Many decision-making scenarios in modern life benefit from the decision support of artificial intelligence algorithms, which focus on a data-driven philosophy and automated programs or systems. However, crucial decision issues related to security, fairness, and privacy should consider more human knowledge and principles to supervise such AI algorithms to reach more proper solutions and to benefit society more effectively. In this work, we extract knowledge-based logic that defines risky driving formats learned from public transportation accident datasets, which haven't been analyzed in detail to the best of our knowledge. More importantly, this knowledge is critical for recognizing traffic hazards and could supervise and improve AI models in safety-critical systems. Then we use automated verification methods to verify the robustness of such logic. More specifically, we gather 72 accident datasets from Data.gov and organize them by state. Further, we train Decision Tree and XGBoost models on each state's dataset, deriving accident judgment logic. Finally, we deploy robustness verification on these tree-based models under multiple parameter combinations.

Financial forecasting plays an important role in making informed decisions for financial stakeholders, specifically in the stock exchange market. In a traditional setting, investors commonly rely on the equity research department for valuable reports on market insights and investment recommendations. The equity research department, however, faces challenges in effectuating decision-making due to the demanding cognitive effort required for analyzing the inherently volatile nature of market dynamics. Furthermore, financial forecasting systems employed by analysts pose potential risks in terms of interpretability and gaining the trust of all stakeholders. This paper presents an interpretable decision-making model leveraging the SHAP-based explainability technique to forecast investment recommendations. The proposed solution not only provides valuable insights into the factors that influence forecasted recommendations but also caters to investors of varying types, including those interested in daily and short-term investment opportunities. To ascertain the efficacy of the proposed model, a case study is devised that demonstrates a notable enhancement in investor's portfolio value, employing our trading strategies. The results highlight the significance of incorporating interpretability in forecasting models to boost stakeholders' confidence and foster transparency in the stock exchange domain.

Automated Decision-Making Systems (ADS) have become pervasive across various fields, activities, and occupations, to enhance performance. However, this widespread adoption introduces potential risks, including the misuse of ADS. Such misuse may manifest when ADS is employed in situations where it is unnecessary or when essential requirements, conditions, and terms are overlooked, leading to unintended consequences. This research paper presents a thorough examination of the implications, distinctions, and ethical considerations associated with digitalization, digital transformation, and the utilization of ADS in contemporary society and future contexts. Emphasis is placed on the imperative need for regulation, transparency, and ethical conduct in the deployment of ADS.

In this paper, we tackle the incremental maintenance of Datalog inference materialisation when the rule set can be updated. This is particularly relevant in the context of the Internet of Things and Edge computing where smart devices may need to reason over newly acquired knowledge represented as Datalog rules. Our solution is based on an adaptation of a stratification strategy applied to a dependency hypergraph whose nodes correspond to rule sets in a Datalog program. Our implementation supports recursive rules containing both negation and aggregation. We demonstrate the effectiveness of our system on real and synthetic data.

Recent advances in natural language processing (NLP) owe their success to pre-training language models on large amounts of unstructured data. Still, there is an increasing effort to combine the unstructured nature of LMs with structured knowledge and reasoning. Particularly in the rapidly evolving field of biomedical NLP, knowledge-enhanced language models (KELMs) have emerged as promising tools to bridge the gap between large language models and domain-specific knowledge, considering the available biomedical knowledge graphs (KGs) curated by experts over the decades. In this paper, we develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models (PLMs). We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical ontology OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. The approach includes partitioning knowledge graphs into smaller subgraphs, fine-tuning adapter modules for each subgraph, and combining the knowledge in a fusion layer. We test the performance on three downstream tasks: document classification,question answering, and natural language inference. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low. Finally, we provide a detailed interpretation of the results and report valuable insights for future work.

