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There has been remarkable success of machine learning (ML) technologies in empowering practical artificial intelligence (AI) applications, such as automatic speech recognition and computer vision. However, we are facing two major challenges in adopting AI today. One is that data in most industries exist in the form of isolated islands. The other is the ever-increasing demand for privacy-preserving AI. Conventional AI approaches based on centralized data collection cannot meet these challenges.

Artificial intelligence (AI) has become a part of our everyday lives from healthcare to law enforcement. AI-related ethical challenges have grown apace ranging from algorithmic bias and data privacy to transparency and accountability. As a direct reaction to these growing ethical concerns, organizations have been publishing their AI principles for ethical practice (over 100 sets and increasing). However, the multiplication of these mostly vaguely formulated principles has not proven to be helpful in guiding practice. Only by operationalizing AI principles for ethical practice can we help computer scientists, developers, and designers to spot and think through ethical issues and recognize when a complex ethical issue requires in-depth expert analysis.

The potential positive impact of Artificial Intelligence (AI) is significant and organizations can expect to cut GHG emissions by 16% in the next three to five years through AI-driven climate action projects according to a research report by Capgemini Research Institute. Despite the considerable potential of AI for climate action, adoption remains low. More than eight in ten organizations spend less than 5% of climate change investment on AI and data tracking; 54% have fewer than 5% of employees with the skills to take up data and AI-driven roles; and more than a third (37%) of sustainability executives have decelerated their climate goals in light of COVID-19, with the highest deceleration in the energy and utilities industry. Only 13% of organizations have aligned their climate vision and strategy with their AI capabilities – these are who Capgemini defines as climate AI champions. Two-fifths of these come from Europe, followed by the Americas and APAC.

Susan will present, "Understanding and Addressing Bias in Analytics" at CONVERGE, December 1-2. This article was originally published on KDnuggets. I use one of those credit monitoring services that regularly emails me about my credit score: "Congratulations, your score has gone up!" "Uh oh, your score has gone down! I shrug and delete the emails. Credit scores are just one example of the many automated decisions made about us as individuals on the basis of complex models.

BEGIN ARTICLE PREVIEW: Tech is often idealized, if not outright fetishized, as a great leveling force in society. It is assumed that the algorithms at the foundation of the digital sphere are impartial and purely objective. In truth, though, every program will in some way reflect the prejudices of the humans who write them. The new documentary Coded Bias explores how racism is written into the structures of contemporary life. Director Shalini Kantayya follows MIT researcher Joy Buolamwini, founder of the Algorithmic Justice League, who uncovered how facial scanning systems have difficulty recognizing nonmale and especially nonwhite faces. From there, Buolamwini explores further issues with automated racism, as AI is being increasingly incorporated into surveillance and law enforcement. Given the subject matter, it’s disappointing that the film yields to orientalist tropes by holding up China as a dark possibility for the US to follow, even though it admits that the on

Data compliance is eating up a growing amount of time for businesses, with legal, IT and any data-handling function have to dot their ‘i’s and cross their fingers that they haven’t made an expensive mistake. Automation and chatbots can play a growing role in ensuring companies can train their staff to manage and deal with customers over GDPR and other data laws.

Nontechnical stakeholders struggle to define business requirements. Crossfunctional teams face an uphill battle to set up robust pipelines for replicable data delivery. Machine learning models can take on a life of their own. If you've been ignoring these critical elements in the past, you may find your deployment rate skyrockets. Your data products may depend on correctly deploying the tips from this article.

What could be one of the most consequential First Amendment cases of the digital age is pending before a court in Illinois and will likely be argued before the end of the year. The case concerns Clearview AI, the technology company that surreptitiously scraped 3 billion images from the internet to feed a facial recognition app it sold to law enforcement agencies. Now confronting multiple lawsuits based on an Illinois privacy law, the company has retained Floyd Abrams, the prominent First Amendment litigator, to argue that its business activities are constitutionally protected. Landing Abrams was a coup for Clearview, but whether anyone else should be celebrating is less clear. A First Amendment that shielded Clearview and other technology companies from reasonable privacy regulation would be bad for privacy, obviously, but it would be bad for free speech, too.

The Los Angeles Police Department has barred officers and detectives from using outside facial recognition platforms in their investigations after uncovering a handful of detectives had used a powerful commercial software platform known as Clearview AI without permission. In a Nov. 13 directive sent to the entire agency, Deputy Chief John McMahon, who heads the LAPD's information technology bureau, noted that the only facial recognition system that LAPD officers are authorized to use is provided through the Los Angeles County Regional Identification System, which is maintained by the county and compares images input by officers against criminal booking photographs. Other platforms like Clearview, which compare images against millions of images posted on the Internet, are not authorized for investigative use, he said. "Department personnel shall not use third-party commercial facial recognition services or conduct facial recognition searches on behalf of outside agencies," McMahon wrote. "Moreover, any department personnel using FRT shall attend the proper training and obtain a certificate of completion prior to using the system."