of blogs on industry 4.0 and AI, look out for a new series of blogs in the coming weeks.