Alicia Carriquiry Distinguished Professor and President's Chair in Statistics Director of CSAFE, Iowa State University In the US criminal justice system, jurors choose between two competing hypothesis: the suspect is the source of the evidence found at the crime scene or s/he is not. The likelihood ratio framework, which relies on Bayes' theorem for assessing the probative value of evidence, is difficult to implement in practice, when evidence is in the form of an image. Machine learning provides a good alternative for determining whether the evidence supports the proposition that the suspect may have been its source. We illustrate these ideas using information about the surface topography of bullet lands.

"AI will fundamentally change the way we live and work. Therefore, we need to get it right and develop this technology in a way, which ensures the trust and security of our citizens while benefitting our economy," a commission spokesperson told EUobserver. In the EU, the largest and most well-established companies are likely to become first adopters of AI technologies, such as automotive companies in Germany or finance firms in the UK. However, the LinkedIn findings suggest that the current market ecosystem for AI in Europe is uneven across both gender and demographic lines. The EU Commission president-elect Ursula von der Leyen has promised that during the first three months in office, the college of commissioners will put forward legislation for a "coordinated approach on the human and ethical implication of AI".

Extraction, transformation and load (ETL) became a familiar concept in the 1990s, when data warehousing became a well known business intelligence (BI) concept. The advent of the web, and the vast volume of data took many organizations' focus away from ETL to data lakes. Too many people disparaged ETL as a tool of the past. However, as IT has always been aware, data lakes aren't a solution all to themselves and rebranding to ELT doesn't change the fact that there are now far more sources and targets than there ever were. Data movement is still a complex problem and metadata management (MDM), and it's a problem becoming even more challenging as regulatory requirements for privacy mean data must be better tracked and controlled.

A code of practice should govern when police forces deploy facial recognition technology, the information commissioner has said. It comes after South Wales Police was found to have acted lawfully when a shopper complained his human rights were breached when he was photographed. An investigation by commissioner Elizabeth Denham has raised "serious concerns" over use of the technology. Ms Denham called on the government to introduce a statutory code of practice. Ed Bridges had brought a legal challenge after he was photographed shopping in Cardiff in 2017, and the following year at a peaceful protest against the arms trade.

It's time for employers to start preparing for legislation recently signed into law in Illinois, the Artificial Intelligence Video Interview Act. The new law, which takes effect on January 1, 2020, regulates Illinois employers' use of artificial intelligence (AI) in the interview and hiring process. Under the AI Video Interview Act, employers that record video interviews and use AI technology to analyze applicants' suitability for employment must: Employers that conduct such interviews may not distribute videos to other parties, except as necessary to obtain expert assistance in evaluating a candidate's fitness for a particular position. In addition, an employer has only 30 days to destroy all video copies of the interview if an applicant seeks such destruction. This law highlights a myriad of privacy concerns for employers evaluating the costs and benefits of incorporating AI technology into their hiring practices.

ProPublica's analysis of recidivism predictions produced by Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) software tool for the task, has shown that the predictions were racially biased against African American defendants. We analyze the COMPAS data using a causal reformulation of the underlying algorithmic fairness problem. Specifically, we assess whether COMPAS exhibits racial bias against African American defendants using FACT, a recently introduced causality grounded measure of algorithmic fairness. We use the Neyman-Rubin potential outcomes framework for causal inference from observational data to estimate FACT from COMPAS data. Our analysis offers strong evidence that COMPAS exhibits racial bias against African American defendants. We further show that the FACT estimates from COMPAS data are robust in the presence of unmeasured confounding.

Even if artificial intelligence is only a part of a larger solution, we must arm the gatekeepers of patent rights with better tools so they can better carry out the goals of the patent system.

AI is the future and there's lots of money to be made from it. But organisations keep making the news over AI governance failings, such as Microsoft's chatbot that turned racist and google images labelling African-Americans as gorillas. We're seeing a growth of ethics and governance councils but with mixed success - Google shut theirs down. Why is good governance proving so difficult? Does there have to be a trade-off between good governance and innovation? The market for AI is has been forecast to grow from $9.5bn in 2018 to $118.6bn by 2025. Naturally there is a race to get to the opportunities first.

At the Common Good in the Digital Age tech conference recently held in Vatican City, Pope Francis urged Facebook executives, venture capitalists, and government regulators to be wary of the impact of AI and other technologies. "If mankind's so-called technological progress were to become an enemy of the common good, this would lead to an unfortunate regression to a form of barbarism dictated by the law of the strongest," he said. In a related but contextually different conversation, this summer Joy Buolamwini testified before Congress with Rep. Alexandria Ocasio-Cortez (D-NY) that multiple audits found facial recognition technology generally works best on white men and worst on women of color. What these two events have in common is their relationship to power dynamics in the AI ethics debate. Arguments about AI ethics can wage without mention of the word "power," but it's often there just under the surface. In fact, it's rarely the direct focus, but it needs to be. Power in AI is like gravity, an invisible force that influences every consideration of ethics in artificial intelligence. Power provides the means to influence which use cases are relevant; which problems are priorities; and who the tools, products, and services are made to serve. It underlies debates about how corporations and countries create policy governing use of the technology.

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