What is The 7 Cs of Artificial Intelligence?

The framework outlines key elements crucial for developing, managing, and deploying AI responsibly. Anthony Chang is described as an accomplished pediatric cardiologist who recognized the potential of computing to revolutionize healthcare. He pursued a master’s degree in data science and artificial intelligence and has become a leading voice on AI in healthcare. In a keynote speech at the AI in Healthcare Summit, he presented seven important words, all starting with the letter C, that the healthcare industry should keep in mind for AI to be effective.

The term “7C” in the context of AI framework refers to a set of principles or components that guide the development and implementation of AI systems. While there isn’t a universally accepted definition of “7C” specifically for AI, it can often encompass the following elements.

The 7C for AI Data Management

• Compliance: Adhering to standards for data integrity is crucial for data used in AI. Compliance through industry standards and in-house rules helps avoid biases in AI algorithms and ensures transparency in data processing and AI outputs, fostering trust and mitigating risks.
• Confidence: Essential for reliable and accurate AI outcomes, confidence in data requires ensuring the quality, completeness, and relevance of input data through rigorous validation processes like cleaning, normalization, and verification. Data governance frameworks and monitoring data sources contribute to confidence, allowing for informed decisions and improved predictive accuracy. 
• Consolidation: Bringing together data from varied sources (structured and unstructured) into a unified dataset enhances the comprehensiveness and representativeness of AI models, enabling more accurate predictions and insights. It facilitates data exploration and discovery of hidden patterns. 
• Consistency: Crucial for reliable and valid outcomes, consistent data ensures AI models are trained on dependable and accurate information, reducing the risk of biased or misleading results. Achieving consistency involves data standardization, normalization, and regular quality checks, improving performance and trustworthiness. 
• Clarity: Clarity of queries used in AI is crucial for generating accurate and relevant outputs. Providing clear and unambiguous questions or commands helps AI models understand intent and context accurately, improving the precision and validity of outputs and mitigating misunderstandings. 
• Context: Provides necessary background information and situational awareness for accurate interpretation and comprehension of AI-generated outputs. AI models rely heavily on context to deliver relevant and meaningful responses aligned with user intent. Excluding or getting context wrong can severely deviate the AI process. 
• Causation: Poses a significant challenge for users interpreting AI responses. While AI excels at pattern recognition and correlation, grasping cause-and-effect relationships is complex for AI tools. Interpreting AI outputs without understanding causation can lead to incorrect assumptions. Human intervention and combining AI with expertise are needed for accurate interpretation.

The 7C for AI Analysis, Design, and Refinement of AI solutions

• Capability: Refers to what the AI system can do with its functional abilities, strengths, and skills. It’s the starting point, defining the set of tasks an AI model is designed to handle (e.g., NLP, image recognition, predictive analytics). Defining capability sets realistic expectations and ensures the model addresses the intended problem. 
• Capacity: Refers to the volume of data an AI model can process and the computational power it requires. Essential because machine learning models need large amounts of data to train effectively. Capacity depends on computing power, memory, and storage resources. Assessing capacity helps decide infrastructure requirements. 
• Collaboration: Refers to how well an AI system can work with humans or other systems. Increasingly vital as AI is rarely used in isolation. Collaborative AI can share insights, receive feedback, and improve based on human input, enabling a more effective and efficient workflow by complementing human expertise. 
• Creativity: Involves the system’s ability to generate new ideas, solutions, or insights beyond basic, rule-based processing. Creative AI can innovate and think outside the box, generating unique outputs used in areas like marketing or healthcare. It provides novel solutions and adds a human-like spark to problem-solving. 
• Cognition: Refers to an AI’s ability to understand, reason, and apply logical thinking, mimicking human thought processes. Cognitive AI analyzes information, identifies patterns, and makes sense of data in a human-like way, forming the core of technologies like NLP, speech recognition, and image recognition. It makes AI more powerful and useful where understanding context and reasoning is required. 
• Continuity: About the system’s ability to operate, learn, and improve continuously over time through continuous learning. Crucial in dynamic environments where conditions or user behaviors change. Continuous learning enables AI to stay effective, relevant, and accurate as new data or situations arise, maximizing long-term value. 
• Control: About the ability to monitor, manage, and regulate the AI system to ensure safe and ethical operation. Crucial to prevent unintended behaviors and maintain accountability. Effective control includes setting limits, enforcing ethical standards, and providing human oversight. It safeguards functionality, ensuring operation within ethical and legal boundaries and alignment with intended goals. 

Resources:

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