Summary
Finance has been a gated system in modern banking. If you have a credit score, tax returns, land records, or collateral, the doors open. If you do not, you are invisible. For decades, billions of workers, farmers, gig earners, and micro-entrepreneurs have been excluded from formal finance not due to…
Source: Hindustan Times
AI News Q&A (Free Content)
Q1: What is ethical finance, and how does it differ from traditional banking?
A1: Ethical finance is a system of financial management or investment that seeks outcomes beyond mere financial returns. It integrates social, environmental, and governance criteria into its operations. Unlike traditional banking, which prioritizes profit maximization, ethical finance aims to deliver positive social and environmental impacts. Ethical banks are involved in activities such as financing renewable energy projects and avoiding investments in industries like weapons manufacturing or fossil fuels. They operate under principles that support transparency and social values, often with narrower profit margins than conventional banks.
Q2: How are AI technologies being used to promote social inclusion?
A2: AI technologies are facilitating social inclusion by providing flexible work arrangements and remote job opportunities, especially for individuals with disabilities. AI-driven platforms enhance digital inclusion by granting access to communication and information, which improves healthcare, social connections, and employment opportunities. Robotic AI companions are also aiding older adults in self-monitoring their health. These applications demonstrate AI's potential to address specific needs and shape social inclusion positively.
Q3: What role does ethical finance play in addressing climate change?
A3: Ethical finance plays a crucial role in combating climate change by directing investments towards environmentally friendly projects, such as renewable energy initiatives like solar and wind farms. By avoiding investments in fossil fuels and supporting sustainable projects, ethical finance contributes to reducing carbon emissions and advancing climate goals. This approach reflects a growing demand for ethical products and services that align with social and environmental concerns.
Q4: What challenges are associated with collecting data for AI systems designed for inclusion?
A4: Collecting data for AI systems aimed at inclusion faces challenges related to ethical and legal considerations, especially when involving vulnerable groups. Standard recording equipment may not be secure for capturing sensitive data. Researchers often lack experience in handling these issues, making it difficult to obtain and share valuable data ethically. Systems like 'CUSCO' have been developed to securely collect and exchange such data, providing a framework for ethical data collection.
Q5: How is the concept of inclusion being integrated into AI development frameworks?
A5: Inclusion is being integrated into AI development frameworks by designing systems that avoid amplifying digital injustices and ensure socially beneficial use cases. For instance, AI systems are being programmed to support multiple languages and provide audio interfaces for visually impaired users. Projects like diagnosing diabetic retinopathy demonstrate the use of AI for social good, with an emphasis on inclusion by design throughout the AI lifecycle.
Q6: What are some examples of ethical finance initiatives globally?
A6: Globally, ethical finance initiatives include investments in renewable energy projects, such as solar and wind farms, and support for companies that prioritize social and environmental sustainability. Ethical finance institutions avoid funding industries that harm society and the environment, such as the weapons and fossil fuel sectors. These initiatives are driven by a growing demand for ethical banking practices and the desire to positively impact society and the environment.
Q7: What are the implications of AI on the social inclusion of individuals with severe mental illness?
A7: AI has a significant impact on the social inclusion of individuals with severe mental illness by enhancing digital inclusion. AI algorithms analyze large datasets to create predictive models that identify key determinants of social inclusion outcomes. These models guide targeted interventions and empower individuals to enhance their social inclusion. AI's ability to facilitate access to technology and information plays a critical role in improving healthcare, education, and employment opportunities for this population.