Our goal is a modern approach to answering questions via systematic reasoning where answers are supported by human interpretable proof trees grounded in an NL corpus of authoritative facts. Such a system would help alleviate the challenges of interpretability and hallucination with modern LMs, and the lack of grounding of current explanation methods (e.g., Chain-of-Thought). This paper proposes a new take on Prolog-based inference engines, where we replace handcrafted rules with a combination of neural language modeling, guided generation, and semiparametric dense retrieval. Our implementation, NELLIE, is the first system to demonstrate fully interpretable, end-to-end grounded QA as entailment tree proof search, going beyond earlier work explaining known-to-be-true facts from text. In experiments, NELLIE outperforms a similar-sized state-of-the-art reasoner [Tafjord et al., 2022] while producing knowledge-grounded explanations. We also find NELLIE can exploit both semi-structured and NL text corpora to guide reasoning. Together these suggest a new way to jointly reap the benefits of both modern neural methods and traditional symbolic reasoning.

This review gives an extensive overview of evaluation methods for task-oriented dialogue systems, paying special attention to practical applications of dialogue systems, for example for customer service. The review (1) provides an overview of the used constructs and metrics in previous work, (2) discusses challenges in the context of dialogue system evaluation and (3) develops a research agenda for the future of dialogue system evaluation. We conducted a systematic review of four databases (ACL, ACM, IEEE and Web of Science), which after screening resulted in 122 studies. Those studies were carefully analysed for the constructs and methods they proposed for evaluation. We found a wide variety in both constructs and methods. Especially the operationalisation is not always clearly reported. We hope that future work will take a more critical approach to the operationalisation and specification of the used constructs. To work towards this aim, this review ends with recommendations for evaluation and suggestions for outstanding questions.

Users in many domains use machine learning (ML) predictions to help them make decisions. Effective ML-based decision-making often requires explanations of ML models and their predictions. While there are many algorithms that explain models, generating explanations in a format that is comprehensible and useful to decision-makers is a nontrivial task that can require extensive development overhead. We developed Pyreal, a highly extensible system with a corresponding Python implementation for generating a variety of interpretable ML explanations. Pyreal converts data and explanations between the feature spaces expected by the model, relevant explanation algorithms, and human users, allowing users to generate interpretable explanations in a low-code manner. Our studies demonstrate that Pyreal generates more useful explanations than existing systems while remaining both easy-to-use and efficient.

The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.

Our user study shows that the anomaly explanation form of PARs is better understood and favoured by Anomaly detection, which aims to identify data instances regular anomaly detection system users compared with existing that do not conform to the expected behavior, is a classic model-agnostic anomaly explanation options. In our machine learning task with numerous applications in experiments, we demonstrate that it is significantly more various domains including fraud detection, intrusion detection, efficient to find PARs than anchors (Ribeiro, Singh, and predictive maintenance, etc. Over the past decades, numerous Guestrin 2018), another rule-based explanation, for identified methods have been proposed to tackle this challenging anomaly instances. Moreover, PARs are also far more problem. Examples include one-class classificationbased accurate than anchors for anomaly explanation, meaning (Manevitz and Yousef 2001; Ruff et al. 2018), nearest that they have considerably higher precision and recall when neighbor-based (Breunig et al. 2000), clustering-based applied as anomaly detection rules on unseen data other (Jiang and An 2008), isolation-based (Liu, Ting, and Zhou than the anomaly instance on which they were originally derived 2012; Hariri, Kind, and Brunner 2019), density-based (Liu, for explanation. Additionally, we show that PARs can Tan, and Zhou 2022; Feng and Tian 2021) and deep anomaly also achieve higher accuracy on abnormal feature identification detection models based on autoencoders (Zhou and Paffenroth compared with many state-of-the-art model-agnostic 2017; Zong et al. 2018), generative adversarial networks explanation methods including LIME (Ribeiro, Singh, and (Zenati et al. 2018; Han, Chen, and Liu 2021), to Guestrin 2016), SHAP (Lundberg and Lee 2017), COIN name a few.