Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how these challenges impact the performance in specific downstream decision-making tasks is limited. Addressing this gap, we make use of a comprehensive real-world dataset of jobseekers to illustrate how the performance of a single CATE model can vary significantly across different decision-making scenarios and highlight the differential influence of challenges such as distribution shifts on predictions and allocations.

We describe a joint probabilistic model for modeling the contents and inter-connectivity of document collections such as sets of web pages or research paper archives. The model is based on a probabilistic factor decomposition and allows identifying principal topics of the collection as well as authoritative documents within those topics. Furthermore, the relationships between topics is mapped out in order to build a predictive model of link content. Among the many applications of this approach are information retrieval and search, topic identification, query disambigua(cid:173) tion, focused web crawling, web authoring, and bibliometric analysis.

Mila – Quebec Artificial Intelligence Institute and the United Nations Educational, Scientific and Cultural Organization (UNESCO) have joined forces on a book entitled Missing links in AI governance. Focussed on the need for better governance of AI, the book comprises 18 chapters written by academics, civil society representatives, innovators and policy makers. The book explores themes such as the influence of AI on indigenous and LGBTI communities, the necessary inclusion of all countries in global governance, and the use of AI to support innovation for socially beneficial purposes. It maps out possible solutions to foster an AI development that is ethical, inclusive, and respectful of human rights. The authors also warn against the use of AI in potentially harmful contexts like autonomous weapons or the manipulation of digital content for social destabilization, deplore the increasing centralization of decision-making power in the development of AI systems and biases embedded in them, and the lack of transparency and accountability in the industry.

BRUSSELS – The European Commission's strategy for artificial intelligence focuses on the need to establish "trust" and "excellence." Recently proposed AI regulation, the Commission argues, will create trust in this new technology by addressing its risks, while excellence will follow from EU member states investing and innovating. With these two factors accounted for, Europe's AI uptake supposedly will accelerate. Unfortunately, protecting EU citizens' fundamental rights, which should be the AI regulation's core objective, appears to be a secondary consideration; and protections for workers' rights don't seem to have been considered at all. AI is a flagship component of Europe's digital agenda, and the Commission's legislative package is fundamental to the proposed single market for data.

Data is certainly the fuel for AI. Yet there is a source of valuable data that usually does not garner much attention. It is from mainframe systems. They hold enormous amounts of data--which go back decades--for mission critical operations. But then again, there are difficulties working with mainframes and AI.

It's tempting to think of the mind as a layer that sits on top of more primitive cognitive structures. We experience ourselves as conscious beings, after all, in a way that feels different to the rhythm of our heartbeat or the rumblings of our stomach. If the operations of the brain can be separated out and stratified, then perhaps we can construct something akin to just the top layer, and achieve human-like artificial intelligence (AI) while bypassing the messy flesh that characterises organic life. I understand the appeal of this view, because I co-founded SwiftKey, a predictive-language software company that was bought by Microsoft. Our goal is to emulate the remarkable processes by which human beings can understand and manipulate language.

The good news is that the issue of B2B late payments is entirely remediable. While organisations such as Mastercard and Visa are beginning to address the problem and infrastructure like Faster Payments are steps in the right direction, these solutions are focussed on accelerating the speed at which payment transactions are made. However, while solutions such as these are undoubtedly a welcome step in the battle against late payments, to truly overcome the issue a holistic solution that streamlines all elements of the payments process is needed. Tackling invoice approval in the long chain of steps in the B2B payments process is essential to unlocking instantaneous payment, akin to those that are the norm in the B2C world.

The machine learning branch of AI continues to make inroads to corporate banking use cases, and this referenced piece, which appears in The Financial Times, describes another one of those. The blog was written by the CEO of Previse, a 2016 startup based in London, which utilizes data contained in supplier invoices to make smart payment decisions using machine learning algorithms. We have covered the use of AI in corporate banking in several member reports, the latest having to do with receivables management, obviously a related use case. One can argue (as we have) that the cash cycle is all connected anyway, and digital process transition is the initiation point. Actually, comments in a previous posting suggest that cleaning your room is more fundamental, but regardless, the'missing link,' as mentioned in this title, is surely one of the tools in the shed to improve liquidity: 'At present, payments in the B2B sphere are hampered by archaic processes.

Machine learning models come in many forms and cater to many use cases, and the proliferation of machine learning over the past decade has happened in three stages. First, there were breakthroughs in deep learning and the complementary emergence of cloud computing and big data. The second stage has been the creation and standardization of tools and frameworks for machine learning development. And most recently the deployment and in-life support of machine learning has come to the fore. But is this enough, or is there something missing?

While there have been several key developments in machine translation (MT) in recent years, MT has not yet reached the level where businesses might be confident to allow it to proceed unchecked by humans. There is a paradox insofar that we want to allow artificial intelligence (AI) and automation to take on more and more tasks to relieve pressure on the human workforce, but, in turn, this creates more work for humans in terms of supervising their digital colleagues. We need look no further than the restaurant in China called "Translate Server Error" or Hillary Clinton's gift to the Russian foreign minister that was inscribed with a message that was supposed to say "reset" in Russian but actually showed the word "overcharge." AI still commits fundamental errors that are embarrassing at best, and at worst, they can convey offensive and/or completely unintended meanings. This is where the importance of quality estimation comes to the fore. A good definition of quality estimation comes from eBay, an enthusiastic user of QE: "A method used to automatically provide a quality indication for machine translation output without depending on human reference translations.