We define several canonical problems related to contrastive explanations, each answering a question of the form ''Why P but not Q?''. The problems compute causes for both P and Q, explicitly comparing their differences. We investigate the basic properties of our definitions in the setting of propositional logic. We show, inter alia, that our framework captures a cardinality-minimal version of existing contrastive explanations in the literature. Furthermore, we provide an extensive analysis of the computational complexities of the problems. We also implement the problems for CNF-formulas using answer set programming and present several examples demonstrating how they work in practice.

We conceptualize explainability in terms of logic and formula size, giving a number of related definitions of explainability in a very general setting. Our main interest is the so-called special explanation problem which aims to explain the truth value of an input formula in an input model. The explanation is a formula of minimal size that (1) agrees with the input formula on the input model and (2) transmits the involved truth value to the input formula globally, i.e., on every model. As an important example case, we study propositional logic in this setting and show that the special explainability problem is complete for the second level of the polynomial hierarchy. We also provide an implementation of this problem in answer set programming and investigate its capacity in relation to explaining answers to the n-queens and dominating set problems.

Artificial intelligence, commonly referred to as AI, represents both a risk and a benefit to the security of society, according to Bruce Schneier, security technologist, researcher, and lecturer at Harvard Kennedy School. Schneier made his remarks about the risks of AI in an afternoon keynote session at the 2021 RSA Conference on May 17. Hacking for Schneier isn't an action that is evil by definition; rather, it's about subverting a system or a set of rules in a way that is unanticipated or unwanted by a system's designers. "All systems of rules can be hacked," Schneier said. "Even the best-thought-out sets of rules will be incomplete or inconsistent, you'll have ambiguities and things that designers haven't thought of, and as long as there are people who want to subvert the goals in a system, there will be hacks." Schneier highlighted a key challenge with hacking that is conducted by some form of AI: it might be difficult to detect.

If you don't have enough to worry about already, consider a world where AIs are hackers. Hacking is as old as humanity. We are creative problem solvers. We exploit loopholes, manipulate systems, and strive for more influence, power, and wealth. To date, hacking has exclusively been a human activity.

The so-called "black-box" aspect of AI, usually referred to as the explainability problem, or X(AI) for short, arose slowly over the past few years. Still, with the rapid development in AI, it is now considered a significant problem. How can you trust a model if you cannot understand how it reaches its conclusions? For commercial benefits, for ethics concerns or regulatory considerations, X)(AI) is essential if users understand, appropriately trust, and effectively manage AI results. In researching this topic, I was surprised to find almost 400 papers on the subject.

In his blog post on artificial intelligence (AI), GovTech Graduate Jonathan Manning draws on the New Zealand Law Foundation: Government use of artificial intelligence in New Zealand (the NZFL report) to discuss the role and effectiveness of explanation tools. As algorithms and AI become ubiquitous we all become'data subjects' to organisations such as governments and businesses. In response, regulations such as the EU's General Data Protection Regulation are beginning to emerge. The New Zealand government is currently exploring how governments, business and society can work together to meet the challenge of regulating AI. A part of this challenge is ensuring when things like algorithmic harm arise, we can explain what happened and why so that mistakes can be fixed and not repeated or obscured.

Artificial intelligence (AI) continues to draw a lot of attention as companies and technology vendors look at how machine learning could improve supply chain operations. In particular demand planning, understood here as the process of developing forecasts that will drive operational supply chain decisions, is being touted as the next potential field for innovation. Technology giants like Amazon and Microsoft have announced AI tools for improving demand planning, and several consulting companies are promoting their skills to bring AI to companies' demand planning processes. In fact, a recent survey by the Institute of Business Forecasting and Planning (IBF) identified AI as the technology that will have the largest impact on demand planning in the next seven years.1 It's not hard to see the fit between AI and demand planning. Demand planning involves lots of number crunching and data analytics, and it is repeated cycle after cycle.

Artificial intelligence has the potential to dramatically re-arrange our relationship with technology, hearkening a new era of human productivity, leisure, and wealth. But none of that good stuff is likely to happen unless AI practitioners can deliver on one simple request: Explain to us how the algorithms got their answers. Businesses have never relied more heavily on machine learning algorithms to guide decision-making than they do right now. Buoyed by the rise of deep learning models that can act upon huge masses of data, the benefits of using machine learning algorithms to automate a host of decisions is simply too great to pass up. Indeed, some executives see it as a matter of business survival.