Questions and Answers
What is a primary consideration in the design, development, and deployment of AI systems?
AI systems should prioritize operational effectiveness over stakeholder obligations.
False
What is data lineage in the context of AI systems?
Understanding where data comes from to its end-use
To ensure fairness in AI decision-making, organizations should minimize _______________________ through data and model design.
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What is the purpose of defining clear roles and responsibilities in AI governance?
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AI systems are designed to prioritize human-centric values.
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Match the following AI governance components with their descriptions:
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What is the primary goal of AI governance in decision-making?
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What is the primary focus of the Model AI Governance Framework in Singapore?
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The Model AI Governance Framework is only applicable to organizations in the technology industry.
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What is the primary objective of the Model AI Governance Framework?
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The Model AI Governance Framework was first released in _______________ and had a second edition in _______________.
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What is an important aspect of human-centricity in AI implementations?
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The Model AI Governance Framework provides a risk-impact matrix for assessing the risks associated with AI implementations.
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Match the following principles of the Model AI Governance Framework with their descriptions:
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What is the name of the privacy principles referenced in the Model AI Governance Framework?
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What is one of the key aspects of fairness in AI systems according to the EC recommendations?
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AI systems should always prioritize efficiency over explainability.
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What are the three key aspects of technical robustness and safety in AI systems?
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According to the EC framework, AI systems should ensure respect for _______________ and quality and integrity of data.
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What is the primary goal of human agency and oversight in AI systems?
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AI systems should always prioritize short-term gains over long-term sustainability.
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Match the following EC framework requirements with their descriptions:
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What is the primary goal of accountability in AI systems?
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Study Notes
Model AI Governance Framework
- Recognized the importance of AI technologies, Singapore released the first edition of the Model AI Governance Framework in 2019, with a second edition in 2020.
- Provides practical guidance to organizations to address key ethical and governance issues surrounding AI implementations.
Features of the Model
- Algorithm-agnostic: Does not focus on specific AI methodology.
- Technology-agnostic: Does not focus on specific systems, software or technologies.
- Sector-agnostic: Includes considerations that are common across different industries.
- Scale and Business-model-agnostic: Does not focus on organizations of a particular scale or the form of business (B2C, B2B).
Objectives of the Model
- Build stakeholder confidence in AI through responsible use of AI to manage different risks in AI deployment.
- Assist organizations in demonstrating reasonable efforts to align internal policies, structures, and processes with relevant practices in data management and protection.
Guiding Principles
- Organizations using AI in decision-making should ensure that the decision-making process is explainable, transparent, and fair.
- AI solutions should be human-centric, protecting the interests of human beings, including their well-being and safety.
Internal Governance Structures and Measures
- Clear roles and responsibilities need to be defined to monitor and manage the implementation, use, and maintenance of AI systems.
- Relevant roles be defined with specific responsibilities towards the use of AI.
- Risks posed by AI systems can be managed within the organization's risk management framework/system.
Operations Management
- Ensure unfair bias is minimized through data and model design.
- Good governance ensures AI implementations balance the competing needs of operational effectiveness and efficiency and the organization's obligations to various stakeholders.
- Data quality and selection are of utmost importance.
- Data lineage and provenance record are essential.
Stakeholder Interaction and Communication
- Ensure appropriate policies are crafted and made known to users and stakeholders.
EC Recommendations on AI Governance
- Fairness: AI systems should ensure equal and just distribution of both benefits and costs.
- Explicability: AI systems should be transparent, and the capabilities and purpose of such systems openly communicated.
Requirements of EC Framework
- Human agency and oversight: Fundamental rights, human agency, and human oversight.
- Technical robustness and safety: Resiliency to attack, including security, fall-back plans, and general safety.
- Privacy and data governance: Respect for privacy, quality, and integrity of data and access to data.
- Transparency: Traceability, explainability, and communication.
- Diversity, non-discrimination, and fairness: Avoidance of unfair bias, accessibility, and universal design, and stakeholder participation.
- Societal and environmental well-being: Sustainability and environmental friendliness, social impact, society, and democracy.
- Accountability: Auditability, minimization, and reporting of negative impact, trade-offs, and redress.
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Description
Learn about AI model governance framework, human-centricity in AI implementations, and risk-impact matrix of human involvement. This quiz covers the importance of AI technologies and model AI governance framework in Singapore.
