Like any new technology, artificial intelligence is capable of immensely good or bad outcomes. The public seems increasingly focused on the bad, especially when it comes to the potential for bias in AI. This concern is both well-founded and well-documented. It is the simulation of human processes by machines. This fear of biased AI ignores a critical fact: The deepest-rooted source of bias in AI is the human behavior it is simulating. It is the biased data set used to train the algorithm.

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Discrimination in the U.S. credit market is well documented. Historically, minorities have disproportionately been denied loans, mortgages, and credit cards, or charged higher rates than other customers. Now that artificial intelligence is taking over many credit decisions -- and taking human bias out of the equation -- it'll be easy to enforce laws against discrimination in lending, right? Not necessarily, argues Jann Spiess, an assistant professor of operations, information, and technology at Stanford Graduate School of Business. In a recent paper in The University of Chicago Law Review, he and Talia Gillis, a doctoral student at Harvard Business School and Harvard Law School, examined what happens when existing anti-discrimination rules are applied to choices made by machines.

Recent developments in Named Entity Recognition (NER) have resulted in better and better models. However, is there a glass ceiling? Do we know which types of errors are still hard or even impossible to correct? In this paper, we present a detailed analysis of the types of errors in state-of-the-art machine learning (ML) methods. Our study reveals the weak and strong points of the Stanford, CMU, FLAIR, ELMO and BERT models, as well as their shared limitations. We also introduce new techniques for improving annotation, for training processes and for checking a model's quality and stability. Presented results are based on the CoNLL 2003 data set for the English language. A new enriched semantic annotation of errors for this data set and new diagnostic data sets are attached in the supplementary materials.

Reasoning about observed effects and their causes is important in multi-agent contexts. While there has been much work on causality from an objective standpoint, causality from the point of view of some particular agent has received much less attention. In this paper, we address this issue by incorporating an epistemic dimension to an existing formal model of causality. We define what it means for an agent to know the causes of an effect. Then using a counterexample, we prove that epistemic causality is a different notion from its objective counterpart. 1 Introduction Research on actual causality involves finding in a given narrative (trace) the event that caused an effect. Pearl [25, 26] was a pioneer to lead a computational enquiry in actual causality. The research was later continued by Halpern and Pearl [12, 15] and others [8, 17, 18, 13, 14]. Unfortunately, as argued by Glymour et al. [9], most of these accounts are developed by analyzing a handful of simple examples, and then validated relative to our intuition for these examples, a process which G oßler et al. [11] referred to as TEGAR (i.e. As such, even after multiple revisions, these definitions continue to suffer from various conceptual problems such as the early preemption problem and the over-determination problem. For instance, despite claims to the contrary, the definitions given in [14] suffer from the problem of preemption, which occurs when two competing events try to achieve the same effect and the latter of these fails to do so as the earlier one has already achieved the effect (see [31] and [4] for a discussion). In an attempt to address these issues, Batusov and Soutchanski [2, 3] recently proposed a new definition of actual causality that is based on a well developed and expressive formalization of actions and change, namely the situation calculus [23, 27]. The definition is derived from first principles and does not follow a TEGAR scheme.

A deep neural network (DNN) classifier represents a model owner's intellectual property as training a DNN classifier often requires lots of resource. Watermarking was recently proposed to protect the intellectual property of DNN classifiers. However, watermarking suffers from a key limitation: it sacrifices the utility/accuracy of the model owner's classifier because it tampers the classifier's training or fine-tuning process. In this work, we propose IPGuard, the first method to protect intellectual property of DNN classifiers that provably incurs no accuracy loss for the classifiers. Our key observation is that a DNN classifier can be uniquely represented by its classification boundary. Based on this observation, IPGuard extracts some data points near the classification boundary of the model owner's classifier and uses them to fingerprint the classifier. A DNN classifier is said to be a pirated version of the model owner's classifier if they predict the same labels for most fingerprinting data points. IPGuard is qualitatively different from watermarking. Specifically, IPGuard extracts fingerprinting data points near the classification boundary of a classifier that is already trained, while watermarking embeds watermarks into a classifier during its training or fine-tuning process. We extensively evaluate IPGuard on CIFAR-10, CIFAR-100, and ImageNet datasets. Our results show that IPGuard can robustly identify post-processed versions of the model owner's classifier as pirated versions of the classifier, and IPGuard can identify classifiers, which are not the model owner's classifier nor its post-processed versions, as non-pirated versions of the classifier.

