With AI becoming more prevalent in day-to-day operations, the word "consultant" may soon take on a new meaning. That's because these intelligent systems will be making decisions and providing insights that would have previously required human consultants. The implications for the legal field are even more significant. The potential for AI to replace many current lawyers' tasks and perform them automatically is real – and it's only a matter of time before it becomes the law of the land. These technologies can intuitively capture information in ways humans never could, as well as predict who might need their services down the road – all without charging any higher rates than their human counterparts.

When a professional sector is confronted with new technology, concerns emerge about how the technology will affect everyday operations and the careers of people who select that field. And the legal profession and attorneys are no exception. Artificial intelligence (AI) is now transforming the legal profession in a variety of ways, but in the majority of situations, it augments what people do and frees them up to focus on higher-level activities like counseling clients, negotiating deals, and presenting in court. You can find several essays written on AI. Artificial intelligence is the phrase used when machines are able to accomplish activities that would normally need human intelligence.

Most likely it's a clumsy attempt to throw disinformation shade at rivals.) Returning to the regulation point, an EU proposal -- the Digital Services Act -- is set to introduce some transparency requirements on large digital platforms, as part of a wider package of accountability measures. And asked about this Geurkink described the DSA as "a promising avenue for greater transparency". But she suggested the legislation needs to go further to tackle recommender systems like the YouTube AI. "I think that transparency around recommender systems specifically and also people having control over the input of their own data and then the output of recommendations is really important -- and is a place where the DSA is currently a bit sparse, so I think that's where we really need to dig in," she told us. One idea she voiced support for is having a "data access framework" baked into the law -- to enable vetted researchers to get more of the information they need to study powerful AI technologies -- i.e. rather than the law trying to come up with "a laundry list of all of the different pieces of transparency and information that should be applicable", as she put it.

TBILISI, July 5 (Thomson Reuters Foundation) - (Editor's note: contains offensive language and terms of racial abuse) From scanning residents' faces to let them into their building to spotting police suspects in a crowd, the rise of facial recognition is accompanied by a growing chorus of concern about unethical uses of the technology. A report published on Monday by U.S.-based researchers showing that Russian facial recognition companies have built tools to detect a person's race has raised fears among digital rights groups, who describe the technology as "purpose-made for discrimination." Developer guides and code examples unearthed by video surveillance research firm IPVM show software advertised by four of Russia's biggest facial analytics firms can use artificial intelligence (AI) to classify faces based on their perceived ethnicity or race. There is no indication yet that Russian police have targeted minorities using the software developed by the firms - AxxonSoft, Tevian, VisionLabs and NtechLab - whose products are sold to authorities and businesses in the country and abroad. But Moscow-based AxxonSoft said the Thomson Reuters Foundation's enquiry prompted it to disable its ethnicity analytics feature, saying in an emailed response it was not interested "in promoting any technologies that could be a basis for ethnic segregation".

With the growing awareness to fairness in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious interest in notions of fair data representations. The goal of such representations is that a model trained on data under the representation (e.g., a classifier) will be guaranteed to respect some fairness constraints. Such representations are useful when they can be fixed for training models on various different tasks and also when they serve as data filtering between the raw data (known to the representation designer) and potentially malicious agents that use the data under the representation to learn predictive models and make decisions. A long list of recent research papers strive to provide tools for achieving these goals. However, we prove that this is basically a futile effort. Roughly stated, we prove that no representation can guarantee the fairness of classifiers for different tasks trained using it; even the basic goal of achieving label-independent Demographic Parity fairness fails once the marginal data distribution shifts. More refined notions of fairness, like Odds Equality, cannot be guaranteed by a representation that does not take into account the task specific labeling rule with respect to which such fairness will be evaluated (even if the marginal data distribution is known a priory). Furthermore, except for trivial cases, no representation can guarantee Odds Equality fairness for any two different tasks, while allowing accurate label predictions for both. While some of our conclusions are intuitive, we formulate (and prove) crisp statements of such impossibilities, often contrasting impressions conveyed by many recent works on fair representations.

In this paper we discuss contrastive explanations for formal argumentation - the question why a certain argument (the fact) can be accepted, whilst another argument (the foil) cannot be accepted under various extension-based semantics. The recent work on explanations for argumentation-based conclusions has mostly focused on providing minimal explanations for the (non-)acceptance of arguments. What is still lacking, however, is a proper argumentation-based interpretation of contrastive explanations. We show under which conditions contrastive explanations in abstract and structured argumentation are meaningful, and how argumentation allows us to make implicit foils explicit.

Today, AI is getting adopted in everyday life, and now it is more important to ensure that decisions that have been taken using AI are not reflecting discriminatory behaviour towards a set of populations. It is important to take fairness into consideration while consuming the output from AI. Discrimination towards a sub-population can be created unintentionally and unknowingly, but during the deployment of any AI solution, a check on bias is imperative. Example 1: Machine learned human biases that result in a model with racial disparity. In the United States, amongst the population sent to lock-up include blacks in disproportionate number. For centuries, the key decisions in the legal process are governed by human instincts and biases.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Emerging technologies meet both advocates and resistance as users weigh the potential benefits with the potential risks. To successfully implement new technologies, we must start small, in a few simplified forms, fitting a small number of use cases to establish proof of concept before scaling usage. Artificial intelligence is no exception, but with the added challenge of intruding into the cognitive sphere, which has always been the prerogative of humans.

Artificial Intelligence is proving to be a huge disruptor. The rising popularity of ChatBots is one of the greatest examples of how technology is changing the way we do business. Various online shopping websites and services have successfully integrated ChatBots into their marketing strategy to improve customer service. While Chatbots do enhance the customer experience, they can also be used to add efficiency to your internal firm operations. Here are some ways in which ChatBots can upgrade the partner, attorney and associate experience by acting as a personal assistant and virtual concierge for Client and Matter related information. KLoBot can help your firm extract deeper data intelligence via a simplified web chat interface that can be surfaced right within your SharePoint Intranet portal or any web channel such as public facing website or extranet sites.

For most people living through the latest pandemic, the urgent questions are often "when questions." When indoor establishments should lift capacity limits. When mask requirements should be dropped. When family, friends, and strangers should reconnect across household lines. For millions of other people, the question is more like whether. Whether there ever will be an opening. Whether they will be welcome participants. Whether the reengineered social relationships for post-pandemic life will include them. Because when the physical world is utterly open, the social world can be closed to these people.