Researchers from the National University of Singapore have concluded that the more explainable AI becomes, the easier it will become to circumvent vital privacy features in machine learning systems. They also found that even when a model is not explainable, it's possible to use explanations of similar models to'decode' sensitive data in the non-explainable model. The research, titled Exploiting Explanations for Model Inversion Attacks, highlights the risks of using the'accidental' opacity of the way neural networks function as if this was a by-design security feature – not least because a wave of new global initiatives, including the European Union's draft AI regulations, are characterizing explainable AI (XAI) as a prerequisite for the eventual normalization of machine learning in society. In the research, an actual identity is successfully reconstructed from supposedly anonymous data relating to facial expressions, through the exploitation of multiple explanations of the machine learning system. 'Explainable artificial intelligence (XAI) provides more information to help users to understand model decisions, yet this additional knowledge exposes additional risks for privacy attacks.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. As AI systems increasingly inform decision-making in health care, finance, law, and criminal justice, they need to provide justifications for their behavior that humans can understand. The field of "explainable AI" has gained momentum as regulators turn a critical eye toward black-box AI systems -- and their creators. But how a person's background can shape perceptions of AI explanations is a question that remains underexplored. A new study coauthored by researchers at Cornell University, IBM, and the Georgia Institute of Technology aims to shed light on the intersection of interpretability and explainable AI.

I recently fell in love with Explainable AI (XAI). XAI is a set of methods aimed at making increasingly complex machine learning (ML) models understandable by humans. XAI could help bridge the gap between AI and humans. That is very much needed as the gap is widening. Machine learning is proving incredibly successful in tackling problems from cancer diagnostics to fraud detection.

The biggest shortcoming of many machine learning models and neural networks is their "blackbox" nature. Which feature was most influential in this predicted output that we got for an instance? XAI which stands for Explainable Artificial Intelligence is the area of study that tries to tackle this blackbox issue of models.

This extends the reach of Boolean logic by enabling a variety of applications that have been explored over the decades. The existential quantification of literals (variable states) and its applications have also been studied in the literature. In this paper, we complement this by studying universal literal quantification and its applications, particularly to explainable AI. We also provide a novel semantics for quantification, discuss the interplay between variable/literal and existential/universal quantification. We further identify some classes of Boolean formulas and circuits on which quantification can be done efficiently. Literal quantification is more fine-grained than variable quantification as the latter can be defined in terms of the former. This leads to a refinement of quantified Boolean logic with literal quantification as its primitive.

Existing studies on disease diagnostic models focus either on diagnostic model learning for performance improvement or on the visual explanation of a trained diagnostic model. We propose a novel learn-explain-reinforce (LEAR) framework that unifies diagnostic model learning, visual explanation generation (explanation unit), and trained diagnostic model reinforcement (reinforcement unit) guided by the visual explanation. For the visual explanation, we generate a counterfactual map that transforms an input sample to be identified as an intended target label. For example, a counterfactual map can localize hypothetical abnormalities within a normal brain image that may cause it to be diagnosed with Alzheimer's disease (AD). We believe that the generated counterfactual maps represent data-driven and model-induced knowledge about a target task, i.e., AD diagnosis using structural MRI, which can be a vital source of information to reinforce the generalization of the trained diagnostic model. To this end, we devise an attention-based feature refinement module with the guidance of the counterfactual maps. The explanation and reinforcement units are reciprocal and can be operated iteratively. Our proposed approach was validated via qualitative and quantitative analysis on the ADNI dataset. Its comprehensibility and fidelity were demonstrated through ablation studies and comparisons with existing methods.

Broad Explainable Artificial Intelligence moves away from interpreting individual decisions based on a single datum and aims to provide integrated explanations from multiple machine learning algorithms into a coherent explanation of an agent's behaviour that is aligned to the communication needs of the explainee. Reinforcement Learning (RL) methods, we propose, provide a potential backbone for the cognitive model required for the development of Broad-XAI. RL represents a suite of approaches that have had increasing success in solving a range of sequential decision-making problems. However, these algorithms all operate as black-box problem solvers, where they obfuscate their decision-making policy through a complex array of values and functions. EXplainable RL (XRL) is relatively recent field of research that aims to develop techniques to extract concepts from the agent's: perception of the environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and objectives. This paper aims to introduce a conceptual framework, called the Causal XRL Framework (CXF), that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI. Additionally, we recognise that RL methods have the ability to incorporate a range of technologies to allow agents to adapt to their environment. CXF is designed for the incorporation of many standard RL extensions and integrated with external ontologies and communication facilities so that the agent can answer questions that explain outcomes and justify its decisions.

Counterfactual explanation methods interpret the outputs of a machine learning model in the form of "what-if scenarios" without compromising the fidelity-interpretability trade-off. They explain how to obtain a desired prediction from the model by recommending small changes to the input features, aka recourse. We believe an actionable recourse should be created based on sound counterfactual explanations originating from the distribution of the ground-truth data and linked to the domain knowledge. Moreover, it needs to preserve the coherency between changed/unchanged features while satisfying user/domain-specified constraints. This paper introduces CARE, a modular explanation framework that addresses the model- and user-level desiderata in a consecutive and structured manner. We tackle the existing requirements by proposing novel and efficient solutions that are formulated in a multi-objective optimization framework. The designed framework enables including arbitrary requirements and generating counterfactual explanations and actionable recourse by choice. As a model-agnostic approach, CARE generates multiple, diverse explanations for any black-box model in tabular classification and regression settings. Several experiments on standard data sets and black-box models demonstrate the effectiveness of our modular framework and its superior performance compared to the baselines.

Regulators have signalled an interest in adopting explainable AI(XAI) techniques to handle the diverse needs for model governance, operational servicing, and compliance in the financial services industry. In this short overview, we review the recent technical literature in XAI and argue that based on our current understanding of the field, the use of XAI techniques in practice necessitate a highly contextualized approach considering the specific needs of stakeholders for particular business applications.

With the increasing deployment of AI systems in high-stakes decision-making domains such as healthcare, finance and law, the technology's explainability has become an issue of public concern. Explainable AI (XAI) is critical for earning the trust of end-users with regard to the outputs generated by machine learning (ML) algorithms, and the research community in recent years has strived to bring more transparency to the inner workings of AI systems by addressing this "black box" problem. The question of just who is going to open the box has however remained relatively underexplored. In a new paper, a team from Georgia Institute of Technology, Cornell University and IBM Research conducts a mixed-methods study on how people with and without expert knowledge of AI perceive different types of AI explanations. The researchers first conducted a systematic review of related work regarding trust, acceptance, and engagement of autonomous or AI systems, followed by informal interviews with six experts spanning human-computer interactions (HCI), AI, and human-robot interactions (HRI).