Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach

We examine counterfactual explanations for explaining the decisions made by model-based AI systems. The counterfactual approach we consider defines an explanation as a set of the system's data inputs that causally drives the decision (i.e., changing the inputs in the set changes the decision) and is irreducible (i.e., changing any subset of the inputs does not change the decision). We (1) demonstrate how this framework may be used to provide explanations for decisions made by general, data-driven AI systems that may incorporate features with arbitrary data types and multiple predictive models, and (2) propose a heuristic procedure to find the most useful explanations depending on the context. We then contrast counterfactual explanations with methods that explain model predictions by weighting features according to their importance (e.g., SHAP, LIME) and present two fundamental reasons why we should carefully consider whether importance-weight explanations are well-suited to explain system decisions. Specifically, we show that (i) features that have a large importance weight for a model prediction may not affect the corresponding decision, and (ii) importance weights are insufficient to communicate whether and how features influence decisions. We demonstrate this with several concise examples and three detailed case studies that compare the counterfactual approach with SHAP to illustrate various conditions under which counterfactual explanations explain data-driven decisions better than importance weights

Counterfactual Explanations for Data-Driven Decisions

Users’ lack of understanding of systems that use predictive models to make automated decisions is one of the main barriers for their adoption. We adopt the increasingly accepted view of a counterfactual explanation for a system decision: a set of the system inputs that is causal (meaning that removing them changes the decision) and irreducible (meaning that removing any subset of the inputs in the explanation does not change the decision). We generalize previous work on counterfactual explanations in three ways: we explain system decisions rather than model predictions; we do not enforce any specific method for removing inputs, and our explanations can incorporate inputs with arbitrary data structures. We also show how model-agnostic algorithms can be tweaked to find the most useful explanations depending on the context. Finally, we showcase our approach using a real data set to illustrate its advantages over other explanation methods when the goal is to understand system decisions better

Learning Global-aware Kernel for Image Harmonization

Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing methods employ local color transformation or region matching between foreground and background, which neglects powerful proximity prior and independently distinguishes fore-/back-ground as a whole part for harmonization. As a result, they still show a limited performance across varied foreground objects and scenes. To address this issue, we propose a novel Global-aware Kernel Network (GKNet) to harmonize local regions with comprehensive consideration of long-distance background references. Specifically, GKNet includes two parts, \ie, harmony kernel prediction and harmony kernel modulation branches. The former includes a Long-distance Reference Extractor (LRE) to obtain long-distance context and Kernel Prediction Blocks (KPB) to predict multi-level harmony kernels by fusing global information with local features. To achieve this goal, a novel Selective Correlation Fusion (SCF) module is proposed to better select relevant long-distance background references for local harmonization. The latter employs the predicted kernels to harmonize foreground regions with both local and global awareness. Abundant experiments demonstrate the superiority of our method for image harmonization over state-of-the-art methods, \eg, achieving 39.53dB PSNR that surpasses the best counterpart by +0.78dB $\uparrow$; decreasing fMSE/MSE by 11.5\%$\downarrow$/6.7\%$\downarrow$ compared with the SoTA method. Code will be available at \href{https://github.com/XintianShen/GKNet}{here}.Comment: 10 pages, 10 figure

Deconfined quantum critical point lost in pressurized SrCu2(BO3)2

In the field of correlated electron materials, the relation between the resonating spin singlet and antiferromagnetic states has long been an attractive topic for understanding of the interesting macroscopic quantum phenomena, such as the ones emerging from magnetic frustrated materials, antiferromagnets and high-temperature superconductors. SrCu2(BO3)2 is a well-known quantum magnet, and it is theoretically expected to be the candidate of correlated electron material for clarifying the existence of a pressure-induced deconfined quantum critical point (DQCP), featured by a continuous quantum phase transition, between the plaquette-singlet (PS) valence bond solid phase and the antiferromagnetic (AF) phase. However, the real nature of the transition is yet to be identified experimentally due to the technical challenge. Here we show the experimental results for the first time, through the state-of-the-art high-pressure heat capacity measurement, that the PS-AF phase transition of the pressurized SrCu2(BO3)2 at zero field is clearly a first-order one. Our result clarifies the more than two-decade long debates about this key issue, and resonates nicely with the recent quantum entanglement understanding that the theoretically predicted DQCPs in representative lattice models are actually a first-order transition. Intriguingly, we also find that the transition temperatures of the PS and AF phase meet at the same pressure-temperature point, which signifies a bi-critical point as those observed in Fe-based superconductor and heavy-fermion compound, and constitutes the first experimental discovery of the pressure-induced bi-critical point in frustrated magnets. Our results provide fresh information for understanding the evolution among different spin states of correlated electron materials under pressure.Comment: 6 pages, 4 figure

Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening

The molecular diversity of transcriptional factor TfoX is a determinant in natural transformation in Glaesserella parasuis

Natural transformation is a mechanism by which a particular bacterial species takes up foreign DNA and integrates it into its genome. The swine pathogen Glaesserella parasuis (G. parasuis) is a naturally transformable bacterium. The regulation of competence, however, is not fully understood. In this study, the natural transformability of 99 strains was investigated. Only 44% of the strains were transformable under laboratory conditions. Through a high-resolution melting curve and phylogenetic analysis, we found that genetic differences in the core regulator of natural transformation, the tfoX gene, leads to two distinct natural transformation phenotypes. In the absence of the tfoX gene, the highly transformable strain SC1401 lost its natural transformability. In addition, when the SC1401 tfoX gene was replaced by the tfoX of SH0165, which has no natural transformability, competence was also lost. These results suggest that TfoX is a core regulator of natural transformation in G. parasuis, and that differences in tfoX can be used as a molecular indicator of natural transformability. Transcriptomic and proteomic analyses of the SC1401 wildtype strain, and a tfoX gene deletion strain showed that differential gene expression and protein synthesis is mainly centered on pathways related to glucose metabolism. The results suggest that tfoX may mediate natural transformation by regulating the metabolism of carbon sources. Our study provides evidence that tfoX plays an important role in the natural transformation of G. parasuis

Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach

We examine counterfactual explanations for explaining the decisions made by model-based AI systems. The counterfactual approach we consider defines an explanation as a set of the system’s data inputs that causally drives the decision (i.e., changing the inputs in the set changes the decision) and is irreducible (i.e., changing any subset of the inputs does not change the decision). We (1) demonstrate how this framework may be used to provide explanations for decisions made by general data-driven AI systems that can incorporate features with arbitrary data types and multiple predictive models, and (2) propose a heuristic procedure to find the most useful explanations depending on the context. We then contrast counterfactual explanations with methods that explain model predictions by weighting features according to their importance (e.g., Shapley additive explanations [SHAP], local interpretable model-agnostic explanations [LIME]) and present two fundamental reasons why we should carefully consider whether importance-weight explanations are well suited to explain system decisions. Specifically, we show that (1) features with a large importance weight for a model prediction may not affect the corresponding decision, and (2) importance weights are insufficient to communicate whether and how features influence decisions. We demonstrate this with several concise examples and three detailed case studies that compare the counterfactual approach with SHAP to illustrate conditions under which counterfactual explanations explain data-driven decisions better than importance weights

Fabrication of Polyethyleneimine-Modified Nanocellulose/Magnetic Bentonite Composite as a Functional Biosorbent for Efficient Removal of Cu(Ⅱ)

A novel inorganic–organic biosorbent, polyethyleneimine (PEI)-modified nanocellulose cross-linked with magnetic bentonite, was prepared for the removal of Cu(Ⅱ) from water. Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD) showed that the amino and carboxyl groups were successfully grafted onto the nanocellulose structure. The adsorption performance of Cu(Ⅱ) with various factors, using the biosorbent, was investigated. The results show that the adsorption equilibrium could be reached within a short time (10 min), and the adsorption capacity of Cu(Ⅱ) reached up to 757.45 mg/g. The adsorption kinetics and adsorption isotherms were well-fitted with the pseudo-second-order and the Freundlich isotherm models, respectively. The adsorption process of the composite is mainly controlled by chemisorption, and functional group chelation and electrostatic force were the adsorption mechanisms; pore filling also has a great influence on the adsorption of Cu(Ⅱ). It was found that the prepared modified nanocellulose composite has great potential for the removal of heavy metals from water