Aram Algorithm
@aram_algorithm
Overland Park, Kansashttps://aramalgorithm.ai/ Business Intelligence PlatformsOverview
About Aram Algorithm
Aram Algorithm is a boutique AI accountability engineering firm focused on high-risk HR and employment AI systems (EU AI Act Annex III-4).
We work with HR-tech providers, deploying organizations, and auditors when a concrete question must be answered:
Can this specific HR AI–assisted decision be reconstructed and justified from contemporaneous evidence alone?
We do not sell governance frameworks, compliance checklists, or assurance claims.
We design and execute scenario-based accountability reconstruction exercises that determine whether decisions are replayable, attributable, and reviewable under scrutiny — or not.
Our work surfaces failure modes that traditional QA, model evaluation, and documentation reviews routinely miss, including:
• Inability to replay a past hiring, promotion, or termination decision
• Missing or non-deterministic decision lineage (inputs, versions, thresholds)
• Absent or non-traceable human oversight and override records
• Provider vs deployer responsibility boundaries that cannot be evidenced
• Scenario-specific failures across the HR lifecycle
(job ads → screening → ranking → promotion → termination → worker management)
We support traditional ML, GenAI, and agentic HR systems, without assuming model class, architecture, or intent.
Aram Algorithm operates at the moment of scrutiny — when evidence either exists or it does not.
We produce deterministic, replayable accountability records.
Interpretation, remediation, and compliance claims are out of scope.
Evidence first.
Scrutiny second.
No narrative repair.
Engagements include scenario demos, fixed-scope pilots, and production evidence continuity.
We work with HR-tech providers, deploying organizations, and auditors when a concrete question must be answered:
Can this specific HR AI–assisted decision be reconstructed and justified from contemporaneous evidence alone?
We do not sell governance frameworks, compliance checklists, or assurance claims.
We design and execute scenario-based accountability reconstruction exercises that determine whether decisions are replayable, attributable, and reviewable under scrutiny — or not.
Our work surfaces failure modes that traditional QA, model evaluation, and documentation reviews routinely miss, including:
• Inability to replay a past hiring, promotion, or termination decision
• Missing or non-deterministic decision lineage (inputs, versions, thresholds)
• Absent or non-traceable human oversight and override records
• Provider vs deployer responsibility boundaries that cannot be evidenced
• Scenario-specific failures across the HR lifecycle
(job ads → screening → ranking → promotion → termination → worker management)
We support traditional ML, GenAI, and agentic HR systems, without assuming model class, architecture, or intent.
Aram Algorithm operates at the moment of scrutiny — when evidence either exists or it does not.
We produce deterministic, replayable accountability records.
Interpretation, remediation, and compliance claims are out of scope.
Evidence first.
Scrutiny second.
No narrative repair.
Engagements include scenario demos, fixed-scope pilots, and production evidence continuity.