A ccontainer vessel leaves the port in Singapore on July 16, 2020. Dow Chemical is in the midst of a digital transformation. They have set up Centers to test out new and emerging technologies. They have had success in developing valuable intellectual property in the area of trade classifications. Dr. John Wassick, Integrated Supply Chain Technology Fellow, Dow Inc. DD, was on a supply chain panel at the ARC Industry Forum in Orlando.

As the popularity of Amazon Alexa and other voice assistants grows, so too does the number of ways those assistants both do and can intrude on users' privacy. Examples include hacks that use lasers to surreptitiously unlock connected-doors and start cars, malicious assistant apps that eavesdrop and phish passwords, and discussions that are surreptitiously and routinely monitored by provider employees or are subpoenaed for use in criminal trials. Now, researchers have developed a device that may one day allow users to take back their privacy by warning when these devices are mistakenly or intentionally snooping on nearby people. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast.

It's hard to think of a world without smart home assistant devices such as Amazon's Alexa, ridesharing apps like Uber, fitness trackers, personalized music playlists or even mobile banking. While some of these innovations have only been with us for less than a decade, they're now thoroughly woven into the fabric of modern society. None of them would have been possible without advances in artificial intelligence and data science. Even sustainable fashion and medical research are using these technology tools to their advantage nowadays. What are the risks involved?

Of all the digital technologies that are driving change in businesses, Artificial Intelligence (AI) is perhaps the most disruptive of all and has taken over the globe by storm. Currently, AI based technology solutions are being deployed in manufacturing, automotive, e-commerce, construction, smart cities and warehousing. However, within the legal and financial sectors, the implementation of AI is not as rapid. With a fast-changing environment, adaptation of new emerging technologies and increasing volumes of information, we will have to accept that AI will be taking over significant aspects of jobs in the future. According to a recent study by the International Data Corporation, worldwide data is expected to grow 61 per cent to 175 zettabytes in five years.--Financial

In response to calls for greater interdisciplinary involvement from the social sciences and humanities in the development, governance, and study of artificial intelligence systems, this paper presents one sociologist's view on the problem of algorithmic bias and the reproduction of societal bias. Discussions of bias in AI cover much of the same conceptual terrain that sociologists studying inequality have long understood using more specific terms and theories. Concerns over reproducing societal bias should be informed by an understanding of the ways that inequality is continually reproduced in society -- processes that AI systems are either complicit in, or can be designed to disrupt and counter. The contrast presented here is between conservative and radical approaches to AI, with conservatism referring to dominant tendencies that reproduce and strengthen the status quo, while radical approaches work to disrupt systemic forms of inequality. The limitations of conservative approaches to class, gender, and racial bias are discussed as specific examples, along with the social structures and processes that biases in these areas are linked to. Societal issues can no longer be out of scope for AI and machine learning, given the impact of these systems on human lives. This requires engagement with a growing body of critical AI scholarship that goes beyond biased data to analyze structured ways of perpetuating inequality, opening up the possibility for radical alternatives.

Even before the recent George Floyd case, there has been much debate about the extent to which claims of systemic racism are supported by statistical evidence. For example (Ross 2015) claims that unarmed blacks are 3.5 times more likely to be shot by police than unarmed whites when adjusting for relative differences in population size. However, (Fryer 2016) - formally published later as (Fryer 2019) - found that there was no such racial disparity when the data were conditioned on people being stopped by police, and there was a similar conclusion in (Patty and Hanson 2020) that was produced in direct response to public concerns about the Floyd case. In response to Fryer, (Ross, Winterhalder, and McElreath 2018) argued that Fryer's analysis was compromised because it was essentially an example of Simpson's paradox (Simpson 1951; Bickel, Hammel, and O'Connell 1975; Fenton, Neil, and Constantinou 2019) whereby conclusions based on pooled statistics are reversed when drilling down into relevant subcategories. A new paper (Knox, Lowe, and Mummolo 2020) explains why Simpson's paradox is not the only statistical explanation for the apparently contradictory conclusions of Ross and Fryer.

Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ in whether they consider features of interest in isolation, e.g., Permutation Feature Importance (PFI), or in relation to all remaining feature variables, e.g., Conditional Feature Importance (CFI). As such, the perturbation mechanisms inherent to PFI and CFI represent extreme reference points. We introduce Relative Feature Importance (RFI), a generalization of PFI and CFI that allows for a more nuanced feature importance computation beyond the PFI versus CFI dichotomy. With RFI, the importance of a feature relative to any other subset of features can be assessed, including variables that were not available at training time. We derive general interpretation rules for RFI based on a detailed theoretical analysis of the implications of relative feature relevance, and demonstrate the method's usefulness on simulated examples.

Until recently, allegations of "racism" in the public sphere have operated like first degree murder charges do in courts of law--in order to establish such a charge, mainstream media often demanded proof of the alleged racist's intent. Dictionary definitions have long tracked this blinkered view of'racism.' For decades, Merriam-Webster's entry described racism as a "belief" of racial supremacy, or a program designed to put that belief into action. Because many people--and some judges--treat dictionary definitions as if they were legal prescriptions, accusations of racism have required proof of intent--a purposeful, race-based disparity in conduct or consequence. Thus, the legal framework for considering racial discrimination has largely echoed the dictionary's narrow take on racism.

DONE wisely, artificial intelligence "can be the best thing ever for humanity", says the fundamental physicist turned AI researcher Max Tegmark in our interview this week (see "If we do it wisely, AI can be the best thing ever for humanity"). We subscribe wholeheartedly to his assessment. Seldom has there been a technology with such an obvious power to improve our lot – or one with such obvious dangers. The risks are potentially existential.

Dr. Fallon Wilson is, like civil rights activist Fannie Lou Hamer, sick and tired of being sick and tired. Hamer and Wilson were both talking about a lack of progress on civil rights, but Wilson is talking specifically about data, AI, and tech from companies that have for years failed to make meaningful progress on diversity and inclusion initiatives. In a speech at the Kapor Center in Oakland, California, she said people cannot rely on companies like Facebook or Google to bring about meaningful change. "The truth is that the business of diversity and inclusion in tech companies will never eradicate structural racism, and I think we have to be clear about that," she said. "They cannot be the weathervane, nor should they, of what equitable progress looks like for Black people in this country as it relates to tech. Wilson was not referencing recent events like boycotts over Facebook's willingness to profit from hate or renewed diversity promises from Google and Microsoft.