With its White Paper on Artificial Intelligence (AI), the European Commission embraces the potential of AI in the European economy and labour market. They have the potential to serve applicants, clients and society by enabling better matches and a faster, more efficient process. These improvements will prove essential for the recovery of the European labour markets following the drastic impact of the Covid-19 pandemic. Still, the human touch will remain crucial in the recruitment industry. Leveraging technology in a smart way allows us to free-up time to focus more on those elements of our work that require human creativity and emotion – traits that technology cannot emulate. The future will be one combining smart tech and human touch.

In this paper we discuss approaches to evaluating and validating the ethical claims of a Conversational AI system. We outline considerations around both a top-down regulatory approach and bottom-up processes. We describe the ethical basis for each approach and propose a hybrid which we demonstrate by taking the case of a customer service chatbot as an example. We speculate on the kinds of top-down and bottom-up processes that would need to exist for a hybrid framework to successfully function as both an enabler as well as a shepherd among multiple use-cases and multiple competing AI solutions.

Effective machine learning models can automatically learn useful information from a large quantity of data and provide decisions in a high accuracy. These models may, however, lead to unfair predictions in certain sense among the population groups of interest, where the grouping is based on such sensitive attributes as race and gender. Various fairness definitions, such as demographic parity and equalized odds, were proposed in prior art to ensure that decisions guided by the machine learning models are equitable. Unfortunately, the "fair" model trained with these fairness definitions is threshold sensitive, i.e., the condition of fairness may no longer hold true when tuning the decision threshold. This paper introduces the notion of threshold invariant fairness, which enforces equitable performances across different groups independent of the decision threshold. To achieve this goal, this paper proposes to equalize the risk distributions among the groups via two approximation methods. Experimental results demonstrate that the proposed methodology is effective to alleviate the threshold sensitivity in machine learning models designed to achieve fairness.

Microsoft isn't selling facial recognition tech to local police, but it apparently doesn't have that reservation for federal law enforcement. The ACLU has published emails indicating that Microsoft "aggressively" pitched the Drug Enforcement Administration on facial recognition between at least September 2017 and November 2018 (the emails extend to December 2018). The tech firm went so far as to host DEA staff for numerous demos and training sessions, and there was even a pilot program. The Administration apparently declined to buy the technology in November 2018, in part because of public concerns about the FBI's use of facial recognition data. The ACLU sued the DEA and FBI in October 2019 to obtain records showing how they use facial recognition.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Amazon, which last week imposed a one-year ban on selling its controversial facial-recognition technology to the police, is being called out for a "performative" gesture amid the ongoing Black Lives Matter protests. That technology, known as Rekogntiion, has long drawn the ire of racial justice groups and civil liberties advocates, who claim it is biased against black people and should not be sold to law enforcement agencies in the U.S. "Corporations have been quick to share expressions of support for the Black Lives Matter movement following the public outrage over the murders of Black Americans like George Floyd at the hands of police," said Rep. Jimmy Gomez, D-Calif., in a letter to Amazon CEO Jeff Bezos. "Unfortunately, too many of these gestures have been performative at best. Calling on Congress to regulate facial recognition technology is one of these gestures. However, Amazon – as a global leader in technology and innovation – has a unique opportunity before them to put substantive action behind their sentiments of'solidarity with the Black community' by not selling a flawed product to police, and instead, play a critical role in ending systemic racism in our nation's criminal justice system," the California lawmaker continued.

Recent events of racial discrimination in law enforcement and the healthcare industry have shown us how biased and racist humans are. The systemic racism that rules over these institutions is evident from police brutality on black people to the denial of hospital treatment of black patients during the pandemic. We all know the countless stories of innocent black people being targeted and killed by the police. In the healthcare industry, for example, the pain of black patients is commonly taken less seriously than that of white patients. All of these failures of the system are due to human racial bias, either conscious or unconscious. Computers are often perceived as far less biased compared to human decision-making processes.

Legend has it that when an emperor asked an inventor to name his reward, the inventor asked the emperor for payment in the humble grain of rice, giving the inventor the total gained by doubling a single grain of rice over a 64-square chessboard. In the end, the final squares had exponential mountains of rice. The velocity of technology advancement, especially for the manufacturing industry, is no different--it's exponential. Despite its lower digital maturity and research and development (R&D) investment than other industries,1 the manufacturing industry has been successful in gradually furthering its innovation agenda by increasing its patent-based innovation intensity to build new product and service capabilities. Continuous advancement will likely become critical to the success in an ecosystem that industry leaders believe is nearing the "second half of the chessboard."2

In the race to enable manufacturing plants to increase production in the face of an intermittent human workforce, manufacturers are looking at how to supplement their cameras with AI to give human inspectors the ability to spot defective products immediately and correct the problem. While machine vision has been around for more than 60 years, the recent surge in the popularity of deep learning has elevated this sometimes misunderstood technology to the attention of major manufacturers globally. As CEO of a deep learning software company, I've seen how deep learning is a natural next step from machine vision, and has the potential to drive innovation for manufacturers. How does deep learning differ from machine vision, and how can manufacturers leverage this natural evolution of camera technology to cope with real-world demands? In the 1960s, several groups of scientists, many of them in the Boston area, set forth to solve "the machine vision problem."

Artificial Intelligence is often interchanged with the word robotics. Although AI might be the single most tremendous technology revolution of our days, with the potential to disrupt almost all aspects of human existence, it does not mean robots are not crucial in our digital world. From helping fight the recent COVID-19 by carrying infectious samples, medicines, food from one place to other to disinfecting public space, to helping manufacturing sectors in assembly lines to inspecting raw materials, robots are almost omnipresent. However, it is still far from being the silver bullet due to the bias of Artificial Intelligence. Recently in May, when Microsoft proposed to replace their human editors with an AI, much was at stake due to Microsoft's reputation of racist bot Tay.

Google has said it is exploring why a picture of Winston Churchill went missing from a search list of former UK prime ministers, amid controversy over the legacy of the wartime leader. The company apologised on Sunday morning for the disappearance of the picture from its "knowledge graph" listing, adding that many photos of Churchill could still be found on its search engine. In a statement made on Twitter, Google's search liaison team said: "We're aware an image for Sir Winston Churchill is missing from his Knowledge Graph entry on Google. This was not purposeful and will be resolved." The problem, which was fixed at around midday on Sunday, was allegedly not specific to Churchill, with a similar problems occurring with images of former prime ministers Harold Wilson, Ramsay MacDonald and Stanley Baldwin.