Starting a new life is difficult for criminals transitioning from prison back to regular society. To help those individuals, Purdue University Polytechnic Institute researchers are using artificial intelligence to uncover risky behaviors which could then help identify when early intervention opportunities could be beneficial. Results of a U.S. Department of Justice study indicated more than 80 percent of people in state prisons were arrested at least once in the nine years following their release. Almost half of those arrests came in the first year following release. Marcus Rogers and Umit Karabiyik of Purdue Polytechnic's Department of Computer and Information Technology, are leading an ongoing project focused on using AI-enabled tools and technology to reduce the recidivism rates for convicted criminals who have been released.

—Rebecca Bayeck and Azure Stewart “Artificial Intelligence and Archives” was the inaugural webinar of the series on Emerging Technologies, Big Data & Archives, organized by CLIR postdocs Rebecca Y. Bayeck of the Schomburg Center for Research in Black Culture and Azure Stewart of New York University. With the emergence of new technologies and big data, the processing and preservation of data has changed and will continue to change. As in other domains (e.g., health, video games), artificial intelligence (AI) is increasingly reshaping the way we process, interact with, and think about archives. Consequently, in the age of big data, archives are not just “a collection of historical records relating to a place, organization, or family” (Cambridge Dictionary Online). Today, archives also include all types of digital data—including social media data—and algorithms. Archivists are therefore called on to preserve and process data as they are being created, which requires understanding AI languages, processes, and practices for the creation and protection of data/records now for the future. In this webinar, our speaker Dr. Anthea Seles, from the International Council on Archives (ICA), discussed AI in archival spaces: its uses, application, and the role archivists should play to become critical voices in AI discussions. Two hours were not enough to address all the questions raised by the 280 attendees. As a follow up to the webinar, we have thematically organized and addressed the unanswered questions and present them here. Artificial Intelligence in Archives How much has AI penetrated archives in the developing world? I would say [this has been] limited, if at all. I think the main issue is that these technologies are being applied in the assessment of development initiatives like Sustainable Development Goals (SDGs). Increasingly there are many projects focusing on artificial intelligence and human rights, for example the University of Essex Human Rights, Big Data and Technology Project, and it is becoming a concern for organisations like Amnesty International. Who already has the best AI for archives today, according to ICA regulation, that we can adopt? There is no commercial provider that works specifically on archival questions. I think you can use off-the-shelf eDiscovery software, but you need to have a basic understanding of what the technology is doing in order to measure your precision and recall.  Artificial Intelligence Tools Will governments and big corporations use artificial intelligence as a tool to centralize information in future? Potentially. I think there is some thinking about this coming out of the records management community, but I still believe it is about balancing the strengths of the tool with the continuing need for human intervention. The question is, when will the human be needed? And what can the tool be trusted to do with minimum supervision? How do we ensure a continuous feedback loop to identify records of long-term value as information creation changes?  What tools were you using for the file analysis and visualization in this presentation? The screen shots are only example photos, they are not from any of the tools we used. We looked at several eDiscovery tools with different algorithms (e.g., Latent Semantic Indexing, Latent Dirichlet Allocation). These are bog standard machine learning applications that have been around for a while, and we chose to go down that road to see what we could get in off-the-shelf commercial software packages. So, is there a way to write a script to avoid metadata corruption and alteration? There are tools now you can use that will preserve the integrity of the metadata when you move material from one system or file to another. I think for historical metadata alteration/corruption it is a question of how we explain this to users and how this might affect different access methods like visualisation.  Will the International Council on Archives provide training on artificial intelligence and machine learning? Not yet, but I’m open to suggestions. [We are] currently speaking with different stakeholders and maybe we can hold a hackathon at the Abu Dhabi Congress.  Access to Archives Will the course Managing Digital Archives be accessible online? The managing digital archives course is organized by the ICA and will be accessible online in fall 2020. Please check the ICA website or social media channels (Twitter and Facebook) for more information. What are some of the practices in the UK National Archives and government on managing structured data as records? How does the UK identify, capture, manage, and apply retention and disposition to data (both transactional applications and analytical ones)? There are no published policies on identification of datasets that I can see and would suggest you contact either the record copying or the UK government web archive records unit to see if anything more substantive has been developed. What is your suggestion for keeping physical records for posterity and authentication? Records should always be maintained in the format in which they are created. The belief in scanning paper records and destroying them in order to save space and save on storage costs is a false economy. The level at which you should be scanning that material and the amount of metadata that should be captured to maintain it over time is very high. Also, you need to take into account computer storage costs, and whether you can afford the costs of digital preservation software, which all begins to add up. One must also take into account the active management of these authentic digital surrogates by digital preservation specialists. Furthermore, if you have a paper management problem and you don’t take that into account when you move into the digital environment you are then transferring an analog integrity issue into a digital integrity/authenticity issue. Digital will not solve integrity issues; in my opinion it will magnify them. Artificial Intelligence and Society In Brazil, we are concerned with the problem of the spread and political use of misinformation (fake news). How can archivists with algorithm training provide reliable research insights to fight against this historical problem? At this point, I couldn’t honestly provide you with an answer but Read More

Previous work in the social sciences, psychology and linguistics has show that liars have some control over the content of their stories, however their underlying state of mind may "leak out" through the way that they tell them. To the best of our knowledge, no previous systematic effort exists in order to describe and model deception language for Brazilian Portuguese. To fill this important gap, we carry out an initial empirical linguistic study on false statements in Brazilian news. We methodically analyze linguistic features using the Fake.Br corpus, which includes both fake and true news. The results show that they present substantial lexical, syntactic and semantic variations, as well as punctuation and emotion distinctions.

