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People & Planet
Economic Context
Data & Input
AI Model
Task & Output
Area 1 / 5
Who are users of your AI system?
How much does your AI system impact different parts of the economy?
How easy is it for users to opt out or challenge decisions made by the system?
How much does the system's output impact fundamental human rights and democratic principles?
What impact does the system have on personal well-being, social norms, and environmental sustainability?
What is a displacement potential of the system? What number of tasks that are or were executed by humans can be automated?
How many industrial sectors is the system deployed in?
How many different business areas does your AI system currently operate in? (e.g., marketing, customer service, operations, etc.)
How often the system is used for both for-profit and non-for-profit purposes?
What is the impact of a system failure or disruption on critical services? (Consider scenarios where failures could result in minor disruptions or major service outages).
What's the scale of system deployment across the business?
How would you identify Technology Readiness Level (TRL) of the system?
Which method of data collection prevails: automated or human?
Who provides more data, experts or amateurs?
How dynamic and how effectively does your system update or react to new data inputs? (e.g., does your AI system adapt instantly to changes in user data, or does it require manual updates?)
What are the data's proprietary rights?
How much the data in your AI solutions is anonymized? When collecting user data for analysis, do you remove or alter personal identifiers so data can't be linked to individual users?
How would you rate the clarity, accuracy and diversity of your data? e.g., it's easy to understand, accurately represents the intended population or phenomena, etc.
How would you describe structure of the data and input?
How standardised is your data?
How widely is the system used throughout the organisation?
What level of information about the underlying model of the AI system is publicly available or published? Is there detailed documentation on how the model handles data?
What type of AI model is it, and how much does it rely on data compared to human-generated rules with a low reliance?
What is the management model of the system?
Is the model generative or discriminative? Does your AI model primarily generate new data (texts, images, etc.) or distinguish between data categories?
How many models is the system comprised of?
What is the ability of the system to learn from human-written rules, data, supervised learning, and other sources?
How does the model improve and adapt to real data? For example, does it refine its decisions or accuracy by learning from new data it encounters during operation?
What is the ability of the system to be trained centrally, in a number of local servers, edge devices and/or other training sources?
How flexible is your AI model with different types of data? For example, can it be easily adjusted for use in different industries or situations, or is it optimized for specific datasets you own?
How can the model be used deterministically and probabilistically? Deterministic use follows strict rules for specific outcomes, while probabilistic use involves predictions based on probabilities.
How clear and transparent is the information, enabling users to understand model outputs?
What number and what types of tasks does the system perform?
What is the system's ability to combine tasks and actions into composite systems?
What is the level of system independence and autonomy?
To how many core application areas does the system belong?
How many standards do you use to measure the performance of your AI system? For example, do you follow well-known benchmarks or guidelines to evaluate performance?
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