Uncovering Algorithmic Fairness in Deep Learning–Based Imputation of Multivariate Clinical Time Series in Heart Failure Patients - Muniz, Rayssa; Lima, Victor; Saldanha, Paloma; Ribeiro, Andrea; Paula, Rodrigo de; Cadeiras, Martin; Silva, Carlo da; Silva, Hítalo; Rocha, Paulo; Pinheiro, Diego (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 2025)
Deep learning for missing data imputation (MDI) in healthcare time series has advanced, but fairness concerns remain underexplored. Traditional evaluations focus on global error metrics (e.g., MAE), overlooking disparities across variables and protected subgroups. We analyze five state-of-the-art MDI models (BRITS, SAITS, USGAN, GPVAE, MRNN) on a heart failure dataset (PhysioNet), assessing fairness via Lorenz curves and the Gini coefficient. Results reveal that low MAE does not imply fairness. SAITS was most efficient (MAE = 0.241) but least fair (Gini = 0.615), while MRNN was less efficient (MAE = 0.672) yet fairer (Gini = 0.439). We highlight the need to balance accuracy and fairness in MDI for clinical applications.