Most of the available health care diagnostics that use artificial intelligence (AI) function as black boxes--meaning that results do not include any explanation of why the machine thinks a patient has a certain disease or disorder. While AI technologies are extraordinarily powerful, adoption of these algorithms in health care has been slow because doctors and regulators cannot verify their results. However, a new type of algorithm called "explainable AI" (XAI) can be easily understood by humans. As a result, all signs point to XAI being rapidly adopted across health care, making it likely that providers will actually use the associated diagnostics. With advantages over black box AI, Explainable AI (XAI) is likely to be the dominant algorithm in health care.

This article is coauthored by Joy Rimchala and Shir Meir Lador. Rapid adoption of complex machine learning (ML) models in recent years has brought with it a new challenge for today's companies: how to interpret, understand, and explain the reasoning behind these complex models' predictions. Treating complex ML systems as trustworthy black boxes without sanity checking has led to some disastrous outcomes, as evidenced by recent disclosures of gender and racial biases in GenderShades¹. As ML-assisted predictions integrate more deeply into high-stakes decision-making, such as medical diagnoses, recidivism risk prediction, loan approval processes, etc., knowing the root causes of an ML prediction becomes crucial. If we know that certain model predictions reflect bias and are not aligned with our best knowledge and societal values (such as an equal opportunity policy or outcome equity), we can detect these undesirable ML defects, prevent the deployment of such ML systems, and correct model defects.

As for explainable AI, companies that buy AI-driven products look for explainability at a business level, said Arnab Chakraborty, Global managing director of applied intelligence for the U.S. West Coast region at Accenture. "A lot of our clients have to explain to stakeholders how models work," he said. So, the models need to be transparent. Accenture, a multinational professional services firm headquartered in Dublin, Ireland, has its own toolkit to help explain how AI systems work. The toolkit helps the company lay out different parameters that go into a model and how that model influences different APIs, among other things, Chakraborty said.

Central to the concept of multi-domain operations (MDO) is the utilization of an intelligence, surveillance, and reconnaissance (ISR) network consisting of overlapping systems of remote and autonomous sensors, and human intelligence, distributed among multiple partners. Realising this concept requires advancement in both artificial intelligence (AI) for improved distributed data analytics and intelligence augmentation (IA) for improved human-machine cognition. The contribution of this paper is threefold: (1) we map the coalition situational understanding (CSU) concept to MDO ISR requirements, paying particular attention to the need for assured and explainable AI to allow robust human-machine decision-making where assets are distributed among multiple partners; (2) we present illustrative vignettes for AI and IA in MDO ISR, including human-machine teaming, dense urban terrain analysis, and enhanced asset interoperability; (3) we appraise the state-of-the-art in explainable AI in relation to the vignettes with a focus on human-machine collaboration to achieve more rapid and agile coalition decision-making. The union of these three elements is intended to show the potential value of a CSU approach in the context of MDO ISR, grounded in three distinct use cases, highlighting how the need for explainability in the multi-partner coalition setting is key. Introduction Multi-domain operations (MDO) require the capacity, capability, and endurance to operate across multiple domains -- from dense urban terrain to space and cyberspace -- in contested environments against near-peer adversaries (U.S. Army 2018).

Have you ever wondered how machine learning models work? Or what, exactly, goes on inside these models and whether we can trust them? Well, you're in luck, because I'm going to try to give you a very general overview of what XAI is and why we need it by answering a few common questions. After reading this, you should be able to understand the necessity of XAI and whether you need to start thinking about integrating it with your ML projects/products. Explainable AI (XAI) is a rather new field in machine learning (ML) in which researchers try to develop models that are able to explain the decision-making process behind ML models. XAI has many different research branches but, generally speaking, it either tries to explain the results of complex, black-box ML models or tries to incorporate interpretability into current ML architectures.

What is driving the demand, how incumbents are responding, and how startups are already tackling explainability 2.0 Explainable AI helps a user understand the machine's decision-making process. Instead of discussing methods of explainable AI (e.g., LIME, SHAP, etc.), below are some dimensions to wrap our heads around the concept. What explainable AI means depends on the user, the object being explained, and the underlying data. It is such a broad and rapidly developing field that when discussing explainable AI in-depth, it is good to have a mental framework of how it fits these dimensions. Most examples in this article are products built for business decision makers analyzing tabular data.

Explainability or interpretability of AI is a huge deal these days, especially due to the rise in the number of enterprises depending on the decisions made by machine learning and deep learning. Naturally, stakeholders want a level of transparency for how the algorithms came up with their recommendations. The so-called "black box" of AI is rapidly being questioned. For this reason, I was encouraged to learn of IBM's recent efforts in this area. The company's research arm just launched a new open-source AI toolkit, "AI Explainability 360," consisting of state-of-the-art algorithms that support the interpretability and explainability of machine learning models.

Demi Lovato deactivates her Twitter account after getting backlash for mocking 21 Savage following his ICE arrest. Demi Lovato is apologizing after some characterized her recent trip to Israel, during which she was baptized in the Jordan River, as a political statement. Lovato, 27, traveled to the Middle East after she "accepted a free trip to Israel in exchange for a few [social media] posts." But her trip apparently sparked backlash, as the singer saw the need to explain the reasoning for her experience in a follow-up Instagram Story. "I'm extremely frustrated," she wrote. "No one told me there would be anything wrong with going or that I could possibly be offending anyone.

In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today's ML algorithms are able to achieve excellent performance (at times even exceeding the human level) on an increasing number of complex tasks. Deep learning models are at the forefront of this development. However, due to their nested non-linear structure, these powerful models have been generally considered "black boxes", not providing any information about what exactly makes them arrive at their predictions. Since in many applications, e.g., in the medical domain, such lack of transparency may be not acceptable, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This introductory paper presents recent developments and applications in this field and makes a plea for a wider use of explainable learning algorithms in practice.

Work in Counterfactual Explanations tends to focus on the principle of ``the closest possible world'' that identifies small changes leading to the desired outcome. In this paper we argue that while this approach might initially seem intuitively appealing it exhibits shortcomings not addressed in the current literature. First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals(e.g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports). Secondly, the counterfactuals may not be based on a ``feasible path'' between the current state of the subject and the suggested one, making actionable recourse infeasible (e.g., low-skilled unsuccessful mortgage applicants may be told to double their salary, which may be hard without first increasing their skill level). These two shortcomings may render counterfactual explanations impractical and sometimes outright offensive. To address these two major flaws, first of all, we propose a new line of Counterfactual Explanations research aimed at providing actionable and feasible paths to transform a selected instance into one that meets a certain goal. Secondly, we propose FACE: an algorithmically sound way of uncovering these ``feasible paths'' based on the shortest path distances defined via density-weighted metrics. Our approach generates counterfactuals that are coherent with the underlying data distribution and supported by the ``feasible paths'' of change, which are achievable and can be tailored to the problem at hand.