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Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world's most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations' 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good. T he challenges facing our world today have grown in complexity and increasingly require large, coordinated efforts: between countries; and across a broad spectrum of governmental and non-governmental organisations (NGOs) and the communities they serve. These coordinated efforts work towards supporting the Sustainable Development Goals (SDGs) 1 , and there continues to be an important role for technology to support the developmental organisations and efforts active in this field to deliver the highest impact. Artificial intelligence (AI) and machine learning (ML) have attracted widespread interest in recent years due to a series of high-profile successes. AI has shown success in games and

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The integration of distributed generation (DG) in distribution grids is one of the pillars of smart grid deployment. However, an increasing amount of DG connected to distribution grids is likely to affect the operation of the grids themselves, e.g., changing the magnitude, and in some cases the direction, of power flows. In order to perform the transition to a smart grid, it is therefore essential to have the distribution system operators (DSOs) involved in the process. However, being that the DSOs' business is controlled by regulators, regulation has a fundamental impact on the speed and the actual performance of DSOs' involvement in the transition toward a smart grid. Therefore, a method is needed to assess network regulation impact on DSOs' incentive to integrate DG into their grids. This paper proposes a new method for the calculation of such incentive, and the method has been applied on a case study to the Portuguese, Danish, and Swedish regulations for different scenarios of DG penetration. The focus is on DSOs' operational costs and revenues. The analyses indicate that DG has a different impact on DSOs business, depending on the different regulations, the most relevant aspects being the structure of customer tariffs and the regulatory treatment of network losses.