Empowering local communities using artificial intelligence

Hsu, Yen-Chia; Huang, Ting-Hao; Verma, Himanshu; Mauri, Andrea; Nourbakhsh, Illah; Bozzon, Alessandro

doi:10.1016/j.patter.2022.100449

Cited by 24 publications

(7 citation statements)

References 49 publications

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“…Participatory methods involving the underserved communities at all the phases of the AI lifecycle are encouraged. It is important to integrate different points of view to avoid bias and to generate audits for citizen participation, as well as to focus on the social impact of AI and in relation to global goals (32).…”

Section: Ai Biasesmentioning

confidence: 99%

Raising awareness of sex and gender bias in artificial intelligence and health

Buslón,

Cortés,

Catuara-Solarz

et al. 2023

Front. Glob. Womens Health

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Historically, biomedical research has been led by and focused on men. The recent introduction of Artificial Intelligence (AI) in this area has further proven this practice to be discriminatory for other sexes and genders, more noticeably for women. To move towards a fair AI development, it is essential to include sex and gender diversity both in research practices and in the workplace. In this context, the Bioinfo4women (B4W) program of the Barcelona Supercomputing Center (i) promotes the participation of women scientists by improving their visibility, (ii) fosters international collaborations between institutions and programs and (iii) advances research on sex and gender bias in AI and health. In this article, we discuss methodology and results of a series of conferences, titled â€œSex and Gender Bias in Artificial Intelligence and Health, organized by B4W and La Caixa Foundation from March to June 2021 in Barcelona, Spain. The series consisted of nine hybrid events, composed of keynote sessions and seminars open to the general audience, and two working groups with invited experts from different professional backgrounds (academic fields such as biology, engineering, and sociology, as well as NGOs, journalists, lawyers, policymakers, industry). Based on this awareness-raising action, we distilled key recommendations to facilitate the inclusion of sex and gender perspective into public policies, educational programs, industry, and biomedical research, among other sectors, and help overcome sex and gender biases in AI and health.

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Section: Ai Biasesmentioning

confidence: 99%

Raising awareness of sex and gender bias in artificial intelligence and health

Buslón,

Cortés,

Catuara-Solarz

et al. 2023

Front. Glob. Womens Health

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“…AI has been employed to augment and enhance human understanding of the environment, including perceptions of citizen scientists [41]. While CS and AI are often viewed as separate tools for ecological monitoring, recent studies indicate that a symbiotic relationship between human intelligence and AI, termed hybrid intelligence (HI) [42,43], can strategically unite the two, enhancing outcomes for conservation activities.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Intelligence for Marine Biodiversity: Integrating Citizen Science with AI for Enhanced Intertidal Conservation Efforts at Cape Santiago, Taiwan

Chen,

Lu,

Han

2024

Sustainability

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Marine biodiversity underpins the formation of marine protected areas (MPAs), necessitating detailed surveys to account for the dynamic temporal and spatial distribution of species influenced by tidal patterns and microhabitats. The reef rock intertidal zones adjacent to urban centers, such as Taiwan’s Cape Santiago, exhibit significant biodiversity, yet they are increasingly threatened by tourism-related activities. This study introduces an artificial intelligence (AI)-empowered citizen science (CS) approach within the local community to address these challenges. By integrating CS with AI, we establish a hybrid intelligence (HI) system that conducts in situ biological surveys and educational programs focused on reef ecological conservation. This initiative not only facilitates the collective gathering and AI-assisted analysis of critical data but also uses machine-learning outputs to gauge data quality, thus informing subsequent data collection and refinement strategies. The resulting collectivity and iterative enhancement foster a mutual and continuous HI learning environment. Our HI model proves instrumental in fostering community engagement and public involvement in CS endeavors, cultivating the skills necessary for documenting rocky intertidal biodiversity shifts. These efforts are pivotal for informing the design and governance of future MPAs, ensuring their efficacy and sustainability in marine conservation.

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“…2021; Hsu et al. 2022). The connection between prosociality, AI, and urban systems is also evident in the case of route recommender systems, where following AI recommendations might lead to detrimental outcomes such as higher pollution levels (Cornacchia et al.…”

Section: Introductionmentioning

confidence: 99%

Prosocial dynamics in multiagent systems

Santos

2024

AI Magazine

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Meeting today's major scientific and societal challenges requires understanding dynamics of prosociality in complex adaptive systems. Artificial intelligence (AI) is intimately connected with these challenges, both as an application domain and as a source of new computational techniques: On the one hand, AI suggests new algorithmic recommendations and interaction paradigms, offering novel possibilities to engineer cooperation and alleviate conflict in multiagent (hybrid) systems; on the other hand, new learning algorithms provide improved techniques to simulate sophisticated agents and increasingly realistic environments. In various settings, prosocial actions are socially desirable yet individually costly, thereby introducing a social dilemma of cooperation. How can AI enable cooperation in such domains? How to understand long‐term dynamics in adaptive populations subject to such cooperation dilemmas? How to design cooperation incentives in multiagent learning systems? These are questions that I have been exploring and that I discussed during the New Faculty Highlights program at AAAI 2023. This paper summarizes and extends that talk.

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Empowering local communities using artificial intelligence

Cited by 24 publications

References 49 publications

Raising awareness of sex and gender bias in artificial intelligence and health

Raising awareness of sex and gender bias in artificial intelligence and health

Hybrid Intelligence for Marine Biodiversity: Integrating Citizen Science with AI for Enhanced Intertidal Conservation Efforts at Cape Santiago, Taiwan

Prosocial dynamics in multiagent systems

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