It was lockdown in the Indian city of Hyderabad when activist S Q Masood was stopped on the street by police who asked him to remove his face mask and then took his picture, giving no reason and ignoring his objections. Worried about how the photographs would be used, Masood sent a legal notice to the city's police chief. But after receiving no response, he filed suit last month over Telangana state's use of facial recognition systems – the first such case in India. "Being Muslim and having worked with minority groups that are frequently targeted by the police, I'm concerned that my photo could be matched wrongly and that I could be harassed," Masood, 38, told the Thomson Reuters Foundation. "It is also about my right to privacy, and my right to know why my photograph was taken, what it will be used for, who can access it, and how it's protected. Everyone has a right to know this information," he said.

Artificial intelligence tools have become prevalent in legal practice, particularly in eDiscovery. That doesn't mean, however, that law firms and litigation support teams have been quick to embrace them. Despite their benefits, many legal organizations have been hesitant to implement AI tools. In the ABA 2020 Legal Tech Survey, 23% of law firms reported not being interested in AI, while 34% said they didn't know enough about AI to speak to their firms' interest. While the survey showed that larger firms were more likely to adopt AI tools, that leaves a lot of room for smaller firms to use AI to their advantage.

On Dec. 29, 2019, a Honda Civic pulled up to the intersection of Artesia Boulevard and Vermont Avenue in Gardena. It was just after midnight. The traffic light was green. As the car proceeded through the intersection, a 2016 Tesla Model S on Autopilot exited a freeway, ran through a red light and crashed into the Civic. The Civic's driver, Gilberto Alcazar Lopez, and his passenger, Maria Guadalupe Nieves-Lopez, were killed instantly.

As organizations roll out machine learning and AI models into production, they're increasing cognizant of the presence of bias in their systems. Not only does this bias potentially lead to poorer decisions on the part of the AI systems, but it can put the organizations running them in legal jeopardy. However, getting on top of this problem is turning out to be tougher than expected for a lot of organizations. For example, Harvard University and Accenture demonstrated how algorithmic bias can creep into the hiring processes at human resources departments in a report issued last year. In their 2021 joint report "Hidden Workers: Untapped Talent," the two organizations show how the combination of outdated job descriptions and automated hiring systems that leans heavily on algorithmic processes for posting of ads for open job and evaluation of resumes can keep otherwise qualified individuals from landing jobs.

Over the past few months, we've documented how the vast majority of AI's applications today are based on the category of algorithms known as deep learning, and how deep-learning algorithms find patterns in data. We've also covered how these technologies affect people's lives: how they can perpetuate injustice in hiring, retail, and security and may already be doing so in the criminal legal system. But it's not enough just to know that this bias exists. If we want to be able to fix it, we need to understand the mechanics of how it arises in the first place. We often shorthand our explanation of AI bias by blaming it on biased training data. The reality is more nuanced: bias can creep in long before the data is collected as well as at many other stages of the deep-learning process.

Just over one year ago, corporate AI ethics became a regular headline issue for the first time. In December 2020, Google had fired Timnit Gebru--one of its top AI ethics researchers--and in February 2021, it would terminate her ethics team co-lead, Margaret Mitchell. Though Google disputes their version of events, the terminations helped push some of the field's formerly niche debates to the forefront of the tech world. Every algorithm, whether it's dictating the contents of a social media feed or deciding if someone can secure a loan, could have real-world impacts and the potential to harm as much as it might help. Policymakers, tech companies, and researchers are all grappling with how best to address that fact, which has become impossible to ignore.

Regulating AI is complex, and it is very likely errors will be made. Pablo Molina, CISO at Drexel University says that this won´t keep regulators from adopting rules in 2022.

Topic taxonomies, which represent the latent topic (or category) structure of document collections, provide valuable knowledge of contents in many applications such as web search and information filtering. Recently, several unsupervised methods have been developed to automatically construct the topic taxonomy from a text corpus, but it is challenging to generate the desired taxonomy without any prior knowledge. In this paper, we study how to leverage the partial (or incomplete) information about the topic structure as guidance to find out the complete topic taxonomy. We propose a novel framework for topic taxonomy completion, named TaxoCom, which recursively expands the topic taxonomy by discovering novel sub-topic clusters of terms and documents. To effectively identify novel topics within a hierarchical topic structure, TaxoCom devises its embedding and clustering techniques to be closely-linked with each other: (i) locally discriminative embedding optimizes the text embedding space to be discriminative among known (i.e., given) sub-topics, and (ii) novelty adaptive clustering assigns terms into either one of the known sub-topics or novel sub-topics. Our comprehensive experiments on two real-world datasets demonstrate that TaxoCom not only generates the high-quality topic taxonomy in terms of term coherency and topic coverage but also outperforms all other baselines for a downstream task.

A gavel leans against the letters, "AI", which illustates the legal ramification and questions ... [ ] surrounding the implementation of artificial technologies. This is the second blog in the Ethical AI short series, looking at the positive and negative aspects of AI. The first blog focused on AI failures and identified lessons to be learned by leaders advancing their AI product innovations, or internal usage of AI solutions. Major progress as a result of the European Commission officially released this year its AI regulations to ensure a well-functioning internal market for artificial intelligence systems, based on EU values and fundamental rights. The new regulations offer the world's strong's attempt to create a uniform legal and ethics framework that can guide businesses and countries for years to come.

California prosecutors have filed two counts of vehicular manslaughter against the driver of a Tesla on Autopilot that ran a red light, slammed into another car and killed two people in 2019. California prosecutors have filed two counts of vehicular manslaughter against the driver of a Tesla on Autopilot that ran a red light, slammed into another car and killed two people in 2019. DETROIT -- California prosecutors have filed two counts of vehicular manslaughter against the driver of a Tesla on Autopilot who ran a red light, slammed into another car and killed two people in 2019. The defendant appears to be the first person to be charged with a felony in the United States for a fatal crash involving a motorist who was using a partially automated driving system. Los Angeles County prosecutors filed the charges in October, but they came to light only last week.