The missing data problem widely exists in real-life scenarios. The incomplete data analysis through imputation can amplify the errors or bias, hindering the effective analysis. Ex-isting tabular data representation methods overlook the missing state of data values, and thus cannot effectively deal with the incomplete data. In this paper, we propose a novel incomplete tabular data representation system, named MISS. It is capable of enabling all Transformer-based tabular representation methods to effectively handle incomplete data. MISS consists of two modules, i.e., missing mechanism learning (MML) and incomplete data representation (IDR). MML leverages a new missingness propensity score calculation strategy to learn the observed data distribution and missing mechanisms within incomplete data. IDR introduces a novel probability-driven Transformer block, in conjunction with an unbiased representation loss function, for effective representation. We prove that, MISS can eliminate the bias resulting from missingness. Extensive experiments on four public real-world datasets demonstrate that, MISS yields a more than 57 % accuracy gain with competitive efficiency, compared with the state-of-the-art approaches.

The missing values widely existed in tabular data hinder the effective analysis of algorithmic fairness. Existing fairness intervention algorithms incorporate constraints or regularizers to reduce discrimination which rely on the complete information. They cannot effectively handle common tabular data with missing values in both sensitive and non-sensitive attributes without imputation. In this paper, we propose a novel Transformer-based fairness-aware prediction model FATE that mitigates the bias introduced by missing values to achieve algorithmic fairness without imputation. FATE consists of two modules, i.e., an incomplete data encoding (IDE) module and a debiased representation learning (DRL) module. IDE designs an incomplete tabular data embedding strategy and a missingness-aware Transformer block to effectively learn the observed data distribution and the missing state information. DRL converts fairness into attention parity when the sensitive attributes are completely missing. It offers a debiased attention mechanism to normalize attention weights in the attention score calculation process. We theoretically prove that, the differences in attention scores can represent the demographic disparities among sensitive groups which in FATE are bounded by a constant, substantially minimizing the group discrimination. Extensive experiments on three public real-world datasets demonstrate that, FATE, with the competitive fairness, yields more than 22% accuracy gain, compared to the state of the arts.