Making AI Evaluation Deployment Relevant Through Context Specification
Organizations: Intellect Frontier, UK · Trustworks, USA · Civitaas Insights, USA
Abstract
With many organizations struggling to gain value from AI deployments, pressure to evaluate AI in an informed manner has intensified. Status quo AI evaluation approaches often mask the operational realities that ultimately determine deployment success, making it difficult for organizational decision makers to know whether and how AI tools will deliver durable value. We introduce and describe context specification as a process to support and inform this decision making process. Context specification turns diffuse stakeholder perspectives about what matters in a given setting into clear, named constructs: explicit definitions of the properties, behaviors, and outcomes that evaluations aim to capture, so they can be observed and measured in context. The process serves as a foundational roadmap for evaluating what AI systems are likely to do in the deployment contexts that organizations actually manage.
Figures & tables
| Priority Items | Related Constructs |
|---|---|
| Is this tool creating rework for my staff, or actually saving time? | Productivity |
| Are employees over-relying on the tool and losing critical skills? | Over-reliance |
| Is the tool shifting liability to our frontline workers? | Accountability |
| Who is consistently benefiting from these systems, and who is absorbing new risks? | Fairness and equity |
| Input Category | Input Type | Activities (Description) | Example Output |
|---|---|---|---|
| Organizational setting and constraints | Org chart; basic HR metrics; public company information | Process mapping of existing HR workflows and constraints for rail operations hiring, plus LLM‑assisted search to gather HR policies, recruitment documents, and prior decisions | Mid‑sized rail operator with 1,000 employees, hiring for Rail Operations Controller roles spread across 20 regions; high‑volume, time‑pressured recruitment for safety‑critical posts |
| System, use case, and scope | Vendor documentation for the screening model; procurement/business case; internal deployment note | Wireframing to specify what the AI screening system and chatbot do, where they sit in the hiring flow, and how system outputs are leveraged and routed across the organization | Tool Info: Third‑party HR screening tool that ranks applicants for Rail Operations Controller roles, marketed as improving speed and consistency in shortlisting; intended use limited to pre‑interview ranking and support for HR screeners |
| Evaluation purpose and scope | Pre‑engagement materials such as email threads, briefings, and internal memos summarising why the review is being commissioned | Interviews with HR staff, hiring managers, and senior leaders to clarify expectations, anticipated or already experienced system risks and benefits, and accountability structures | HR staff had made note of how the AI‑enabled hiring process might alter risks in shortlisting; senior leadership decided to commission an evaluation to understand the risk and opportunity surface of the AI‑influenced Rail Operations Controller hiring process |
| Governance and compliance expectations | Existing internal policies, procedures, and governance documents (both AI‑specific and general), plus any documented external regulatory or standards obligations | LLM‑assisted search to identify applicable policies and guidance to link them to concrete constructs of interest such as fairness, over‑reliance, and productivity | The company had identified several compliance obligations and governance standards for which understanding the deployment context and its potential impacts would be useful, motivating a closer look at how the AI‑enabled hiring process operates in practice |
| Stakeholders and roles | Org chart; documents and emails related to the system procurement and how the system’s use may impact the hiring process; informal knowledge about who is involved or affected | Mock‑up of field testing for selected HR staff to see how scores and chat responses play out under realistic conditions | A list of stakeholders and their roles is compiled, including decision‑makers, day‑to‑day users, and affected groups (e.g. HR professionals, senior leadership, and Rail Operations Controller candidates) |