Reports from the 2015 AAAI Workshop Program

Artificial Intelligence (AI) and technology have emerged as transformative tools in achieving the Sustainable Development Goals (SDGs), especially SDG 3, which strongly emphasises health and well-being. Using a mixed-methods approach that combines qualitative insights and quantitative analysis, this study investigates the potential of AI-driven interventions in enhancing healthcare, mental well-being, and economic development in line with the Sustainable Development Goals. Healthcare stakeholders and AI-driven initiatives provided primary data, and economic reports and global health indices were used for secondary data analysis. Advanced analytical tools provided insights into AI integration's feasibility and impact. Findings suggest that AI enhances healthcare quality and accessibility by improving resource allocation, personalising treatment regimens, and expediting tests. Chatbots and other AI solutions in mental health help enhance treatment outcomes and lessen stigma. Economic advantages of AI-driven projects include creating jobs, lower expenses, and increased productivity due to better public health. The analysis emphasises how crucial it is for everyone to have equal access to AI technology and how strong policy frameworks are required to handle issues like data privacy, restricted access to AI infrastructure in underprivileged areas, and ethical concerns. It concludes that AI can help achieve SDG 3 by promoting economic growth and enhancing health. It suggests funding AI infrastructure, encouraging public-private collaborations, and putting laws in place that strike a balance between ethics and innovation. In order to address difficulties like data privacy, limited AI infrastructure in poor areas, and ethical concerns, the study highlights the necessity of strong policy frameworks and fair access to AI technology. To help accomplish SDG 3, it recommends investing in AI infrastructure, promoting public-private partnerships, and passing legislation that strikes a balance between ethics and innovation.

Nature Communications, 2020

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.

European Commission publication, 2020

This document presents the contributions discussed at the second institutional workshop on Artificial Intelligence (AI), organized by the Joint Research Centre (JRC) of the European Commission. This workshop was held on 05th July 2019 at the premises of the JRC in Ispra (Italy), with video-conference to all JRC's sites. The workshop aimed to gather JRC specialists on AI and Big Data and share their experience, identify opportunities for meeting EC demands on AI, and explore synergies among the different JRC's working groups on AI. In comparison with the first event, according to the JRC Director General Vladimír Šuchav, the activities and results presented in this second workshop demonstrated a significant development of AI research and applications by JRC in different policy areas. He suggested to think about replicating the event at the premises of diverse policy DGs in order to present and discuss the clear opportunities created by JRC activities. After the opening speech by the JRC Director General Vladimír Šuchav, the research and innovation presentation were anticipated by two presentations by Alessandro Annoni and Stefano Nativi. The first presentation dealt with the results of one year of AI@JRC and six months of fully operational AI&BD community of practice . The second presentation reported the results of the AI competences survey at JRC. The research and innovation contributions consisted in flash presentations (5 minutes) covering a wide range of areas. This report is structured according to the diverse domain areas addressed by the presenters. While the first part of the workshop was mainly informative, in the second part we collectively discussed about how to move on and evolve the AI&BD community of practice.

2022

The project "amai!" ("Oh my" in Flemish) implements citizen science methods to co-develop solutions using artificial intelligence (AI) for societal challenges. The project looks for AI solutions in the field of mobility, climate, health, and work, and with the overarching theme of digital inclusion. Citizens and citizen organisations are included in the entire development process of the AI solutions, from sharing their initial ideas and co-defining solutions, to selecting proposals to receive funding to implement the solution, and involvement in the training of the AI solutions. The target audience is people not primarily interested in AI but who are interested in solving societal issues. Citizens are also invited to take part in other activities, such as using creative AI tools and playing the amai! card game. This contribution gives an overview of the approach of amai! and touches upon valuable discussions on this approach during the poster session of the

AI Magazine

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statist...