It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency. We consider two variants of decision making, where "good" decisions amount to alternatives (i) meeting "most" goals, and (ii) meeting "most preferred" goals. We then define, for each variant and notion of "goodness" (corresponding to a number of existing notions in the literature), explanations in two formats, for justifying the selection of an alternative to audiences with differing needs and competences: lean explanations, in terms of goals satisfied and, for some notions of "goodness", alternative decisions, and argumentative explanations, reflecting the decision process leading to the selection, while corresponding to the lean explanations. To define argumentative explanations, we use assumption-based argumentation (ABA), a well-known form of structured argumentation. Specifically, we define ABA frameworks such that "good" decisions are admissible ABA arguments and draw argumentative explanations from dispute trees sanctioning this admissibility. Finally, we instantiate our overall framework for explainable decision-making to accommodate connections between goals and decisions in terms of decision graphs incorporating defeasible and non-defeasible information.

The benefits of analytics are well-documented. Analytics has helped organisations transform retail experiences, map pathways for trains and trucks, discover extraterrestrial life, and even predict diseases. However, over the past few years, organisations across the globe have wrestled with just how much human error has permeated their analytics attempts, often ending with disastrous results. From crashing spacecraft to sinking ships, transferring billions of dollars to unintended recipients, and causing deaths due to overdose of medication, human error in data analysis has far-reaching ramifications for organisations. The reason for human error in data analysis could be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or the all-too-common biases in interpreting data. However, what's common among these errors is that they are related to humans reading, processing, analysing, and interpreting data.

Testing with quiz questions has proven to be an effective way to assess and improve the educational process. However, manually creating quizzes is tedious and time-consuming. To address this challenge, we present Leaf, a system for generating multiple-choice questions from factual text. In addition to being very well suited for the classroom, Leaf could also be used in an industrial setting, e.g., to facilitate onboarding and knowledge sharing, or as a component of chatbots, question answering systems, or Massive Open Online Courses (MOOCs). The code and the demo are available on GitHub.

Controllable text generation concerns two fundamental tasks of wide applications, namely generating text of given attributes (i.e., attribute-conditional generation), and minimally editing existing text to possess desired attributes (i.e., text attribute transfer). Extensive prior work has largely studied the two problems separately, and developed different conditional models which, however, are prone to producing biased text (e.g., various gender stereotypes). This paper proposes to formulate controllable text generation from a principled causal perspective which models the two tasks with a unified framework. A direct advantage of the causal formulation is the use of rich causality tools to mitigate generation biases and improve control. We treat the two tasks as interventional and counterfactual causal inference based on a structural causal model, respectively. We then apply the framework to the challenging practical setting where confounding factors (that induce spurious correlations) are observable only on a small fraction of data. Experiments show significant superiority of the causal approach over previous conditional models for improved control accuracy and reduced bias.

Does the recent flurry of headlines about Facebook and the negative outcomes produced by its algorithms have you worried about the future and the implications of widespread AI usage? It's a rational response to have during an alarming news cycle. However, this situation shouldn't be interpreted as a death knell for the use of AI in human communications. It's more of a cautionary example of the disastrous consequences that can occur as a result of not using AI in a responsible way. Read on to learn more about ethical technology, data quality, and the significance of human-in-the-loop AI.

As artificial intelligence plays an increasingly important role in the R&D process, the premise that invention is a uniquely human characteristic is being challenged. Patent offices and courts around the world have recently been grappling with the question of whether an AI system can be the inventor of a patent. This has been prompted by Dr. Stephen Thaler's applications to designate his AI system (known as'DABUS') as the inventor of patents filed in multiple jurisdictions. Most recently, the appeal board of the European Patent Office (EPO) refused Dr. Thaler's patent applications because there was no valid inventor. Dr. Thaler, as part of the Artificial Inventor Project, is pursuing parallel patent applications across over fifteen jurisdictions which designate his AI system, DABUS, as the inventor.

Gibson Dunn lawyers examine a number of the most significant developments from 2021 affecting companies as they navigate the evolving AI and autonomous systems landscape.

Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this issue, many KG completion methods are proposed. Among them, open KG completion methods leverage the Web to find missing facts. However, noisy data collected from diverse sources may damage the completion accuracy. In this paper, we propose a new trustworthy method that exploits facts for a KG based on multi-sourced noisy data and existing facts in the KG. Specifically, we introduce a graph neural network with a holistic scoring function to judge the plausibility of facts with various value types. We design value alignment networks to resolve the heterogeneity between values and map them to entities even outside the KG. Furthermore, we present a truth inference model that incorporates data source qualities into the fact scoring function, and design a semi-supervised learning way to infer the truths from heterogeneous values. We conduct extensive experiments to compare our method with the state-of-the-arts. The results show that our method achieves superior accuracy not only in completing missing facts but also in discovering new facts.

Even after more than a hundred years after its introduction, histology remains the gold standard in tumor diagnosis and prognosis. Anatomic pathologists evaluate histology to stratify cancer patients into different groups depending on their tumor genotypes and phenotypes, and their clinical outcome [1,2]. However, human evaluation of histological slides is subjective and not repeatable [3]. Furthermore, histological assessment is a time-consuming process that requires highly trained professionals. With significant technological advances in the last decade, techniques such as whole slide imaging (WSI) and deep learning (DL) are now widely available.

From better healthcare access to improved food security, machine learning could tackle a wide range of challenges in developing countries. In 2020, a study published in Nature showed that Google's machine learning artificial intelligence programme, DeepMind AI, outperformed radiologists in detecting breast cancer. After being trained on thousands of mammograms, the system was able to accurately identify 89% of breast cancer cases, compared to radiologists' 74%. Just imagine what a difference the deployment of such a system could make in sub-Saharan Africa, where there are 0.2 doctors per 1000 people, according to the World Bank. Marilyn Moodley, Country Leader for South Africa and WECA (West, East, Central Africa) at SoftwareONE, says machine learning can help with some of the region's most pervasive problems, from reducing poverty and improving education to delivering healthcare and addressing sustainability challenges such as food demand.