Now, if a clerk asks to help you, it probably means you've been acting shady. Artificial intelligence continues to seep into our daily lives, touching up photos, developing snacks, and imitating school girls online. Now, AI has been tasked with tackling a crime as old as retail itself: shoplifting. A recent study by telecom giant NTT found that Japanese businesses lose around 400 billion yen (US$3.7B) annually through five-fingered discounts. No store is immune to this larceny, except perhaps anvil shops, and technology has yet to come up with a strong enough solution to effectively combat it, until now.

Published Sunday, Jun. 24, 2018, 12:48 pm Dear EarthTalk: What are some ways artificial intelligence is being used to fight climate change and otherwise protect the environment? Artificial intelligence (AI), defined as the capability of machines to imitate intelligent human behavior and learn from data, is considered by many to be the final frontier of computing. And environmentalists and tech companies are now harnessing the power of AI to service to the environment. To wit, Microsoft announced in December 2017 that it is expanding its "AI for Earth" program and committing $50 million over the next five years to put AI technologies in the hands of individuals and organizations working to solve global environmental challenges, including climate change as well as water, agriculture and biodiversity issues. Lucas Joppa, Microsoft's first Chief Environmental Scientist, is convinced that AI is now mature enough and the global environmental crisis acute enough to justify the creation of an AI platform for the planet.

After the fall of the Berlin Wall, East German citizens were offered the chance to read the files kept on them by the Stasi, the much-feared Communist-era secret police service. To date, it is estimated that only 10 percent have taken the opportunity. In 2007, James Watson, the co-discoverer of the structure of DNA, asked that he not be given any information about his APOE gene, one allele of which is a known risk factor for Alzheimer's disease. Most people tell pollsters that, given the choice, they would prefer not to know the date of their own death--or even the future dates of happy events. Each of these is an example of willful ignorance.

The government and the police are discussing the idea of developing a computer system that can predict street crime by utilizing big data and artificial intelligence. They hope such a system will be able to show them where and how to take greater measures to prevent crime. Street crime prediction "has already achieved results in Europe and the United States," said Mami Kajita, who established the data-analysis company Singular Perturbations Inc. last year in hopes of developing a Japanese version of the methods used in the United States. In some parts of America, the police have ramped up patrols in areas where AI-based systems predicted crime was more likely to happen, achieving a reduction of 20 percent on average, Kajita said. A more cautionary tale comes from China, where the government is racing ahead to use big data and facial recognition technology to surveil the population.

Machine learning is revolutionizing the way we interact with the world. Popular websites use algorithms to analyze user data and recommend videos, customize social media feeds, and optimize advertisements. Unsurprisingly, machine learning is taking a large role in making decisions about human beings, ranging from credit to parole decisions, and is likely to be more and more widely used in the future. It is not hard to imagine that, even in cases where the final decisions are made by people, they will be doing so with advice from algorithms that make inferences from patterns in petabytes of data. Some proponents of machine learning have suggested that not only are these algorithms able to leverage the increasing amount of data we have access to, but also that they might be able to make these decisions more fairly, as they seem to not be subject to human biases. There is some truth to these claims.

The Serious Fraud Office is a specialist prosecuting authority tackling the top level of serious or complex fraud, bribery, and corruption across England, Wales and Northern Ireland. It both investigates and prosecutes its cases, which is unique but necessary because the cases are complicated, and lawyers and investigators need to work together from the outset of them. Supporting both lawyers and investigators is an IT infrastructure that is managed by chief technology officer (CTO) Ben Denison, who also looks after the technology that supports the SFO's operational activities. "That includes digital evidence, so in our cases when we're investigating someone we'll see information from phones, tablets, laptops, and email, and we have a digital forensics team that processes that material and extracts relevant data from it, and it can then be ingested into another system which our case teams use to review it from," Denison tells PublicTechnology. In essence, the SFO ensures that all of the data – whether it's digital or a hard copy – is put into one place, so that it can be reviewed together.

The AML/KYC regulatory landscape continues to impose greater costs to financial institutions as they begin to collect, refresh, and analyze more and more customer data. New Customer Due Diligence (CDD) requirements set forth by the Financial Crimes Enforcement Network (FinCen) include the Final Rule for beneficial ownership and control, and with effect from May 11, 2018, and the EU 5th AML Directive, which also mandates beneficial ownership collection for legal entity customers. Fines for AML and KYC deficiencies have topped billions of USD for both US and EU banks for lack of sufficient AML/KYC programs, failure to file Suspicious Activity Reports (SARs), CDD deficiencies, and other violations. The implications for financial institutions go well beyond additional documentation for customers to complete. The current AML/KYC regulatory framework calls for the implementation of a substantial framework to collect and analyze customer data on both a retroactive and ongoing basis.

In 2015, a worried father asked Rhema Vaithianathan a question that still weighs on her mind. A small crowd had gathered in a basement room in Pittsburgh, Pennsylvania, to hear her explain how software might tackle child abuse. Each day, the area's hotline receives dozens of calls from people who suspect that a child is in danger; some of these are then flagged by call-centre staff for investigation. But the system does not catch all cases of abuse. Vaithianathan and her colleagues had just won a half-million-dollar contract to build an algorithm to help. Vaithianathan, a health economist who co-directs the Centre for Social Data Analytics at the Auckland University of Technology in New Zealand, told the crowd how the algorithm might work. For example, a tool trained on reams of data -- including family backgrounds and criminal records -- could generate risk scores when calls come in. That could help call screeners to flag which families to investigate.

This translation by Jeffrey Ding, edited by Paul Triolo, covers some of the most interesting parts of the Standards Administration of China's 2018 White Paper on Artificial Intelligence Standardization, a joint effort by more than 30 academic and industry organizations overseen by the Chinese Electronics Standards Institute. Ding, Triolo, and Samm Sacks describe the importance of this white paper and other Chinese government efforts to influence global AI development and policy formulation in their companion piece, "Chinese Interests Take a Big Seat at the AI Governance Table." Historical experience demonstrates that new technologies can often improve productivity and promote societal progress. But at the same time, as artificial intelligence (AI) is still in the early phrase of development, the policies, laws, and standards for safety, ethics, and privacy in this area are worthy of attention. In the case of AI technology, issues of safety, ethics, and privacy have a direct impact on people's trust in AI technology in their interaction experience with AI tools.

Employees at Amazon.com are calling on chief executive Jeffrey P. Bezos to end the sale of facial-recognition technology to law enforcement agencies and to discontinue partnerships with companies that work with U.S. Immigration and Customs Enforcement (ICE). In a letter, a group of Amazon workers said they are also troubled by a recent report from the American Civil Liberties Union revealing the company's sale and marketing of Rekognition, its facial-recognition technology, to police departments and government agencies. Workers at Amazon are protesting the recently halted Trump administration policy of separating migrant children from their parents at the U.S.-Mexico border. "We don't have to wait to find out how these technologies will be used. We already know that in the midst of historic militarization of police, renewed targeting of Black activists, and the growth of a federal deportation force currently engaged in human rights abuses -- this will be another powerful tool for the surveillance state, and ultimately serve to harm the most marginalized," the letter states.