Artificial Intelligence-Driven (AI-Driven) digital technologies (DT) are intrinsically connected to interact, perceive, and understand people, businesses, economies, and lives in general. The term Artificial Intelligence (AI) can be understood as a general combination and integration of applications with other “DTs” to create machines capable of thinking like humans. AI-Driven DT economic and societal impacts increase on a continuous basis and more recently they are assuming an important role in the Sustainable Development Goals (SDG) Agenda 2030, and their implementations are a considerable decision for developed and developing countries. In turn, Brazil and Portugal have been elected in this research to display their view on AI-driven DT on SDG achievements, contradicting their perspectives in this field.
SDG | Contributions | Barriers |
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Economic Dimension | ||
SDG1. No Poverty [1,16,17][1][16][17] |
Data providing a deep learning process in a domestic income predictor, determining appropriate thresholds of poverty and its classification. | Dependence on other nations if no pathways for AI breakthroughs are identified nationwide. AI-driven automation could affect low-salary labor workforce. |
SDG2. Zero Hungry [1,18][1][18] |
Gathering socio-economic and demographic information to predict famine or demand after disasters or crop diseases and plagues. | Sharing big data to foster intelligent farming practices may be subject to the appropriation and abuse of such data. |
SDG3. Health & wellbeing [1,[3,119]][3][19] |
Using predictive machine learning for various medical prognosis and experts’ judgement in advances in biomedicine. | Ethical dilemmas about the culpability in fatal outcomes with AI usage or an excessive loss of human skills in medical or surgical procedures. |
SDG8. Work & economic growth [1 | ||
Life on land | ||
[ | ||
8 | ||
, | ||
35 | ||
] | ||
[ | ||
8 | ||
] | ||
[ | ||
35 | ||
] | ||
Early disease detection in crops to reduce herbicide use and environmental impact. Sensor-driven automatic fire detection for earlier, safer action and cost reduction. Intelligent irrigation and automate cultivation, reducing water consumption. Wildlife and ecosystems protection. Farming product sales forecasting to prevent overproduction and waste. | Complexity of deploying highly sophisticated DT capable of operating in difficult conditions, e.g., low visibility due to fires. Cost of deploying in farmlands, not affordable to small farmers. AI to reduce deforestation is a major challenge in developed countries, implying logistic problems. |
Initiatives | Description | Access | |||
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Dialoga Brasil |
A digital participation platform where citizens can make suggestions to assist in the debate and formulation of public policies including those to reach SDG targets. | http://dialoga.gov.br Accessed on 6 October 2021 |
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SDG Strategy |
An electronic website bringing together organizations representing civil society, the private sector, local governments, and academia, with the aim of broadening and enhancing the debate on SDG and mobilizing, discussing, and proposing means of implementation for the 2030 Agenda. | http://www.estrategiaods.org.br Accessed on 6 October 2021 |
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Participa.br Portal | A social media instrument providing participation tools for citizens, networks, social movements, and organizations, enabling dialogue among governmental bodies and society, through public consultations, debates, conferences, and online events. | http://www.participa.br Accessed on 6 October 2021 |
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The 2030 Agenda Platform |
A platform structured into three axes: (i) information presenting the process of developing the follow-up agenda for the SDGs and their targets, as well as providing publications and contents on the 2030 Agenda in Brazil; (ii) monitoring and review, which provides information on the monitoring indicators and will present graphs and database with SDG outcomes in the federated entities; (iii) participation, whose main target audience comprises users and institutions wishing to follow up discussions and advances regarding the SDGs. | http://www.agenda2030.com.br Accessed on 6 October 2021 |
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,20][1][20] | |||||
Map of Civil Society | Efficient transportation and flexible working through smart cities, external outsourcing, and digital labor, creating employment or anticipating job accidents in risk contexts using ambient intelligence. | Organizations |
A georeferenced platform with data on civil society organizations, allowing for the dissemination of the 2030 Agenda, as well as a follow-up of activities carried out by these organizations and their relationship with the respective SDG targets.Non-regulated AI deployment in business contexts and workers replacements by robots or algorithms in developing countries are increasing inequalities; or psychological risks from remote working are rising. | ||
http://mapaosc.ipea.gov.br | Accessed on 6 October 2021 | SDG9. Industry, Innovation & Infrastructure [21] |
Sustainable smart factories and inclusive innovation for developing regions. Supporting of SMEs and startups anywhere. Detection of anomalies and maintenance facilitation from remote computational vision and models. | Lack of scientific standards for some DT (i.e., Digital 3D and Digital Twins), lack of integrated data platforms hampers intelligent systems. Governments and companies’ reluctance to openly report pollutant emissions to build AI prediction and warning systems. | |
Municipal Vulnerability Atlas | Social Dimension | ||||
SDG 11. Cities & Communities [1,20,21,22][1][20][21][22] |
Technologies that monitor and predict new systems building, technologies to optimize essential cities’ supplies, or that preserve heritage and nature facilitating citizen lives. | Few citizen-centered initiatives, human behavior is unpredictable to be a data source, barriers from public and private institutions to achieve data interoperability. | |||
