AI ETHICS The second meaning concerns the environment in which users use the AI. This is perhaps more relevant to defenders reading this white paper and using or preparing to use AI. A closed environment (sometimes called an “enterprise” or “private” deployment) keeps user inputs (mostly) confidential and segregated, either on-premises (i.e., on a localized server on the firm’s premises) or in a cloud-based instance (i.e., the platform stores the data in the cloud). An open environment, by contrast, may incorporate user interactions into future training data or make them accessible to the provider, law enforcement, or other unintended third parties. This distinction matters particularly for attorneys handling confidential client information. Bar ethics opinions have emphasized that lawyers must understand how their AI tools operate with respect to preserving confidentiality under Model Rule 1.6. 29
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What are the risks of open and closed systems?
Open AI systems use publicly available models, transparent training datasets, and are often freely accessible to consumers. 30 When users enter information into open systems, that data may become part of the broader training dataset or otherwise leave the user’s control. Open systems offer certain advantages — including broader training data and greater resources for innovation — but this openness and transparency comes at a cost for confidentiality. Searches performed and content generated through open systems may effectively enter the public domain. Moreover, stored data is likely used to train the system and otherwise shared with third parties. Closed AI systems, by contrast, operate within self-contained and controlled environments. These enterprise-level deployments house data on private servers or secure cloud instances, limiting access to authorized users within an organization. Closed systems restrict training data to information the organization provides or approves, and they better maintain confidentiality (albeit not entirely) by preventing data from leaving the secure environment. However, closed systems typically require significant financial investment and technical infrastructure that many solo practitioners and under- resourced public defender offices cannot easily afford. Finally, defenders may learn about “on-prem” (i.e., on premises) AI environments. These are often a subset of closed AI environments in which the data actually stays within the firm’s or office’s own infrastructure and is managed by its own internal information technology and security teams. On-prem AI systems can be beneficial for defenders (and lawyers, generally) because they are more customizable and directly managed by those using the system every day. However, on-prem AI requires upfront spending on the actual hardware and physical infrastructure (e.g., servers, space, and other physical infrastructure, like cooling) that many solo practitioners or under-resourced firms may not be able to afford. 31
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Parity in Practice: The Defender’s Duty to Ethically Use AI
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