A worker-centric approach to AI.

 

Achieving more inclusive and equitable technological development requires placing greater emphasis on workers and their professional growth. This involves broadening the focus of traditional goals of maximizing productivity and efficiency, to foster skill development and empower workers to adapt to and thrive in a rapidly evolving technological landscape. Increased automation in recent decades has contributed to higher productivity and lower prices, but the distribution of benefits has been largely in favour of capital. A worker-centric approach can contribute to an economic model that is socially and politically sustainable. Translating technological progress into shared prosperity requires labour-friendly policies in three stages: investments in education and skills, in pre-production; labour protection and worker empowerment, in production; and progressive taxation, in post-production. For example, such policies were implemented in the United States and Western Europe during the technological transitions in the early twentieth century and in the post-World War II era. A basic step is to empower the workforce with digital literacy, reinforced through all stages of education and lifelong training systems that incorporate digital skills in curricula and are tailored to different occupations, to prepare for possible future transformations. Technological advances continually perpetuate and amplify inequalities, and it is important to directly target inequality that arises during production. With regard to jobs that are highly exposed to AI automation, Governments need to help workers transitioning to new occupations and tasks, through reskilling training and tailored social protection measures, for a smooth transition process. Workers whose jobs are subjectto AI augmentation can also benefit from upskilling programmes to acquire new complementary competences, in order to make use of the latest technologies, and enhance their roles to include high-value tasks. To build trust and acceptance, workers should be actively involved in the design and implementation of AI tools. Job workflows and tasks should be rearranged to integrate AI effectively while addressing workers’ needs and maintaining meaningful human roles. 

Collaborative AI systems should empower rather than replace workers, foster job satisfaction and create opportunities for personal and professional growth. Labour unions and worker representatives can play a key role in shaping such collaboration. During previous industrial revolutions, for example, unions helped set wages, working hours and safety standards. Similarly, they can provide a voice to workers worldwide, to direct AI towards a worker-centric transformation with a more equitable distribution of productivity gains between firms and workers. Global union federations, such as UNI Global Union, are active in safeguarding workers’ interests and human rights in theage of AI. For example, UNI Global Union has issued top 10 principles for ethical AI and negotiated over 50 global agreements with companies, to secure and enforce the rights of workers. Setting a course for AI systems that enhance and complement human skills also depends on robust public policy. This should include increased R&D funding, strategic public procurement and targeted tax incentives for human-complementary AI technologies. Some countries have lower taxes for capital than for labour, thus encouraging technology for automation rather than for labour augmentation. Consideration should be given to whether and how existing measures, such as tax rates, tax credits or deductions and accelerated depreciation, might incentivize technology and business development that is more labour-friendly and guide enterprises towards human-complementary AI technologies. To prevent deskilling and mitigate the risk of brain drain to developed countries, it is essential for developing countries to improve labour market opportunities, provide continuous upskilling training and establish clear career development pathways. The private sector plays a leading role in AI, due to the concentration of resources, expertise and substantial financial investments within large multinational enterprises. Yet such companies can collaborate with Governments and academia on capacity-building initiatives that foster quality employment, such as placement programmes, apprenticeships and industry– academia research partnerships. Smaller developing countries may have less power to negotiate for socially beneficial public–private partnerships, but can still aim to maintain or improve standards and avoid a dangerous race to the bottom. A worker-centric approach is part of a more general strategy to prepare for advances in AI, which is addressed earlier.