Towards real interpretability of student success prediction combining methods of XAI and social science

Despite calls to increase the focus on explainability and interpretability in EDM and, in particular, student success prediction, so that it becomes useful for personalized intervention systems, only few efforts have been undertaken in that direction so far. In this paper, we argue that this is mainly due to the limitations of current Explainable Artificial Intelligence (XAI) approaches regarding interpretability. We further argue that the issue, thus, calls for a a combination of AI and social science methods utilizing the strengths of both. For this, we introduce a step-wise model of interpretability where the first step constitutes of knowing important features, the second step of understanding counterfactuals regarding a particular person’s prediction, and the third step of uncovering causal relations relevant for a set of similar students. We show that LIME, a current XAI method, reaches the first but not subsequent steps. To reach step two, we propose an extension to LIME, Minimal Counterfactual-LIME, finding the smallest number of changes necessary to change a prediction. Reaching step three, however, is more involved and additionally requires theoretical and causal reasoning - to this end, we construct an easily applicable framework. Using artificial data, we showcase that our methods can recover connections among features; additionally, we demonstrate its applicability on real-life data. Limitations of our methods are discussed and collaborations with social scientists encouraged

Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?

Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is important that goal recognition algorithms can recognize goals of an observed agent as fast as possible. However, many early approaches in the area of Plan Recognition As Planning, require quite large amounts of computation time to calculate a solution. Mainly to address this issue, recently, Pereira et al. developed an approach that is based on planning landmarks and is much more computationally efficient than previous approaches. However, the approach, as proposed by Pereira et al., also uses trivial landmarks (i.e., facts that are part of the initial state and goal description are landmarks by definition). In this paper, we show that it does not provide any benefit to use landmarks that are part of the initial state in a planning landmark based goal recognition approach. The empirical results show that omitting initial state landmarks for goal recognition improves goal recognition performance.Comment: Will be presented at KI 202

The roles of poly(ADP-ribose)-metabolizing enzymes in alkylation-induced cell death

Abstract.: Poly(ADP-ribose) (PAR) has been identified as a DNA damage-inducible cell death signal upstream of apoptosis-inducing factor (AIF). PAR causes the translocation of AIF from mitochondria to the nucleus and triggers cell death. In living cells, PAR molecules are subject to dynamic changes pending on internal and external stress factors. Using RNA interference (RNAi), we determined the roles of poly(ADP-ribose) polymerases-1 and -2 (PARP-1, PARP-2) and poly(ADP-ribose) glycohydrolase (PARG), the key enzymes configuring PAR molecules, in cell death induced by an alkylating agent. We found that PARP-1, but not PARP-2 and PARG, contributed to alkylation-induced cell death. Likewise, AIF translocation was only affected by PARP-1. PARP-1 seems to play a major role configuring PAR as a death signal involving AIF translocation regardless of the death pathway involve

The roles of poly(ADP-ribose)-metabolizing enzymes in alkylation-induced cell death

Poly(ADP-ribose) (PAR) has been identified as a DNA damage-inducible cell death signal upstream of apoptosis-inducing factor (AIF). PAR causes the translocation of AIF from mitochondria to the nucleus and triggers cell death. In living cells, PAR molecules are subject to dynamic changes pending on internal and external stress factors. Using RNA interference (RNAi), we determined the roles of poly(ADP-ribose) polymerases-1 and -2 (PARP-1, PARP-2) and poly(ADP-ribose) glycohydrolase (PARG), the key enzymes configuring PAR molecules, in cell death induced by an alkylating agent. We found that PARP-1, but not PARP-2 and PARG, contributed to alkylation-induced cell death. Likewise, AIF translocation was only affected by PARP-1. PARP-1 seems to play a major role configuring PAR as a death signal involving AIF translocation regardless of the death pathway involved

Morbidity and mortality after anaesthesia in early life: results of the European prospective multicentre observational study, neonate and children audit of anaesthesia practice in Europe (NECTARINE)

BACKGROUND: Neonates and infants requiring anaesthesia are at risk of physiological instability and complications, but triggers for peri-anaesthetic interventions and associations with subsequent outcome are unknown. METHODS: This prospective, observational study recruited patients up to 60 weeks' postmenstrual age undergoing anaesthesia for surgical or diagnostic procedures from 165 centres in 31 European countries between March 2016 and January 2017. The primary aim was to identify thresholds of pre-determined physiological variables that triggered a medical intervention. The secondary aims were to evaluate morbidities, mortality at 30 and 90 days, or both, and associations with critical events. RESULTS: Infants (n=5609) born at mean (standard deviation [sd]) 36.2 (4.4) weeks postmenstrual age (35.7% preterm) underwent 6542 procedures within 63 (48) days of birth. Critical event(s) requiring intervention occurred in 35.2% of cases, mainly hypotension (>30% decrease in blood pressure) or reduced oxygenation (SpO2 <85%). Postmenstrual age influenced the incidence and thresholds for intervention. Risk of critical events was increased by prior neonatal medical conditions, congenital anomalies, or both (relative risk [RR]=1.16; 95% confidence interval [CI], 1.04–1.28) and in those requiring preoperative intensive support (RR=1.27; 95% CI, 1.15–1.41). Additional complications occurred in 16.3% of patients by 30 days, and overall 90-day mortality was 3.2% (95% CI, 2.7–3.7%). Co-occurrence of intraoperative hypotension, hypoxaemia, and anaemia was associated with increased risk of morbidity (RR=3.56; 95% CI, 1.64–7.71) and mortality (RR=19.80; 95% CI, 5.87–66.7). CONCLUSIONS: Variability in physiological thresholds that triggered an intervention, and the impact of poor tissue oxygenation on patient's outcome, highlight the need for more standardised perioperative management guidelines for neonates and infants