As artificial intelligence (AI) is integrated into various services and systems in society, many companies and organizations have proposed AI principles, policies, and made the related commitments. Conversely, some have proposed the need for independent audits, arguing that the voluntary principles adopted by the developers and providers of AI services and systems insufficiently address risk. This policy recommendation summarizes the issues related to the auditing of AI services and systems and presents three recommendations for promoting AI auditing that contribute to sound AI governance. Recommendation1.Development of institutional design for AI audits. Recommendation2.Training human resources for AI audits. Recommendation3. Updating AI audits in accordance with technological progress. In this policy recommendation, AI is assumed to be that which recognizes and predicts data with the last chapter outlining how generative AI should be audited.

Attention has become a common ingredient in deep learning architectures. It adds a dynamical selection of information on top of the static selection of information supported by weights. In the same way, we can imagine a higher-order informational filter built on top of attention: an Attention Schema (AS), namely, a descriptive and predictive model of attention. In cognitive neuroscience, Attention Schema Theory (AST) supports this idea of distinguishing attention from AS. A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents. As such, multi-agent reinforcement learning would be an ideal setting to experimentally test the validity of AST. We explore different ways in which attention and AS interact with each other. Our preliminary results indicate that agents that implement the AS as a recurrent internal control achieve the best performance. In general, these exploratory experiments suggest that equipping artificial agents with a model of attention can enhance their social intelligence.

Late last year, the European Commission's Expert Group on Liability and New Technologies – New Technologies Formation (NTF) released a report on Liability for Artificial Intelligence. The report focuses on liability regimes across European Union (EU) member states and offers high-level recommendations on how those liability regimes can be adapted to meet challenges posed by artificial intelligence (AI) and other digital technologies. Insights from this report may inform legislative and regulatory changes in the EU and elsewhere, including in Canada. Here's what you need to know. The NTF first convened in June 2018.

In the digital economy, change is rapid and often unexpected. Technological and business-model innovations are disrupting market dynamics, while economic and geopolitical uncertainty injects a whole new level of volatility into the business environment. Companies that fail to address both challenges risk the erosion of their competitive advantage. The Hackett Group's research indicates that finance executives are looking to smart automation to tighten controls while at the same time enhancing their agility. With tools like robotic process automation (RPA) and AI-enabled analytics, they are beginning to automate compliance monitoring and remediation activities.

A company was charged for an unauthorized $30,000 squirrel hunting trip. You read that right – squirrel hunting. A logistics company unknowingly paid a supplier that didn't exist to maintain infrastructure that didn't exist. The AP team at a technology company received an invoice for goods never purchased. An executive bought IT equipment with company funds and sold the hardware privately for personal gain.

But what does it mean, and more importantly, what might it mean for you in the future? First, let's get on the same page in case you are one of the many who looks up most acronyms these days because acronyms appear and disappear so quickly. "A branch of computer science dealing with the simulation of intelligent behavior in computers (…machine to imitate intelligent human behavior)" "A field of computer science that uses statistical techniques to give computer systems the ability to'learn' (e.g., progressively improve performance of a specific task) with data, without being explicitly programmed" Another related term is "expert system," closely related to both AI and ML, which uses a knowledge base of expert information plus an inference engine to make decisions and solve complex problems. The Merriam-Webster definition of AI (specifically, "…machine to imitate intelligent human behavior") did put a smile on my face as I contemplated whether a computer imitating stupid human behavior would qualify as artificial intelligence, or do we also need a definition for artificial stupidity? You may think I'm kidding, but it's only a slight exaggeration if you realize that some of the current buzz around Google Duplex and Assistant emphasizes a computer agent that can imitate the voice, pauses, and false starts inherent in human communication.

In a recent Deloitte Center for Controllership poll of more than 1,700 finance, accounting and other professionals, 52.8 percent say their organizations plan digital controllership improvements--leveraging process automation, analytics and other technologies for financial and accounting processes--in the year ahead. Using finance and accounting robotic process automation (RPA) to increase efficiency and internal controls is the top priority for such efforts (34.7 percent). "Finance and accounting process automation can really run the gamut. Simpler, enhanced finance automation can address common, industry agnostic accounting issues. RPA can build momentum by performing repetitive, manual financial and accounting processes. And, cognitive computing can be configured to adapt to non-routine, industry and organizationally specific needs," said Kyle Cheney, Deloitte Risk and Financial Advisory partner, Deloitte & Touche LLP.

There is currently much fear and hype around Artificial intelligence (AI) and its impact on accountants. In Gartner's Hype Cycle of Artificial Intelligence, the majority of AI applications are climbing and cresting the Peak of Inflated Expectations--meaning that expectations are high and many technologies are already failing to meet those expectations. But this doesn't mean that AI is going to go away. It means that we're starting to push through the hype and figure out realistic applications for AI--some of which will be useful to accountants and many of which will be leveraged by the organizations we serve. Part of the challenge with an emerging technology is that there is often an unclear definition of what technology is, and what it is not.

An important problem faced by auditors is gauging how much reliance can be placed on the accounting systems that process millions of transactions to produce the numbers summarized in a company's financial statements. Accounting sys-ems contain internal controls, procedures designed to detect and correct errors and irregularities that can occur in the processing of transactions. In a complex accounting system, it can be an extremely difficult task for the auditor to anticipate the possible errors that can occur and evaluate the effectiveness of the controls at detecting them. An accurate analysis must take into account the unique features of each company's business processes. To cope with this complexity and variability, the COMET system applies a model-based reasoning approach to the analysis of accounting systems and their controls. An auditor uses COMET to create a hierarchical flowchart model that describes the intended processing of business transactions by an accounting system and the operation of its controls. COMET uses the constructed model to automatically analyze the effectiveness of the controls in detecting potential errors. Price Waterhouse auditors have used COMET on a variety of real audits in several countries around the world.