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Using Societal Context Knowledge to Foster Responsible AI Application.

Historical Background:
The application of AI-related products and technologies has always been intricately tied to the societal context in which they operate. This context encompasses a dynamic interplay of social, cultural, historical, political, and economic factors, posing unique challenges for responsible AI development. Traditionally, standard machine learning approaches have struggled to effectively represent and address the qualitative and complex nature of societal contexts, leading to issues like biases and fragile ML solutions.

Past Vs Present Situation:
In the past, AI product developers faced difficulties in understanding and incorporating societal context into their solutions, often resulting in incomplete problem understanding and biased outcomes. However, present efforts, such as Societal Context Understanding Tools and Solutions (SCOUTS) by the Responsible AI and Human-Centered Technology (RAI-HCT) team within Google Research, are focused on bridging the gap between qualitative societal understanding and quantitative ML approaches. SCOUTS aims to empower developers with structured societal context knowledge throughout the AI product development lifecycle to ensure responsible and robust solutions.

Advantages of Societal Context Understanding:
1. Enhanced Problem Understanding: By integrating community-validated and structured societal context knowledge, AI developers gain a comprehensive understanding of complex societal problems, reducing the risk of biased or incomplete solutions.
2. Responsible AI Development: Incorporating societal context throughout the product lifecycle promotes responsible AI application, ensuring that AI systems consider the diverse needs and impacts on different communities.
3. Bias Mitigation: Leveraging structured societal context knowledge aids in detecting and mitigating biases in AI models, contributing to fairer and more equitable AI outcomes.
4. Scalable Solutions: Tools like SCOUTS enable scalable and trustworthy access to societal context knowledge, making it easier for AI developers to address complex societal challenges effectively.
5. Social Impact: Responsible AI application driven by societal context understanding has the potential to address some of the world's most pressing issues, positively impacting communities and fostering open societies.

Disadvantages and Challenges:
1. Complexity of Societal Context: Incorporating societal context into AI product development requires innovative approaches to navigate the complex, non-linear, and subjective nature of societal factors.
2. Data Privacy and Ethics: Handling data related to societal context raises concerns about privacy and ethical considerations, necessitating robust data protection measures.
3. Resource Intensive: Extracting, structuring, and validating societal context knowledge may demand significant time and resources, particularly for large-scale AI projects.
4. Ongoing Research Efforts: The development and integration of tools like SCOUTS require continuous research to improve accuracy, scalability, and usability.

Major Future Development Predictions on AI and Societal Context:
1. Advancements in Contextual AI: AI systems will become more adept at understanding and incorporating societal context, leading to more context-aware and responsible applications.
2. Cross-Domain Integration: AI-driven tools like SCOUTS will expand to integrate context knowledge from diverse domains, facilitating more comprehensive problem understanding.
3. AI for Social Good: AI development will increasingly focus on addressing complex societal problems, driven by responsible AI practices and societal context awareness.
4. Interdisciplinary Collaboration: Future AI development will witness collaborations between AI researchers, social scientists, and experts from various domains to create holistic solutions.

Final Conclusion:
Understanding and integrating societal context knowledge into AI development is crucial for fostering responsible AI applications. By bridging the problem understanding chasm, AI developers can create more equitable, unbiased, and impactful solutions. Emphasizing responsible AI practices and continuing research in the domain will pave the way for AI to address complex societal challenges, positively shaping the future of technology and society.