NEW YORK--(BUSINESS WIRE)--Axiom, the leading alternative legal services provider, today announced the market launch of AxiomAI – a program that leverages Artificial Intelligence (AI) to improve the efficiency and quality of contracts work. Over the last 4 years, Axiom has collaborated with, and conducted numerous tests, of the leading AI contract data extraction providers and has leveraged those learnings on a number of client engagements. Axiom will initially embed Kira into its leading M&A Diligence and Integration offering to source relevant clauses from those contracts, thus enabling more efficient interpretation and structuring of contract data, which underlies the insights Axiom provides to its clients' legal and business users. The goal is to move from finding clauses to interpreting clauses, which promises to dramatically improve the speed of contract analysis, enable more powerful insights, and ultimately deliver the capability of creating new bodies of contracts faster, and with higher quality.

By now we have all heard the term "AI" or artificial intelligence many times over, whether reading the latest business headlines, attending an industry conference or simply scanning your social news feeds on LinkedIn or Twitter. While this latest buzzword seems to be nothing more than a distraction to some, there are, in fact, real and practical applications of AI happening within legal departments. In fact, AI was the focus of several sessions and countless conversations during the Corporate Legal Operations Consortium (CLOC) Institute this past May, which illustrates its ascendance within the legal ops community. Certainly, AI is not a stand-alone answer for managing legal spend, or any other aspect of legal department operations. As I stated in an earlier blog post, the truth is that AI has delivered a clear incremental improvement over an old process.

Recently Lofty Labs was engaged by a law firm. Now, that's not the type of business we generally target as clients for a data analysis consultancy. It turned out they had a data problem, though. When two large companies sue each other, a lot of historical communication records get exchanged between the two sides through the facilitation of the court in a process known within the field as "discovery". This is the process you probably think of from the movies, where lawyers can be seen carting dollies full archival boxes overflowing with paper documents.

Algorithms learned from data are increasingly used for deciding many aspects in our life: from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence that decision making by inappropriately trained algorithms may unintentionally discriminate people. For example, in automated matching of candidate CVs with job descriptions, algorithms may capture and propagate ethnicity related biases. Several repairs for selected algorithms have already been proposed, but the underlying mechanisms how such discrimination happens from the computational perspective are not yet scientifically understood. We need to develop theoretical understanding how algorithms may become discriminatory, and establish fundamental machine learning principles for prevention. We need to analyze machine learning process as a whole to systematically explain the roots of discrimination occurrence, which will allow to devise global machine learning optimization criteria for guaranteed prevention, as opposed to pushing empirical constraints into existing algorithms case-by-case. As a result, the state-of-the-art will advance from heuristic repairing, to proactive and theoretically supported prevention. This is needed not only because law requires to protect vulnerable people. Penetration of big data initiatives will only increase, and computer science needs to provide solid explanations and accountability to the public, before public concerns lead to unnecessarily restrictive regulations against machine learning.

The sea and ocean environment has long been explored using some of the most sophisticated technology tools. Today's technologies make it child's play to explore natural environments under the sea. The American Goddard Space Flight Center, which belongs to NASA, relies on machine learning to track microscopic algal growth in oceans. The microalgae, which float on the water's surface, are largely responsible for producing oxygen, an element essential for supporting life. Many underwater observations rely on advanced detection technologies.

Opaque and potentially biased mathematical models are remaking our lives--and neither the companies responsible for developing them nor the government is interested in addressing the problem. This week a group of researchers, together with the American Civil Liberties Union, launched an effort to identify and highlight algorithmic bias. The AI Now initiative was announced at an event held at MIT to discuss what many experts see as a growing challenge. Algorithmic bias is shaping up to be a major societal issue at a critical moment in the evolution of machine learning and AI. If the bias lurking inside the algorithms that make ever-more-important decisions goes unrecognized and unchecked, it could have serious negative consequences, especially for poorer communities and minorities.

Welcome to the new week. Over the weekend, we talked about our new robot friends, HTC's return to flagship smartphone form and fines for walking while texting -- if you're in Honolulu. They only want your love (and occasionally some anonymized metadata.) Who needs friends when robots are this sociable? From social robots to military tools, where do you draw the line? How do you add personality to a robot, and why would you?

From the Australian government's new "data-driven profiling" trial for drug testing welfare recipients, to US law enforcement's use of facial recognition technology and the deployment of proprietary software in sentencing in many US courts … almost by stealth and with remarkably little outcry, technology is transforming the way we are policed, categorized as citizens and, perhaps one day soon, governed. We are only in the earliest stages of so-called algorithmic regulation -- intelligent machines deploying big data, machine learning and artificial intelligence (AI) to regulate human behaviour and enforce laws -- but it already has profound implications for the relationship between private citizens and the state. Furthermore, the rise of such technologies is occurring at precisely the moment when faith in governments across much of the Western world has plummeted to an all-time low. Voters across much of the developed world increasingly perceive establishment politicians and those who surround them to be out-of touch bubble-dwellers and are registering their discontent at the ballot box. In this volatile political climate, there's a growing feeling that technology can provide an alternative solution.

Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns an active learning strategy may be exploring. This work expands on the Local Interpretable Model-agnostic Explanations framework (LIME) to provide explanations for active learning recommendations. We demonstrate how LIME can be used to generate locally faithful explanations for an active learning strategy, and how these explanations can be used to understand how different models and datasets explore a problem space over time. In order to quantify the per-subgroup differences in how an active learning strategy queries spatial regions, we introduce a notion of uncertainty bias (based on disparate impact) to measure the discrepancy in the confidence for a model's predictions between one subgroup and another. Using the uncertainty bias measure, we show that our query explanations accurately reflect the subgroup focus of the active learning queries, allowing for an interpretable explanation of what is being learned as points with similar sources of uncertainty have their uncertainty bias resolved. We demonstrate that this technique can be applied to track uncertainty bias over user-defined clusters or automatically generated clusters based on the source of uncertainty.

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to retrieve (by derivation) multiple statements (sometimes assumed) or results from the literature.