Machine learning models can identify intricate correlations between enormous numbers of data points.

Google LLC has introduced a new "Explainable AI" service to its cloud platform aimed at making the process by which machine learning models come to their decisions more transparent. The idea is that this will help build greater trust in those models, Google said. That's important because most existing models tend to be rather opaque. It's just not clear how they reach their decisions. Tracy Frey, director of strategy for Google Cloud AI, explained in a blog post today that Explainable AI is intended to improve the interpretability of machine learning models.

Artificial Intelligence (AI) has become an integral part of domains such as security, finance, healthcare, medicine, and criminal justice. Explaining the decisions of AI systems in human terms is a key challenge--due to the high complexity of the model, as well as the potential implications on human interests, rights, and lives . While Explainable AI is an emerging field of research, there is no consensus on the definition, quantification, and formalization of explainability. In fact, the quantification of explainability is an open challenge. In our previous work, we incorporated domain knowledge for better explainability, however, we were unable to quantify the extent of explainability. In this work, we (1) briefly analyze the definitions of explainability from the perspective of different disciplines (e.g., psychology, social science), properties of explanation, explanation methods, and human-friendly explanations; and (2) propose and formulate an approach to quantify the extent of explainability. Our experimental result suggests a reasonable and model-agnostic way to quantify explainability

Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust. Prior works that have focused on paths connecting users and items in a heterogeneous network have several limitations, such as discovering relationships rather than true explanations, or disregarding other users' privacy. In this work, we take a fresh perspective, and present PRINCE: a provider-side mechanism to produce tangible explanations for end-users, where an explanation is defined to be a set of minimal actions performed by the user that, if removed, changes the recommendation to a different item. Given a recommendation, PRINCE uses a polynomial-time optimal algorithm for finding this minimal set of a user's actions from an exponential search space, based on random walks over dynamic graphs. Experiments on two real-world datasets show that PRINCE provides more compact explanations than intuitive baselines, and insights from a crowdsourced user-study demonstrate the viability of such action-based explanations. We thus posit that PRINCE produces scrutable, actionable, and concise explanations, owing to its use of counterfactual evidence, a user's own actions, and minimal sets, respectively.

Health care innovators are developing artificial intelligence algorithms called Explainable AI (XAI) that actually reveal the logic behind their diagnoses. Because their results can be verified, doctors and regulators will be more likely to adopt these algorithms than traditional "black box" AI. However, the transparency that makes these algorithms valuable to practitioners also makes the technology trickier to protect as intellectual property. With some legal creativity, there are multiple paths to patent protection for XAI-based diagnostics. The very nature of XAI algorithms prevents them from being kept secret, and the law governing patents for diagnostic algorithms is nearly undecipherable.

The TextGraphs-13 Shared Task on Explanation Regeneration asked participants to develop methods to reconstruct gold explanations for elementary science questions. Red Dragon AI's entries used the language of the questions and explanation text directly, rather than a constructing a separate graph-like representation. Our leaderboard submission placed us 3rd in the competition, but we present here three methods of increasing sophistication, each of which scored successively higher on the test set after the competition close.

Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance. Enriching the trust between these systems and people will accelerate this integration process. Many medical and retail banking/finance applications use state-of-the-art machine learning techniques to predict certain aspects of new instances. Tree ensembles, like random forests, are widely acceptable solutions on these tasks, while at the same time they are avoided due to their black-box uninterpretable nature, creating an unreasonable paradox. In this paper, we provide a sequence of actions for shedding light on the predictions of the misjudged family of tree ensemble algorithms. Using classic unsupervised learning techniques and an enhanced similarity metric, to wander among transparent trees inside a forest following breadcrumbs, the interpretable essence of tree ensembles arises. An explanation provided by these systems using our approach, which we call "LionForests", can be a simple, comprehensive rule.

Machine learning algorithms are starting to exceed human performance in many narrow and specific domains, such as image recognition and certain types of medical diagnoses. We increasingly rely on machine learning algorithms to make decisions on a wide range of topics, from what we collectively spend billions of hours watching to who gets the job. But machine learning algorithms cannot explain the decisions they make. How can we justify putting these systems in charge of decisions that affect people's lives if we don't understand how they're arriving at those decisions? This desire to get more than raw numbers from machine learning algorithms has led to a renewed focus on explainable AI: algorithms that can make a decision or take an action, and tell you the reasons behind it.

We are living in an era that is showing massive growth in data and computing power. We have seen a lot of progress in machine learning and deep learning, but there is an ever-growing need for more intelligent, more explainable AI. Most people's perception of artificial intelligence boils down to either science fiction, or what we call conventional AI. The foundations of conventional AI are numerical techniques like data analytics, including statistical analysis, modeling, and machine learning. This has been the primary approach to AI over the past few decades with significant success by numerous companies in many industries.

Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the pred iction and behavior of machine learning models. An instance of expl anations are counterfactual explanations which provide an intuitive an d useful explanations of machine learning models. In this survey we review model-specific methods for efficientl y computing counterfactual explanations of many different machine learning models and propose methods for models that have not been considered in l iterature so far.