This blog post was guest-written by Annie O'Rourke, CEO of Digital Workforce Australia and 89 Degrees East. She will be guest speaking about'Addressing real world problems with Artificial Intelligence' session of the Women Rock-IT series on 17 October. To sign up for this or other webinars in the series, click here. Don't tell my husband, but I've recently started an affair. No need to be too shocked though, because I'm pretty sure I can package it as so-called ethical polygamy.

Clearly alarmed by shifting public perceptions about facial recognition technology and the potential for state and local governments to impose an outright ban on the use of such technology, tech vendors and other businesses offering facial recognition technology solutions are now mobilizing their forces. They are reaching out to U.S. congressional leadership, urging the House and Senate to re-think any initiatives to impose a "blanket moratorium" on the use of facial recognition technology. And, at the same time, they are rushing to the legal defense of big Silicon Valley tech firms such as Facebook, which is facing a major class action lawsuit in the state of Illinois over the wrongful use of biometric facial data. In one highly public move, the U.S. Chamber of Commerce wrote an open letter on facial recognition technology, which was addressed to the top political leaders in both the U.S. House of Representatives and U.S. Senate. The letter on facial recognition technology urges political leaders to consider all the positive uses of the technology.

Roundup Here's our latest summary of AI news beyond what we've already covered. Over 40 festivals pledge to not use facial recognition: A campaign against facial recognition led by the nonprofit Fight for the Future has led to over 40 music festivals publicly committing that they would not use the technology. Evan Greer, deputy director, and Tom Morello, a musician and guitarist for rock band Rage Against the Machine, teamed up to pen an op-ed celebrating the efforts to push back on the smart AI cameras. "Over the last month, artists and fans waged a grassroots war to stop Orwellian surveillance technology from invading live music events," they wrote on Buzzfeed News. Our campaign pushed more than 40 of the world's largest music festivals -- like Coachella, Bonnaroo, and SXSW -- to go on the record and state clearly that they have no plans to use facial recognition technology at their events." Musicians and fans were invited to write to their favorite festival organizers, urging them to not support facial recognition. Now, the list of festivals that have confirmed they won't be using the tech has grown. There are still a few top names that have yet to respond, however, including Burning Man and Outside Lands. You can see the complete list here. Amazon's facial recognition tool fails on black athletes: Amazon's controversial Rekognition software mistook the faces of 27 black athletes competing in American football, baseball, basketball, and hockey, as suspected criminals in a mugshot database. An experiment by the American Civil Liberties Union (ACLU) revealed the dangers of relying on facial recognition technology like Rekognition. "This technology is flawed," said Duron Harmon, a football player for the New England Patriots safety whose face was false identified in the experiment. "If it misidentified me, my teammates, and other professional athletes in an experiment, imagine the real-life impact of false matches.

Technological advancements can enhance human development and contribute to creating optimal conditions for the exercise of human rights. At the same time, we need to address questions of fairness, of the risk of perpetuating bias and stereotypes, of discriminatory decision-making patterns, and of challenges related to interpretability, privacy, security and oversight. And we should ask ourselves: what can countries and international organisations do to address the challenge of "algocracy"? The discussion Artificial Intelligence – Technology to Serve Humankind will engage the audience in critical reflection on the challenges and opportunities that AI carries for individuals and societies, and for the viability of institutional frameworks, with a special emphasis on the use of the technology for public policies that enhance the quality of life and progress of humankind. The event will address the legal and ethical questions that accompany the current and potential use of AI in our society and identify potential ways forward.