SDG 16. Peace, justice & institutions [1,23][1][23] |
Better decision-making processes based on data crime in real-time, crime prediction or crime diagnosis at little costs, and justice accessibility through higher community coverage. | Diversity compromised by globalized views, wrong usage of technologies aggravating security breaches, intentional manipulation causing bias against certain groups in crime prediction tasks. | |||
SDG 17. Partnerships for the goals [1,24,25][1][24][25] |
Citizen awareness towards a life shared, centered, and ethical vision of people, or partnerships to set global standards for sustainability for massive earth observation. | Ethical dilemmas and negative public reactions are difficult to evaluate and hinder the consolidation of digital standards and negative impacts on communities by algorithmic decisions. | |||
SDG 4. Quality Education [1,26,27][1][26][27] |
Student engagement with special needs, broader classroom participation promoting ideas that empower citizens, sharing contents that drive equality. Content for learners’ individual needs adapted in favor of inclusive education. | Insufficient training of technologies and user–computer interaction below the pace of digital transformation in education systems and society. Teachers without skills in DT. Inequality in technologies access, regarding they are not a universal right. | |||
SDG 5. Gender Equality [1,3,25][1][3][25] |
Women’s empowerment openness economic and psychologically, releasing them from men’s dependency. Raising co-operative awareness among women with common interests worldwide. | Privacy concerns and digital harassment in social media. Patriarchal family structures in some countries. Retaliation and government control against those who oppose the status quo. Job market losses for those without digital skills. | |||
SDG 10: Reduced Inequalities [1,3][1][3] |
Cyber-security technologies offering strategic view in manipulation detection of financial markets. Open opportunities for foreign trade by small firms at lower costs. Alleviating economic breach across workers in various sectors using financial recommender systems. | Difficulties for data generation mechanisms from discriminated communities to update systems. Automated work and economic environments accentuate inequalities against vulnerable individuals. Polarization across sectors enhanced through fake news and bots yield a dangerous trend. | |||
A platform comprising the Social Vulnerability Index (IVS), based on indicators of the Human Development Atlas1. Organized in three dimensions: Urban Infrastructure, Human Capital, and Income and Labor. The Social Vulnerability Index allows mapping out exclusion and social vulnerability in 5565 municipalities and in Human Development Units of the main metropolitan regions of the country. This tool assists municipalities to assess and plan actions focused on local. | Environmental Dimension | ||||
http://ivs.ipea.gov.br | Accessed on 6 October 2021 | SDG 6. Clean water and sanitation [1,28][1][28] |
Predicting weather and drought by planned, complex water system simplification, facilitating human intervention with real-time data to reduce contamination and assure quality. Water resource distribution fairness. | Shortage of high-quality data and complete information, high temporal variability in water-related processes, focus on short-term predictive models has disregarded advances in long-term reliable water predictions, and lack of staff jointly specialized in DT and water resources. | |
SDG 7. Affordable and clean Energy [6,17,29][6][17][29] |
Safer management of renewable energy plants reducing energy consumption. Remote decentralized management of massive energy infrastructures in real-time. Energy efficiency and its timely supply at an optimal cost. | Cyber-attacks, long-term obsolescence, and no standardization of digital energy systems are vulnerabilities and cause difficulties of implementation. Digitization consumption tends to cause blackouts in developing countries, representing an expressive part of global energy consumption. | |||
SDG 12. Responsible consumption and Production [7,30][7][30] |
Accountability and transparency in consumption policies to predict and simulate production processes to reduce energy consumption and raw material overuse. Early detection of breakdown to prevent waste, more synergy of production and consumption, aiming reductions in industrial waste and pollutant emissions. Production planning adapted to predicted consumption patterns to avoid unnecessary waste. | High simplification of production chains for their optimization, resource availability dependence on weather factors affecting predictions, and hard production process adaptation due to high modification costs. Sustainability and cost reduction are often two opposite goals in industrial production. Deemed unacceptable costs of integrating AI and DT might be denied by firms and consumers. | |||
SDG 13. Climate Action [1,31,32][1][31][32] |
Remotely assist countries to make better emergency or disaster recovery decisions. Education of younger generations about climate change action. Early prediction of natural catastrophes, enabling loss reduction and better understanding of desertification trends. | Not affordable data or information in certain regions, certain political resistance, and economic cost for large-scale systems to optimize pollutant emissions in urban areas combined with inherent computational cost requires significant energy. | |||
SDG 14. Life below water [1,33,34][1][33][34] |
Predict water quality parameters, early oil dumping detection and ocean acidification estimation. Exploiting data from monitoring sources to obtain knowledge for predictive decision-making about sustainable exploitation of ocean resources. | Digitization incurs high economic costs. Massive volumes of data to make accurate estimates are difficult to obtain due to the complexity of the marine physical environment. Malicious uses of digital technologies, and cyber-attacks may lead to uncontrolled overexploitation. | |||
SDG 15. |