Neural Information Processing Systems http://nips.cc/

Autonomous vehicles (AVs) must be both safe and trustworthy to gain social acceptance and become a viable option for everyday public transportation. Explanations about the system behaviour can increase safety and trust in AVs. Unfortunately, explaining the system behaviour of AI-based driving functions is particularly challenging, as decision-making processes are often opaque. The field of Explainability Engineering tackles this challenge by developing explanation models at design time. These models are designed from system design artefacts and stakeholder needs to develop correct and good explanations. To support this field, we propose an approach that enables context-aware, ante-hoc explanations of (un)expectable driving manoeuvres at runtime. The visual yet formal language Traffic Sequence Charts is used to formalise explanation contexts, as well as corresponding (un)expectable driving manoeuvres. A dedicated runtime monitoring enables context-recognition and ante-hoc presentation of explanations at runtime. In combination, we aim to support the bridging of correct and good explanations. Our method is demonstrated in a simulated overtaking.

CXPlain is significantly more accurate and faster than existing model-agnostic methods for estimating feature importance.

R1: "I am not entirely convinced that an amortized explanation model is a reasonable thing. R2: "I would appreciate some clarification about what is gained by learning ˆ A and not just reporting Ω directly." We thank R1, R2 and R3 for their insightful feedback. R1: "(The objective) does not attribute importance to features that change how the model goes wrong (...)" R1: "Why is the rank-based method necessary? R2: "Additionally, can the authors clarify what is being averaged in the definition of the causal objective? R2: "If the goal is to determine what might happen to our predictions if we change a particular feature slightly Our goal is not to estimate what would happen if a particular feature's value changed, but to provide a causal explanation R2: "Some additional clarity on why the authors are using a KL discrepancy is merited. R3: "Masking one by one; this is essentially equivalent to assuming that feature contributions are additive." We do not define a feature's importance as its additive contribution to the model output, but as it's marginal reduction This subtle change in definition allows us to efficiently compute feature importance one by one. R3: "Replacing a masked value by a point-wise estimation can be very bad, especially when the classifiers output Why would the average value (or, even worse, zero) be meaningful?" We will clarify this point in the next revision. R3: "It would also be interesting to compare the proposed method with causal inference technique for SEMs." Recent work [29] has explored the use of SEMs for model attribution in deep learning. CXPlain can explain any machine-learning model, and (ii) attribution time was considerably slower than CXPlain. R3: "It seems to me that the chosen performance measure may correlate much more with the Granger-causal loss

Results are averaged over 10 fairwashing attacks.

In particular, we show that fairwashed explanation models can generalize beyond the suing group ( i.e., data points that are being explained), meaning that

Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applications, such as job-person fit and explanation in job seeking platforms, introduces distinct challenges. At LinkedIn, the job person fit task requires analyzing a candidate's public profile against job requirements to produce both a fit assessment and a detailed explanation. Directly applying open source or finetuned LLMs to this task often fails to yield high quality, actionable feedback due to the complexity of the domain and the need for structured outputs. Moreover, the large size of these models leads to high inference latency and limits scalability, making them unsuitable for online use. To address these challenges, we introduce LANTERN, a novel LLM knowledge distillation framework tailored specifically for job person fit tasks. LANTERN involves modeling over multiple objectives, an encoder model for classification purpose, and a decoder model for explanation purpose. To better distill the knowledge from a strong black box teacher model to multiple downstream models, LANTERN incorporates multi level knowledge distillation that integrates both data and logit level insights. In addition to introducing the knowledge distillation framework, we share our insights on post training techniques and prompt engineering, both of which are crucial for successfully adapting LLMs to domain specific downstream tasks. Extensive experimental results demonstrate that LANTERN significantly improves task specific metrics for both job person fit and explanation. Online evaluations further confirm its effectiveness, showing measurable gains in job seeker engagement, including a 0.24\% increase in apply rate and a 0.28\% increase in qualified applications.

CXPlain is significantly more accurate and faster than existing model-agnostic methods for estimating feature importance.

Explainability is a critical tool in helping stakeholders understand robots. In particular, the ability for robots to explain why they have made a particular decision or behaved in a certain way is useful in this regard. Behaviour trees are a popular framework for controlling the decision-making of robots and other software systems, and thus a natural question to ask is whether or not a system driven by a behaviour tree is capable of answering "why" questions. While explainability for behaviour trees has seen some prior attention, no existing methods are capable of generating causal, counterfactual explanations which detail the reasons for robot decisions and behaviour. Therefore, in this work, we introduce a novel approach which automatically generates counterfactual explanations in response to contrastive "why" questions. Our method achieves this by first automatically building a causal model from the structure of the behaviour tree as well as domain knowledge about the state and individual behaviour tree nodes. The resultant causal model is then queried and searched to find a set of diverse counterfactual explanations. We demonstrate that our approach is able to correctly explain the behaviour of a wide range of behaviour tree structures and states. By being able to answer a wide range of causal queries, our approach represents a step towards more transparent, understandable and ultimately trustworthy robotic systems.