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

Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier

This paper reports two experiments (N=349) on the impact of post hoc explanations by example and error rates on peoples perceptions of a black box classifier. Both experiments show that when people are given case based explanations, from an implemented ANN CBR twin system, they perceive miss classifications to be more correct. They also show that as error rates increase above 4%, people trust the classifier less and view it as being less correct, less reasonable and less trustworthy. The implications of these results for XAI are discussed.Comment: 2 Figures, 1 Table, 8 page

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)

Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification

The 28th International Conference on Computational Linguistics (COLING'2020), Online Conference, 8-13 December 2020Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is crucial to not only have a highly robust and accurate model, but be able to generate useful explanations to garner a user’s trust in the automated system. Regrettably, the recent research regarding eXplainable AI (XAI) in financial text classification has received little to no attention, and many current methods for generating textual-based explanations result in highly implausible explanations, which damage a user’s trust in the system. To address these issues, this paper proposes a novel methodology for producing plausible counterfactual explanations, whilst exploring the regularization benefits of adversarial training on language models in the domain of FinTech. Exhaustive quantitative experiments demonstrate that not only does this approach improve the model accuracy when compared to the current stateof-the-art and human performance, but it also generates counterfactual explanations which are significantly more plausible based on human trials.Science Foundation Ireland2021-02-11 JG: resubmitted due to broken PD

Plasma lutein and zeaxanthin concentrations associated with musculoskeletal health and incident frailty in The Irish Longitudinal Study on Ageing (TILDA)

peer-reviewedIntroduction Lutein and zeaxanthin are diet-derived carotenoids that are proposed to help mitigate frailty risk and age-related declines in musculoskeletal health via their anti-oxidant and anti-inflammatory properties. Therefore, this study aimed to investigate the association between lutein and zeaxanthin status and indices of musculoskeletal health and incident frailty among community-dwelling adults aged ≥50 years in the Irish Longitudinal Study on Ageing (TILDA). Methods Cross-sectional analyses (n = 4513) of plasma lutein and zeaxanthin concentrations and grip strength, usual gait speed, timed up-and-go (TUG), probable sarcopenia (defined as grip strength <27 kg in men, <16 kg in women), and bone mass (assessed using calcaneal broadband ultrasound stiffness index) were performed at Wave 1 (2009–2011; baseline). In the longitudinal analyses (n = 1425–3100), changes in usual gait speed (at Wave 3, 2014–2015), grip strength (Wave 4, 2016) and TUG (at Wave 5, 2018), incident probable sarcopenia (at Wave 4) and incident frailty (Fried's phenotype, Frailty Index, FRAIL Scale, Clinical Frailty Scale-classification tree, at Wave 5) were determined. Data were analysed using linear and ordinal logistic regression, adjusted for confounders. Results Cross-sectionally, plasma lutein and zeaxanthin concentrations were positively associated with usual gait speed (B [95 % CI] per 100-nmol/L higher concentration: Lutein 0.59 [0.18, 1.00], Zeaxanthin 1.46 [0.37, 2.55] cm/s) and inversely associated with TUG time (Lutein −0.07 [−0.11, −0.03], Zeaxanthin −0.14 [−0.25, −0.04] s; all p 0.05). Plasma lutein concentration was positively associated with bone stiffness index (0.54 [0.15, 0.93], p 0.05). Conclusion Higher plasma lutein and zeaxanthin concentrations at baseline were associated with a reduced likelihood of incident frailty after ~8 years of follow up. Baseline plasma lutein and zeaxanthin concentrations were also positively associated with several indices of musculoskeletal health cross-sectionally but were not predictive of longitudinal changes in these outcomes over 4–8 years.Horizon 2020 Marie Skłodowska-Curie ActionsThis work was supported by the Teagasc Research Leaders 2025 programme co-funded by Teagasc and the European Union's Horizon 2020 - Research and Innovation Framework Programme under the H2020 Marie Skłodowska-Curie Actions grant agreement number 754380. TILDA is funded by Atlantic Philanthropies, the Irish Department of Health and Irish Life. Roman Romero-Ortuno is funded by a grant from Science Foundation Ireland under grant number 18/FRL/6188

Explaining Artificial Neural Networks With Post-Hoc Explanation-By-Example

Explainable artificial intelligence (XAI) has become one of the most popular research areas in AI the past several years, with many workshops, conferences, and government/industry research programs now dedicated to the topic. Historically, one of the main avenues for this type of research was based around showing previous examples to explain or justify an automated prediction by an AI, and these explanations have seen a resurgence recently to help deal with the opaque nature of modern black-box deep learning systems because of how they mimic human reasoning. However, recent implementations of this explanation strategy do not abstract the black-box AI’s reasoning in a faithful way, or focus on important features used in a prediction. Moreover, generated synthetic examples shown are often lacking in plausibility. This thesis explores all these avenues both computationally and in user testing. The results demonstrate (1) a novel approach called twin-systems for computing nearest neighbour explanations which have the highest fidelity to the AI system it is explaining relative to other state-of-the-art methods, (2) the introduction of a novel XAI approach which focuses on specific “parts” of the explanations in twin-systems, (3) that these explanations have the effect of making misclassifications seem less incorrect in user testing, and (4) that other options aside from nearest neighbour explanations (e.g., semi-factuals) are valid options and deserve more attention in the scientific community.2022-11-08 JG: Author's signature removed from PD