The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomes

When users receive either a positive or negative outcome from an automated system, Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes into positive ones by crossing a decision boundary using counterfactuals (e.g., \textit{"If you earn 2k more, we will accept your loan application"}). Here, we instead focus on \textit{positive} outcomes, and take the novel step of using XAI to optimise them (e.g., \textit{"Even if you wish to half your down-payment, we will still accept your loan application"}). Explanations such as these that employ "even if..." reasoning, and do not cross a decision boundary, are known as semifactuals. To instantiate semifactuals in this context, we introduce the concept of \textit{Gain} (i.e., how much a user stands to benefit from the explanation), and consider the first causal formalisation of semifactuals. Tests on benchmark datasets show our algorithms are better at maximising gain compared to prior work, and that causality is important in the process. Most importantly however, a user study supports our main hypothesis by showing people find semifactual explanations more useful than counterfactuals when they receive the positive outcome of a loan acceptance

On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning

There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly explain their operation and outputs. In response to this disquiet counterfactual explanations have become massively popular in eXplainable AI (XAI) due to their proposed computational psychological, and legal benefits. In contrast however, semifactuals, which are a similar way humans commonly explain their reasoning, have surprisingly received no attention. Most counterfactual methods address tabular rather than image data, partly due to the nondiscrete nature of the latter making good counterfactuals difficult to define. Additionally generating plausible looking explanations which lie on the data manifold is another issue which hampers progress. This paper advances a novel method for generating plausible counterfactuals (and semifactuals) for black box CNN classifiers doing computer vision. The present method, called PlausIble Exceptionality-based Contrastive Explanations (PIECE), modifies all exceptional features in a test image to be normal from the perspective of the counterfactual class (hence concretely defining a counterfactual). Two controlled experiments compare this method to others in the literature, showing that PIECE not only generates the most plausible counterfactuals on several measures, but also the best semifactuals.Comment: 4 figures, 9 page

Human-Guided Complexity-Controlled Abstractions

Neural networks often learn task-specific latent representations that fail to generalize to novel settings or tasks. Conversely, humans learn discrete representations (i.e., concepts or words) at a variety of abstraction levels (e.g., "bird" vs. "sparrow") and deploy the appropriate abstraction based on task. Inspired by this, we train neural models to generate a spectrum of discrete representations, and control the complexity of the representations (roughly, how many bits are allocated for encoding inputs) by tuning the entropy of the distribution over representations. In finetuning experiments, using only a small number of labeled examples for a new task, we show that (1) tuning the representation to a task-appropriate complexity level supports the highest finetuning performance, and (2) in a human-participant study, users were able to identify the appropriate complexity level for a downstream task using visualizations of discrete representations. Our results indicate a promising direction for rapid model finetuning by leveraging human insight.Comment: NeurIPS 202

Birth delivery method affects expression of immune genes in lung and jejunum tissue of neonatal beef calves

peer-reviewedBackground Caesarean section is a routine veterinary obstetrical procedure employed to alleviate dystocia in cattle. However, CS, particularly before the onset of labour, is known to negatively affect neonatal respiration and metabolic adaptation in humans, though there is little published information for cattle. The aim of this study was to investigate the effect of elective caesarean section (ECS) or normal trans-vaginal (TV) delivery, on lung and jejunal gene expression profiles of neonatal calves. Results Paternal half-sib Angus calves (gestation length 278 + 1.8 d) were delivered either transvaginally (TV; n = 8) or by elective caesarean section (ECS; n = 9) and immediately euthanized. Lung and jejunum epithelial tissue was isolated and snap frozen. Total RNA was extracted using Trizol reagent and reverse transcribed to generate cDNA. For lung tissue, primers were designed to target genes involved in immunity, surfactant production, cellular detoxification, membrane transport and mucin production. Primers for jejunum tissue were chosen to target mucin production, immunoglobulin uptake, cortisol reaction and membrane trafficking. Quantitative real-time PCR reactions were performed and data were statistically analysed using mixed models ANOVA. In lung tissue the expression of five genes were affected (p < 0.05) by delivery method. Four of these genes were present at lower (LAP, CYP1A1, SCN11α and SCN11β) and one (MUC5AC) at higher abundance in ECS compared with TV calves. In jejunal tissue, expression of TNFα, Il-1β and 1 l-6 was higher in ECS compared with TV calves. Conclusions This novel study shows that ECS delivery affects the expression of key genes involved in the efficiency of the pulmonary liquid to air transition at birth, and may lead to an increased inflammatory response in jejunal tissue, which could compromise colostral immunoglobulin absorption. These findings are important to our understanding of the viability and management of neonatal calves born through ECS

Illumina MiSeq Phylogenetic Amplicon Sequencing Shows a Large Reduction of an Uncharacterised Succinivibrionaceae and an Increase of the Methanobrevibacter gottschalkii Clade in Feed Restricted Cattle

peer-reviewedPeriodic feed restriction is used in cattle production to reduce feed costs. When normal feed levels are resumed, cattle catch up to a normal weight by an acceleration of normal growth rate, known as compensatory growth, which is not yet fully understood. Illumina Miseq Phylogenetic marker amplicon sequencing of DNA extracted from rumen contents of 55 bulls showed that restriction of feed (70% concentrate, 30% grass silage) for 125 days, to levels that caused a 60% reduction of growth rate, resulted in a large increase of relative abundance of Methanobrevibacter gottschalkii clade (designated as OTU-M7), and a large reduction of an uncharacterised Succinivibrionaceae species (designated as OTU-S3004). There was a strong negative Spearman correlation (ρ = -0.72, P = <1x10-20) between relative abundances of OTU-3004 and OTU-M7 in the liquid rumen fraction. There was also a significant increase in acetate:propionate ratio (A:P) in feed restricted animals that showed a negative Spearman correlation (ρ = -0.69, P = <1x10-20) with the relative abundance of OTU-S3004 in the rumen liquid fraction but not the solid fraction, and a strong positive Spearman correlation with OTU-M7 in the rumen liquid (ρ = 0.74, P = <1x10-20) and solid (ρ = 0.69, P = <1x10-20) fractions. Reduced A:P ratios in the rumen are associated with increased feed efficiency and reduced production of methane which has a global warming potential (GWP 100 years) of 28. Succinivibrionaceae growth in the rumen was previously suggested to reduce methane emissions as some members of this family utilise hydrogen, which is also utilised by methanogens for methanogenesis, to generate succinate which is converted to propionate. Relative abundance of OTU-S3004 showed a positive Spearman correlation with propionate (ρ = 0.41, P = <0.01) but not acetate in the liquid rumen fraction.This study was supported by the Science Foundation Ireland (http://www.sfi.ie) (Contract number SFI 09/RFP/GEN2447-awarded to SMW) and Teagasc Walsh Fellowship Funding (www.teagasc.ie) (Teagasc project RMIS 6341-awarded to SMW)

Longitudinal Study on Sustained Attention to Response Task (SART): Clustering Approach for Mobility and Cognitive Decline

The Sustained Attention to Response Task (SART) is a computer-based go/no-go task to measure neurocognitive function in older adults. However, simplified average features of this complex dataset lead to loss of primary information and fail to express associations between test performance and clinically meaningful outcomes. Here, we combine a novel method to visualise individual trial (raw) information obtained from the SART test in a large population-based study of ageing in Ireland and an automatic clustering technique. We employed a thresholding method, based on the individual trial number of mistakes, to identify poorer SART performances and a fuzzy clusters algorithm to partition the dataset into 3 subgroups, based on the evolution of SART performance after 4 years. Raw SART data were available for 3468 participants aged 50 years and over at baseline. The previously reported SART visualisation-derived feature 'bad performance', indicating the number of SART trials with at least 4 mistakes, and its evolution over time, combined with the fuzzy c-mean (FCM) algorithm, individuated 3 clusters corresponding to 3 degrees of physiological dysregulation. The biggest cluster (94% of the cohort) was constituted by healthy participants, a smaller cluster (5% of the cohort) by participants who showed improvement in cognitive and psychological status, and the smallest cluster (1% of the cohort) by participants whose mobility and cognitive functions dramatically declined after 4 years. We were able to identify in a cohort of relatively high-functioning community-dwelling adults a very small group of participants who showed clinically significant decline. The selected smallest subset manifested not only mobility deterioration, but also cognitive decline, the latter being usually hard to detect in population-based studies. The employed techniques could identify at-risk participants with more specificity than current methods, and help clinicians better identify and manage the small proportion of community-dwelling older adults who are at significant risk of functional decline and loss of independence

Identification and characterization of an acyl-CoA dehydrogenase from Pseudomonas putida KT2440 that shows preference towards medium to long chain length fatty acids

Diverse and elaborate pathways for nutrient utilization, as well as mechanisms to combat unfavourable nutrient conditions make Pseudomonas putida KT2440 a versatile micro-organism able to occupy a range of ecological niches. The fatty acid degradation pathway of P. putida is complex and correlated with biopolymer medium chain length polyhydroxyalkanoate (mcl-PHA) biosynthesis. Little is known about the second step of fatty acid degradation (beta-oxidation) in this strain. In silico analysis of its genome sequence revealed 21 putative acyl-CoA dehydrogenases (ACADs), four of which were functionally characterized through mutagenesis studies. Four mutants with insertionally inactivated ACADs (PP_1893, PP_2039, PP_2048 and PP_2437) grew and accumulated mcl-PHA on a range of fatty acids as the sole source of carbon and energy. Their ability to grow and accumulate biopolymer was differentially negatively affected on various fatty acids, in comparison to the wild-type strain. Inactive PP_2437 exhibited a pattern of reduced growth and PHA accumulation when fatty acids with lengths of 10 to 14 carbon chains were used as substrates. Recombinant expression and biochemical characterization of the purified protein allowed functional annotation in P. putida KT2440 as an ACAD showing clear preference for dodecanoyl-CoA ester as a substrate and optimum activity at 30 degrees C and pH 6.5-7