AI for social good: unlocking the opportunity for positive impact

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

Uganda Impact Report: The World Citizens Panel

The World Citizens Panel (WCP) is an impact measurement methodology developed by Oxfam Novib. It is designed to measure and understand the changes in people's lives resulting from Oxfam's projects. The WCP combines quantitative research (impact surveys) with qualitative research (Stories of Change) to give participants in Oxfam Novib's programmes a voice, to learn how our programmes can be improved, and to contribute to the public debate on the effectiveness of development cooperation.This impact study of the programme in Uganda was carried out in 2014. About 170 interviewers carried out a total of 4,953 interviews; 17 partners carried out the surveys in their own areas of intervention. The study included a broad set of indicators, covering major dimensions of poverty and injustice. Data collected by partners with the help of a smartphone app was transferred into a central database, managed and analysed by the Oxfam Novib World Citizens Panel team. Based on the outcomes of the impact surveys, it was decided to conduct further qualitative research with Stories of Change on gender-based violence and land rights for women.This report presents the major findings from the analysis of the survey results and Stories of Change

AI for the Social Good (Dagstuhl Seminar 19082)

Artificial intelligence (AI) and machine learning (ML) have made impressive progress in the last few years. Long-standing challenges like Go have fallen and the technology has entered daily use via the vision, speech or translation capabilities in billions of smartphones. The pace of research progress shows no signs of slowing down, and demand for talent is unprecedented. AI for Social Good in general is trying to ensure that the social good does not become an afterthought, but that society benefits as a whole. In this Dagstuhl seminar, we brought together AI and machine learning researchers with non-governmental organisations (NGOs), as they already pursue a social good goal, have rich domain knowledge, and vast networks with (non)-governmental actors in developing countries. Such collaborations benefit both sides: on the one hand, the new techniques can help with prediction, data analysis, modelling, or decision making. On the other hand, the NGOs\u27 domains contain many non-standard conditions, like missing data, side-effects, or multiple competing objectives, all of which are fascinating research challenges in themselves. And of course, publication impact is substantially enhanced when a method has real-world impact. In this workshop, researchers and practitioners from diverse areas of machine learning joined stakeholders from a range of NGOs to spend a week together. We first pursued an improved understanding of each side\u27s challenges and established a common language, via presentations and discussion groups. We identified ten key challenges for AI for Social Good initiatives. To make matters concrete, we organised a hackathon around some existing technical questions within the NGOs to scope the applicability of AI methods and seed collaborations. Finally, we defined guidelines and next steps for future AI for Social Good initiatives

Mobilizing farmers to stop land degradation: A different discourse from Burundi

Stopping land degradation is one of the biggest challenges worldwide and particularly in Burundi, with its unprecedented rates of soil loss and growing food insecurity. This article proposes a different discourse on how to engage people in stopping land degradation, and presents results and lessons learned from a bottom‐up inclusive approach implemented since 2014 in Burundi: the integrated farm planning (PIP) approach. The PIP approach aims to build a solid foundation for sustainable change toward enhanced food production and good land stewardship, based on three foundation principles (motivation, stewardship, and resilience) and three guiding principles (empowerment, integration, and collaboration). This article is based on two studies undertaken in 2018: an impact study among 202 households and a qualitative study using the most significant change methodology with 30 households. Findings from both studies provide initial support that the PIP approach generates considerable changes at household, farm, and village level. Based on a vision and a plan for their farm, motivated PIP households are currently investing in the resilience of their farms and applying a diversity of conservation practices, while in all PIP villages concrete collective action is undertaken for sustainable land stewardship. Given its rapid upscaling in Burundi and the potential of the PIP approach to mobilize farmers for motivated action, the article concludes with a reflection on the core elements of a different discourse to stop land degradation.<br/

Effect of heat processing on the nutrient composition, colour, and volatile odour compounds of the long-horned grasshopper Ruspolia differens serville

Heat processing is commonly used to prepare edible insects for consumption. This study aimed at determining the effect of boiling and subsequent oven roasting on Ruspolia differens’ nutrient composition, colour and odor compounds. Boiling leads to: a significant increase in protein and decrease in fat content on a dry matter basis; a minimal influence on its amino and fatty acids profile; a significant reduction in its ash content due to leaching of phosphorus, potassium and sodium; a significant increase in iron, zinc, copper, manganese and calcium content; and a fivefold reduction in the amount of vitamin B12. Roasting leads to a relative increase in the amount of calcium and trace mineral elements but doesn’t affect other nutrients. Roasting results into a more uniform colour intensity when green and brown polymorphs are roasted together. Lipid oxidation is responsible for the colour and aroma of heat processed R. differens.status: Published onlin

Frequency of family meals and food consumption in families at high risk of type 2 diabetes : the Feel4Diabetes-study

A family meal is defined as a meal consumed together by the members of a family or by having> 1 parent present during a meal. The frequency of family meals has been associated with healthier food intake patterns in both children and parents. This study aimed to investigate in families at high risk for developing type 2 diabetes across Europe the association (i) between family meals' frequency and food consumption and diet quality among parents and (ii) between family meals' frequency and children's food consumption. Moreover, the study aimed to elucidate the mediating effect of parental diet quality on the association between family meals' frequency and children's food consumption. Food consumption frequency and anthropometric were collected cross-sectionally from a representative sample of 1964 families from the European Feel4Diabetes-study. Regression and mediation analyses were applied by gender of children. Positive and significant associations were found between the frequency of family meals and parental food consumption (beta = 0.84; 95% CI 0.57, 1.45) and diet quality (beta = 0.30; 95% CI 0.19, 0.42). For children, more frequent family meals were significantly associated with healthier food consumption (boys, beta = 0.172, p < 0.05; girls, beta = 0.114, p< 0.01). A partial mediation effect of the parental diet quality was shown on the association between the frequency of family meals and the consumption of some selected food items (i.e., milk products and salty snacks) among boys and girls. The strongest mediation effect of parental diet quality was found on the association between the frequency of family breakfast and the consumption of salty snacks and milk and milk products (62.5% and 37.5%, respectively) among girls. Conclusions: The frequency of family meals is positively associated with improved food consumption patterns (i.e., higher intake of fruits and vegetables and reduced consumption of sweets) in both parents and children. However, the association in children is partially mediated by parents' diet quality. The promotion of consuming meals together in the family could be a potentially effective strategy for interventions aiming to establish and maintain healthy food consumption patterns among children