Comparative Fairness Analysis of SageMaker Autopilot and FairAutoML on Benchmark Datasets

Abstract

Abstract: ``AI as a Service'' (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools. AIaaS enables its customers (users)—who may lack the expertise, data, and/or resources to develop their own systems—to easily build and integrate AI capabilities into their applications. Yet, it is known that AI systems can encapsulate biases and inequalities that can have societal impact. This paper argues that the context-sensitive nature of fairness is often incompatible with AIaaS' `one-size-fits-all' approach, leading to issues and tensions. Specifically, we review and systematise

Research Question

How does the fairness performance of SageMaker Autopilot's preprocessing pipeline compare to that of state-of-the-art fairness-aware AutoML systems like FairAutoML on benchmark datasets (e.g., COMPAS, Adult) when evaluated using demographic parity and equal opportunity metrics?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Truth-Engine Gate Verdict

This record has not completed Gate 2 of the verification pipeline (a type-checked Lean4 proof for mathematical claims, or a sealed-sandbox reproduction for empirical claims). It is a literature synthesis only. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance