Rapid advances in the development and adoption of artificial intelligence (AI) technologies provide new opportunities but also raise fears about disruptive labour market and workplace transitions. This working paper examines how social dialogue can shape the AI transition in beneficial ways for both workers and firms. It highlights that social dialogue can generally help foster inclusive labour markets and ease technological transitions, and presents new descriptive evidence together with ongoing initiatives from social partners showing that social dialogue has an important role to play in the AI transition as well. The paper also discusses how AI adoption may affect social dialogue itself, e.g. by adding new pressures on weakening labour relations systems and posing practical challenges to social partners, such as insufficient AI-related expertise and resources to respond to the AI transition. Based on these insights, the paper suggests a few measures for policy makers who would like to support social partners' efforts in shaping the AI transition.

Depending on their design and use in the workplace, digital technologies such as artificial intelligence, advanced robots and sensor technologies impact on many areas of working conditions, raising new ethical concerns about workers' fundamental rights. It will be critical to ensure policies on ethical issues also consider quality aspects of work and not just legal and compliance issues. COVID-19 may have boosted the use of digital technologies in the workplace, in some cases with little consideration for the ethical implications. Technologies used to limit the spread of COVID-19 in the workplace may be redeployed for other purposes, such as enhanced monitoring and surveillance of workers. It is therefore crucial to re-assess the ethical implications of the use of digital technologies both in terms of job quality and workers' rights.

Within the next decade, the world will see a major disruption of the workforce due to advances in artificial intelligence (AI) technology. According to a McKinsey Global Institute report, 375 million workers, or about 14 percent of the global workforce, may be required to shift occupations as digitization, automation, and AI technologies start to take over the workspace. In a separate 2018 report by the Organization for Economic Cooperation and Development (OECD), half of the global workforce is expected to be impacted one way or another by machine-learning technologies. AI technology will be at the forefront of the Fourth Industrial Revolution, and it will prove to be a far greater challenge than the ones that preceded it. If the world does not prepare, robots and technology could cause mass unemployment.

Intrinsically motivated agents freely explore their environment and set their own goals. Such goals are traditionally represented as specific states, but recent works introduced the use of language to facilitate abstraction. Language can, for example, represent goals as sets of general properties that surrounding objects should verify. However, language-conditioned agents are trained simultaneously to understand language and to act, which seems to contrast with how children learn: infants demonstrate goal-oriented behaviors and abstract spatial concepts very early in their development, before language mastery. Guided by these findings from developmental psychology, we introduce a high-level state representation based on natural semantic predicates that describe spatial relations between objects and that are known to be present early in infants. In a robotic manipulation environment, our DECSTR system explores this representation space by manipulating objects, and efficiently learns to achieve any reachable configuration within it. It does so by leveraging an object-centered modular architecture, a symmetry inductive bias, and a new form of automatic curriculum learning for goal selection and policy learning. As with children, language acquisition takes place in a second phase, independently from goal-oriented sensorimotor learning. This is done via a new goal generation module, conditioned on instructions describing expected transformations in object relations. We present ablations studies for each component and highlight several advantages of targeting abstract goals over specific ones. We further show that using this intermediate representation enables efficient language grounding by evaluating agents on sequences of language instructions and their logical combinations.

Autonomous reinforcement learning agents, like children, do not have access to predefined goals and reward functions. They must discover potential goals, learn their own reward functions and engage in their own learning trajectory. Children, however, benefit from exposure to language, helping to organize and mediate their thought. We propose LE2 (Language Enhanced Exploration), a learning algorithm leveraging intrinsic motivations and natural language (NL) interactions with a descriptive social partner (SP). Using NL descriptions from the SP, it can learn an NL-conditioned reward function to formulate goals for intrinsically motivated goal exploration and learn a goal-conditioned policy. By exploring, collecting descriptions from the SP and jointly learning the reward function and the policy, the agent grounds NL descriptions into real behavioral goals. From simple goals discovered early to more complex goals discovered by experimenting on simpler ones, our agent autonomously builds its own behavioral repertoire. This naturally occurring curriculum is supplemented by an active learning curriculum resulting from the agent's intrinsic motivations. Experiments are presented with a simulated robotic arm that interacts with several objects including tools.

There is little doubt artificial intelligence (AI) will play a major role in the future of work – a future that has already begun. Think, for example, of self-driving cars, algorithmic stock market trading, or even computer-aided medical diagnosis. The rapid advances in AI have the potential to create new opportunities, higher productivity and better earnings, but there are also fears they could cause job losses and a rise in inequality, with a lucky few appropriating the benefits of AI while leaving others behind. So which way will it be? The answer is, we can be moderately optimistic, provided policy-makers and social partners adopt the right measures.

There is little doubt artificial intelligence (AI) will play a major role in the future of work – a future that has already begun. Think, for example, of self-driving cars, algorithmic stock market trading, or even computer-aided medical diagnosis. The rapid advances in AI have the potential to create new opportunities, higher productivity and better earnings, but there are also fears they could cause job losses and a rise in inequality, with a lucky few appropriating the benefits of AI while leaving others behind. So which way will it be? The answer is, we can be moderately optimistic, provided policy-makers and social partners adopt the right measures.

Imagine lying on a hospital bed. One leans down to tell you that you are terribly sick and says they recommend a risky procedure as your best hope. You ask them to explain what's going on. Your trust in the doctors ebbs away. Replace the doctors with a computer program and you more or less have the state of artificial intelligence (AI) today.