It was a sunny afternoon last month when my smartphone decided to ignore me. Well, it didn't ignore my African American self, but it did ignore the carved face of a black man in a sculpture I was trying to photograph. Instead, it bracketed the carving of a white man's face to indicate that it was "seeing" him, while not bracketing the black face in the center of the frame. This problem of artificial intelligence having difficulty with black faces has been around for at least a decade. In addition to causing issues with commonplace activities, like taking pictures, it also raises a bigger question of how we guarantee equal opportunity across race in a world run on AI. AI is already ubiquitous and powerful, and it becomes more so every year.

Algorithmic or automated decision systems use data and statistical analyses to classify people for the purpose of assessing their eligibility for a benefit or penalty. Such systems have been traditionally used for credit decisions, and currently are widely used for employment screening, insurance eligibility, and marketing. They are also used in the public sector, including for the delivery of government services, and in criminal justice sentencing and probation decisions. Most of these automated decision systems rely on traditional statistical techniques like regression analysis. Recently, though, these systems have incorporated machine learning to improve their accuracy and fairness. These advanced statistical techniques seek to find patterns in data without requiring the analyst to specify in advance which factors to use. They will often find new, unexpected connections that might not be obvious to the analyst or follow from a common sense or theoretic understanding of the subject matter. As a result, they can help to discover new factors that improve the accuracy of eligibility predictions and the decisions based on them. In many cases, they can also improve the fairness of these decisions, for instance, by expanding the pool of qualified job applicants to improve the diversity of a company's workforce.

Editor's Note: All opinion section content reflects the views of the individual author only and does not represent a stance taken by The Collegian or its editorial board. Letters to the Editor reflect the view of a member of the campus community and are submitted to the publication for approval. I am writing this essay to bring awareness and recognition to a fast-approaching topic in the field of military technology -- weaponized artificial intelligence. Weaponized AI is any military technology that operates off a computer system that makes its own decisions. Simply put, anything that automatically decides a course of action against an enemy without human control would fall under this definition. Weaponized AI is a perfect example of a sci-fi idea that has found its way into the real world and is not yet completely understood.

Accenture said the acquisition will further equip its clients with capabilities to meet the growing demand for enterprise-scale AI, analytics and automation solutions. On Friday (13 December), professional services company Accenture announced that it has entered into an agreement to acquire Clarity Insights. Clarity Insights, a Chicago-based data consultancy with deep data science, AI and machine learning expertise, will bring its 350 employees to Accenture's Applied Intelligence business in North America. Founded in 2008, Clarity Insights focuses on serving clients' needs from end to end, aiming to transform business processes to embed and scale AI with deeper insights from data. Accenture, which is headquartered in Dublin, did not disclose the terms of the deal.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

People need to be held accountable for the mistakes AI and algorithms make on their behalf, such as that seen in the government's robodebt scandal, according to Australian human rights commissioner Ed Santow. The proposal comes in a new discussion paper on the impact of new technologies on human rights in Australia, released by the commission on Tuesday. After the Australian government backed down on the use of automatic debt notices based on income averaging, and had legislation for its facial recognition system rejected by a government-dominated parliamentary committee, Santow said it was time to set some rules to govern how these new technologies are used. "Robodebt is just a prominent example of data science and government AI being used in decision-making," he said. All government use of AI should be enshrined in legislation, he said.

As 2019 winds down, Legal Current is revisiting milestones and key accomplishments from the Legal business of Thomson Reuters this year. Today, we look at the impact of the Westlaw Edge Quick Check launch. In July, Westlaw Edge Quick Check became the latest addition to the Westlaw Edge legal research suite. The sophisticated artificial intelligence-based lawyer work product review and analysis tool allows Westlaw Edge users to quickly and securely review motions, briefs or text with citations to instill more confidence in their legal research and ensure they have not missed anything important. The launch earned praise from leading legal industry influencers, including TechLaw Crossroads' Steve Embry, who noted: "The beauty of the product lies, of course, in Westlaw's massive data base of cases and authorities.

GBT Technologies is a development-stage company, native IoT creator, developing Internet of Things (IoT) and Artificial Intelligence (AI) enabled mobile technology platforms. As Artificial Intelligence (AI) becomes increasingly visible in the popular imagination through applications such as virtual assistants, computer vision, natural language processing and self-driving cars, companies are unsurprisingly expanding their IP (intellectual property) portfolios to keep pace. According to the World Intellectual Property Organization (WIPO), "Since that time, innovators and researchers have published over 1.6 million AI-related scientific publications and filed patent applications for nearly 340,000 AI-related inventions." The report from WIPO looks at growth trends in AI, and specifically AI-related application patents. According to the research, machine learning (ML) is mentioned in 40 percent of all AI-related patents, while deep learning represents the "fastest growing technique in AI, with a 175 percent increase [from 2013 to 2016]."

The Chief Justice of India may have cleared the air on India's courts using artificial intelligence (AI) in their decision-making process, but some concerns remain. In a measured gesture, CJI SA Bobde clarified a statement he reportedly made a few weeks ago at an event, saying that using technologies such as AI could help deliver justice swiftly. The CJI had also said that such technologies would help streamline hearing of cases while enabling better court management. The statement made many experts raise concerns over the potential abuse of AI in the judicial decision-making processes -- an issue that's fodder for heated debates across the globe now -- including former CJI RM Lodha, who at an event in Nagpur last week, expressed concerns over the use of AI in court proceedings. Today, the explosive growth in machine learning and AI seems so enticing that almost everyone, from businesses to governments to savvy individuals, is tempted to try these emerging technologies in services and processes they deem fit and in requirement of some automatic processes.

PUBLICATION OF THE PLATFORM - This publication addresses the question of the extent to which the existing legal norms can be applied to Artificial Intelligence. The Working Group on the Legal Framework highlights which legislative gaps the legislator may have to close.