The company is calling for 10 billion yuan ($1.4 billion) in damages and demands that Apple cease "manufacturing, using, promising to sell, selling, and importing" products that infringe on the patent, it said in a social media post. In the lawsuit filed in a local Chinese court, Xiao-i argued that Apple's voice-recognition technology Siri infringes on a patent that it applied for in 2004 and was granted in 2009. In a statement, Apple said that its Siri does not contain features included in the Xiao-i patent, which the iPhone maker argues relates to games and instant messaging. The company also said that independent appraisers certified by the Supreme People's Court have concluded that Apple does not infringe Xiao-i Robot's technology. "We are disappointed Xiao-i Robot has filed another lawsuit," Apple said in a statement.

Thanks to electronic banking, there are millennials who have never written a paper cheque. And whilst the overall volume of paper cheques continues to plummet by 10 per cent to 15 per cent a year, NatWest Group still sometimes processes as many as 500,000 cheques per day and needs to do so as quickly, accurately and cost-effectively as possible. The UK recently introduced legislation requiring that all banks switch to image exchange of cheques. The new legislation means that banks now capture images of cheques and exchange these instead of the physical paper cheques. NatWest Group decided that instead of doing the bare minimum to comply with the image-clearing cheque system requirements, it would take the initiative and work with long-time outsourcing partner DXC Technology to completely transform the cheque-processing system.

In a win for transparency, a state court judge ordered the California Department of Corrections and Rehabilitation (CDCR) to disclose records regarding the race and ethnicity of parole candidates. This is also a win for innovation, because the plaintiffs will use this data to build new technology in service of criminal justice reform and racial justice. In Voss v. CDCR, EFF represented a team of researchers (known as Project Recon) from Stanford University and University of Oregon who are attempting to study California parole suitability determinations using machine-learning models. This involves using automation to review over 50,000 parole hearing transcripts and identify various factors that influence parole determinations. Project Recon's ultimate goal is to develop an AI tool that can identify parole denials that may have been influenced by improper factors as potential candidates for reconsideration.

A close-up of a police facial recognition camera used in Cardiff, Wales. Since 2017, police in the UK have been testing live, or real-time, facial recognition in public places to try to identify criminals. The legality of these trials has been widely questioned by privacy and human rights campaigners, who just won a landmark case that could have a lasting impact on how police use the technology in the future. In a ruling Tuesday, the UK Court of Appeal said South Wales Police had been using the technology unlawfully, which amounted to a violation of human rights. In a case brought by civil liberties campaigner Ed Bridges and supported by human rights group Liberty, three senior judges ruled that the South Wales Police had violated Bridges' right to privacy under the European Convention of Human Rights.

For the third straight year, Deloitte surveyed executives about their companies' sentiments and practices regarding AI technologies. We were particularly interested in understanding what it will take to stay ahead of the pack as AI adoption grows--and we wanted to learn how adopters are managing risk around the technologies as AI governance, trust, and ethics become more of a boardroom issue. Get the Deloitte Insights app. Adopters continue to have confidence in AI technologies' ability to drive value and advantage. We see increasing levels of AI technology implementation and financial investment. Adopters say they are realizing competitive advantage and expect AI-powered transformation to happen for both their organization and industry. Early-mover advantage may fade soon. As adoption becomes ubiquitous, AI-powered organizations may have to work harder to maintain an edge over their industry peers.

Starting a new life is difficult for criminals transitioning from prison back to regular society. To help those individuals, Purdue University Polytechnic Institute researchers are using artificial intelligence to uncover risky behaviors which could then help identify when early intervention opportunities could be beneficial. Results of a U.S. Department of Justice study indicated more than 80 percent of people in state prisons were arrested at least once in the nine years following their release. Almost half of those arrests came in the first year following release. Marcus Rogers and Umit Karabiyik of Purdue Polytechnic's Department of Computer and Information Technology, are leading an ongoing project focused on using AI-enabled tools and technology to reduce the recidivism rates for convicted criminals who have been released.

The UK Court of Appeal has unanimously reached a decision against a face-recognition system used by South Wales Police. The judgment, which called the use of automated face recognition (AFR) "unlawful", could have ramifications for the widespread use of such technology across the UK. But there is disagreement about exactly what the consequences will be. Ed Bridges, who initially launched a case after police cameras digitally analysed his face in the street, had appealed, with the support of personal rights campaign group Liberty, against the use of face recognition by police. The police force claimed in court that the technology was similar to the use of closed-circuit television (CCTV) cameras in cities.