It can be difficult for those outside the trademark industry to comprehend the archaic nature of many of the processes. Those used to the magic of Google at their fingertips to obtain information are often taken aback by a proposed turnaround time of a clearance search that numbers in days and sometimes weeks and yet still carry a hefty price tag. They are often shocked to discover that'watching your marks' often means not only paying for a service to deliver reports but that it means wading through pages upon pages of results with no guarantee that there is even a single result in there worthy of your attention. Even in private practice law firms, the trademark workload is notoriously heavy from an administrative perspective.

Corey Booker and some of his Senate colleagues would like to introduce a new area of government regulation in the tech industry. We need to be keeping a closer eye on the development of Artificial Intelligence, but not because of the coming robot revolution. The problem, you see, is that the computer algorithms are (wait for it)… racist. And that justifies some sort of government oversight of the tech sector beyond what we already have in place today. Congress is starting to show interest in prying open the "black box" of tech companies' artificial intelligence with oversight that parallels how the federal government checks under car hoods and audits banks.

It's well established that algorithms can exhibit bias, however inadvertently, and a trio of US politicians believe they can do something about it. Senators Cory Booker, Yvette Clarke and Ron Wyden have introduced an Algorithmic Accountability Act that would require larger companies to test their algorithms and fix anything "inaccurate, unfair, biased or discriminatory." The move would also ask them to study how their systems protect personal data,and would let the FTC create regulations mandating impact studies for "highly sensitive" automated systems. The bill would only apply to companies that either make more than $50 million per year or have data for at least one million people or devices. Small businesses would theoretically be safe.

The new world of marketing is personalized, contextualized, and dynamic. Increasingly, this world is orchestrated not by outside parties but by chief marketing officers partnering with their technology organizations to bring control of the human experience back in-house. Together, CMOs and CIOs are building an arsenal of experience-focused marketing tools that are powered by emerging technology. Their goal is to transform marketing from a customer acquisition-focused activity to one that enables a superb human experience, grounded in data. In experiential marketing, companies treat each customer as an individual by understanding their preferences and behaviors. Analytics and cognitive capabilities illuminate the context of customers' needs and desires, and determine the optimal way to engage with them. Experience-management tools tailor content and identify the best method of delivery across physical and digital touchpoints, bringing us closer to truly unique engagement with each and every human. Imagine a world in which a brand knows who you are and what you want, and can deliver the product, service, or experience that best suits your needs seamlessly and in real time, across physical or digital channels. Marketing technology is undergoing a renaissance. Channel-focused solutions such as websites, social and mobile platforms, content management tools, and search engine optimization are fast becoming yesterday's news. As part of the growing beyond marketing trend, organizations are adopting a new generation of martech systems that deliver unprecedented levels of customer intimacy, targeted engagement, and precision impact.

The Hugo Awards are some of the most important prizes in genre fiction, including science fiction and fantasy. Among past winners we see Ursula K. Le Guin, Isaac Asimov, Neil Gaiman, and most recently, N.K. Jemisin, who made history for winning Best Novel three years in a row for every book in her Broken Earth series. This year, nestled among nominees for novels, short stories, and even individual episodes of The Good Place and Doctor Who, is an unexpected contender for the Best Related Work category: the primarily women-run fan fiction website Archive of Our Own. Archive of Our Own (often known as "AO3" for short) is an online platform for fan works-- creative work based on existing media like novels, books, and video games, produced by fans of the originals. The nearly 5 million works archived there--4,690,000 as of this writing--represent almost 2 million registered users and countless more who visit the site every day, consuming content and leaving comments.

Cluster analysis or clustering is the task of dividing a set of objects in such a way that elements in the same group or cluster are more similar, according to some dissimilarity measure, than elements in different groups. To achieve this task there are two main types of algorithms: partitioning algorithms, which try to split the data into k groups that usually minimize some optimality criteria, or agglomerative algorithms, which start with single observations and merge them into clusters according to some dissimilarity measure. Such methods have been investigated in a large amount of literature, hence we refer to [12] and references therein for an overview. Clustering techniques used as unsupervised classification procedures are increasingly more influential in people's life since they are used in credit scoring, article recommendation, risk assessment, spam filtering or sentencing recommendations in courts of law, among others. Hence controlling the outcome of such procedures, in particular ensuring that some variables which should not be taken into account due to moral or legal issues are not playing a role in the classification of the observations, has become an important field of research known as fair learning. We refer to [15], [3], [1] or [9] for an overview of such legal issues and mathematical solutions to address them. For instance avoiding discrimination against sensitive characteristics such as sex, race or age can not only be achieved using the naive solution of simply ignoring such protected attribute. Indeed, if the the data at hand reflects a real world bias, machine learning algorithms can pick on this behaviour and emulate it. More precisely, suppose we have data that includes information about attributes that we know or suspect that are biased with respect to the protected class.

The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems may bring, models can reflect, inject, or exacerbate implicit and explicit societal biases into their outputs, disadvantaging certain demographic subgroups. Discovering which biases a machine learning model has introduced is a great challenge, due to the numerous definitions of fairness and the large number of potentially impacted subgroups. We present FairVis, a mixed-initiative visual analytics system that integrates a novel subgroup discovery technique for users to audit the fairness of machine learning models. Through FairVis, users can apply domain knowledge to generate and investigate known subgroups, and explore suggested and similar subgroups. FairVis' coordinated views enable users to explore a high-level overview of subgroup performance and subsequently drill down into detailed investigation of specific subgroups. We show how FairVis helps to discover biases in two real datasets used in predicting income and recidivism. As a visual analytics system devoted to discovering bias in machine learning, FairVis demonstrates how interactive visualization may help data scientists and the general public in understanding and creating more equitable algorithmic systems.

There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race, gender, or age. However, this raises two significant challenges: (1) protected attributes may not be available or it may not be legal to use them, and (2) it is often desirable to simultaneously consider multiple protected attributes, as well as their intersections. In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individual's true occupation and a word embedding of their name. This method leverages the societal biases that are encoded in word embeddings, eliminating the need for access to protected attributes. Crucially, it only requires access to individuals' names at training time and not at deployment time. We evaluate two variations of our proposed method using a large-scale dataset of online biographies. We find that both variations simultaneously reduce race and gender biases, with almost no reduction in the classifier's overall true positive rate.

Artificial intelligence is using satellite images to observe farms. The technique is being tested in the US to detect farms that may be illegally polluting the waterways, and has been trialled across Europe to monitor and inspect farmland. In the US, facilities known as concentrated animal feeding operations (CAFOs) comprise around 40 per cent of the country's livestock. These intensive farms often contain as many as 2500 pigs or 125,000 chickens per facility and generate around 335 million tonnes of waste per year. Manure forms a large proportion of this waste, which often makes its way into waterways untreated.

The government's new proposals to try and protect people from harm on the internet could actually create a huge censorship operation, campaigners have warned. Ministers have revealed the long-awaited white paper on online harms, in an attempt to deal with the dangers posed by content being distributed on social media sites like Facebook and Twitter. It recommends a wide variety of new legal changes, including imposing a duty of care on the tech firms and holding individual people at the top of those companies responsible for breaking the rules. They will force the companies to remove any offending content – from terrorist content to child abuse imagery – as quickly as they can, and within time limits. We'll tell you what's true.