The role-based EthAI-SB framework for use in small business
| Characteristics | AI as an intern | GenAI as an assistant | GenAI as a manager |
|---|---|---|---|
| Level of technology | Passive | Interactive | Proactive |
| Level of control: belief in the ability to influence and have agency (Letheren et al., 2019) | None | Limited | High autonomy but business is in control. They set the rules of engagement |
| Engagement preference: cognitive, affective and behavioural interactions (Letheren et al., 2019) | Cognitive engagement, controlled (task oriented, informational) | Behavioural engagement, delegated, (suggestions) | Emotional engagement, outsourced |
| Trust and perceived risk: the belief that an entity will act in a predictable and beneficial way, possibility for loss or harm (Letheren et al., 2019) | Low trust and high perceived risk | Moderate trust and risk | High trust and low perceived risk |
| Fairness: avoiding discriminatory or unjust outcomes. (Shin and Park, 2019) | Ensure a lack of bias in audits of AI tools | Review AI-assisted outputs for potential biases before finalisation. Ensure training data for any custom AI assistant tools is diverse and representative; provide clear instructions to the AI to avoid biased language or outputs | Conduct regular, independent audits of the AI manager’s decision-making algorithms and data inputs for bias; establish appeal mechanisms for decisions made by the AI manager; ensure diverse human oversight of the AI manager’s functions |
| Accountability: being answerable for decisions and for addressing risks (Shin and Park, 2019) | Define roles for AI oversight; document AI decision-making processes; establish protocols for error reporting and remediation; ensure human supervision | Clearly define the scope of tasks delegated to the AI assistant; maintain logs of AI usage and outputs; ensure the supervising professional reviews and takes responsibility for all work produced with AI assistance | Clearly define the AI manager’s scope of authority and decision-making limits; assign ultimate accountability to senior human leadership; implement robust logging and audit trails for all AI managerial actions and decisions |
| Transparency: Reasoning and data management is understandable to users (Shin and Park, 2019) | Train staff to interpret AI explanations; develop clear client communication protocols regarding AI use | Understand the AI assistant’s capabilities and how it generates its contributions; document the extent of AI assistance in work papers; communicate the use of AI to clients if its role in service delivery is significant | Use well-developed and proven AI systems with strong AI capabilities; ensure clear documentation and communication of the AI manager’s functions and logic; disclose the use of an AI manager to all relevant stakeholders |
| Accuracy: correctness and reliability of information or outcomes (Rana et al., 2024) | Implement data validation for inputs; conduct rigorous human review of outputs; cross-reference AI outputs with other sources; monitor AI performance | Implement rigorous human review and validation of all AI-assisted outputs; cross-reference AI-generated information with reliable sources; do not solely rely on the AI assistant for critical information | Implement stringent data validation processes for all data informing the AI manager; regularly verify the accuracy of the AI manager’s analyses and outputs; ensure human review of critical managerial decisions based on AI outputs |
| Autonomy: maintaining decision-making control, rather than being overruled by automated systems. (Rana et al., 2024) | Define tasks suitable for AI autonomy; establish human intervention points and override capabilities; regularly review the appropriateness of AI autonomy | Define clear boundaries for the AI assistant’s tasks; ensure human professionals actively guide and oversee the AI assistant’s work; maintain the ability to intervene and correct AI-assisted outputs at all stages | Strictly define and limit the AI manager’s autonomous decision-making authority; require human approval for significant managerial decisions; ensure robust human oversight and the capability to intervene or override the AI manager at any point |
| Characteristics | GenAI as an assistant | GenAI as a manager | |
|---|---|---|---|
| Level of technology | Passive | Interactive | Proactive |
| Level of control: | None | Limited | High autonomy but business is in control. They set the rules of engagement |
| Engagement preference: | Cognitive engagement, controlled (task oriented, informational) | Behavioural engagement, delegated, (suggestions) | Emotional engagement, outsourced |
| Trust and perceived risk: | Low trust and high perceived risk | Moderate trust and risk | High trust and low perceived risk |
| Fairness: | Ensure a lack of bias in audits of | Review AI-assisted outputs for potential biases before finalisation. Ensure training data for any custom | Conduct regular, independent audits of the |
| Accountability: | Define roles for | Clearly define the scope of tasks delegated to the | Clearly define the |
| Transparency: | Train staff to interpret | Understand the | Use well-developed and proven |
| Accuracy: | Implement data validation for inputs; conduct rigorous human review of outputs; cross-reference | Implement rigorous human review and validation of all AI-assisted outputs; cross-reference AI-generated information with reliable sources; do not solely rely on the | Implement stringent data validation processes for all data informing the |
| Autonomy: | Define tasks suitable for | Define clear boundaries for the | Strictly define and limit the |
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