Since the onset of the pandemic, a growing number of companies have turned to AI to assist with their hiring. The most common systems involve using face-scanning algorithms, games, questions, or other evaluations to help determine which candidates to interview. While activists and scholars warn that these screening tools can perpetuate discrimination, the makers themselves argue that algorithmic hiring helps correct for human biases. Algorithms can be tested and tweaked, whereas human biases are much harder to correct--or so the thinking goes. In a December 2019 paper, researchers at Cornell reviewed the landscape of algorithmic screening companies to analyze their claims and practices.

A group of U.K. Uber drivers has launched a legal challenge against the company's subsidiary in the Netherlands. The complaints relate to access to personal data and algorithmic accountability. Uber drivers and Uber Eats couriers are being invited to join the challenge, which targets Uber's use of profiling and data-fueled algorithms to manage gig workers in Europe. Platform workers involved in the case are also seeking to exercise a broader suite of data access rights baked into EU data protection law. It looks like a fascinating test of how far existing legal protections wrap around automated decisions at a time when regional lawmakers are busy drawing up a risk-based framework for regulating applications of artificial intelligence. Many uses of AI technology look set to remain subject only to protections baked into the existing General Data Protection Regulation (GDPR).

What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.

The National Academies of Sciences, Engineering, and Medicine (NASEM) are now assembling an ad hoc committee to identify emerging scientific and technological advances from across a broad range of disciplines that the U.S. Environmental Protection Agency's (EPA) Office of Research and Development (ORD) should consider in its research planning to support EPA's mission for protecting human health and the environment. In addition, according to NASEM, the committee will recommend how ORD could best take advantage of those advances to meet current and future challenges during the next 10 - 20 years. NASEM states that the committee will consider EPA's mission, strategic planning documents, and current initiatives, as well as other broader topics, including, but not limited to, biotechnology, big data, climate impacts, environmental monitoring and sensors, impacts of stressors on ecological and human health, and artificial intelligence and machine learning. The committee also will consider advances that help EPA better incorporate systems thinking into multimedia, multidisciplinary approaches. Nominations for committee members and reviewers are due August 5, 2020.

What does it mean to have freedom of speech? Naively, it means that you have the right to express ideas without fear of governmental retaliation or censorship. Free speech is valuable when you are communicating with others: abstractly, freedom of speech means the right to distribute information to an audience. If you frame freedom of speech not in terms of what comes out of your mouth, but in terms of the interaction between yourself and another party,1 then edge cases rapidly emerge. For example, suppose that you are on the street, lawfully raising a protest sign supporting X.

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LCRSummarizer is a prototype of tool for automatic extractive text summarization of legal documents. LCRSummarizer was developed using Python programming language and usual NLP libraries, such as: nltk and spacy. Summarization was implemented using TF-IDF (Term Frequency - Inverse Document Frequency) and NER (Named Entity Recognition). The configurability of summary is enabled by adjusting the importance of the desired entities and key phrases in the document through sliders in center of user interface. Watch video to see how LCRSummarizer works...

Kate Compton, an expert in artificial intelligence, responds to Holli Mintzer's "Legal Salvage." I've begun collecting vintage brooches. I started after reading a theory that Queen Elizabeth was communicating secret political shade through her choice of accessories. They also reminded me of my grandmother, a woman with that refined 1950s hostess style that I learned to associate with being an adult. I can wear one to feel like the sort of formidable grand dame that I imagine myself growing into as I age.

In this special guest feature, Joseph E. Mutschelknaus, a director in Sterne Kessler's Electronics Practice Group, addresses some of the top data privacy compliance issues that startups dealing with AI and ML applications face. He also assists with district court litigation and licensing issues. Based in Washington, D.C. and renown for more than four decades for dedication to the protection, transfer, and enforcement of intellectual property rights, Sterne, Kessler, Goldstein & Fox is one of the most highly regarded intellectual property specialty law firms in the world. Last year, the Federal Trade Commission (FTC) hit both Facebook and Google with record fines relating to their handling of personal data. The California Consumer Privacy Act (CCPA), which is widely viewed the toughest privacy law in the U.S., came online this year.

This week I came across several articles that challenge the development and utilization of AI-based system across several domains. This week I came across several articles that challenge the development and utilization of AI-based system across several domains. I've never had to genuinely reflect on the philosophical and legal aspects of my contributions as a machine learning practitioner, but this has changed after reading some interesting articles that present the consequences of AI advancement that are happening now, and those that are yet to happen. Our lives today could look entirely different tomorrow. Would you let a machine learning model that has a failure rate of 98% and a false positive rate of 81% into production?