Federal Trade Commission chair Lina Khan is hiring three artificial intelligence researchers from New York University to advise on emerging technology issues. Khan announced Friday that NYU's Meredith Whittaker, Amba Kak, and Sarah Myers West are joining an AI strategy group within the FTC's Office of Policy Planning. They join Olivier Sylvain, a law professor from Fordham University, who is serving as Khan's senior adviser on technology. The new hires likely signal an increased regulatory emphasis on AI technologies like facial recognition, which is also an area of expertise for Alvaro Bedoya, who's been nominated as an FTC commissioner.

The VP, Artificial Intelligence (AI) will help Synchrony coordinate and manage the CTO AI roadmap, aligning marketplace opportunities to Synchrony business objectives. The VP, Artificial Intelligence will also help Synchrony capitalize on this rapidly changing technology environment by working with the business and technology teams to explore, evaluate and deploy AI capabilities ensuring a cohesive and comprehensive Artificial Intelligence strategy. This position is remote, where you have the option to work from home. On occasion we may request for you to commute to our nearest office for in person engagement activities such as team meetings, training and culture events. We're proud to offer you choice and flexibility.

As we know, the importance of patents is immense in all industries. Amidst today's hyper-competitive landscape, organizations are aggressively acquiring or filing for patents to keep their innovations secure. AI can assist organizations in managing and analyzing patents more accurately. The analysis of patents is a significant part of IP management today. Competently handled patent analysis is essential for complementing the research and development that has gone into creating a brand-new technology or any other form of innovation, facilitating the procurement and safeguarding of IP rights linked to the said innovation or technology and making strategic business decisions for the long-term.

Despite the hope that artificial intelligence (AI) might revolutionise the future of work, the general consensus so far seems to be that it has negative implications on the workforce. The TUC warned in March that workers could be "hired and fired by algorithm", while a recent Harvard Business School study revealed that the majority (88 per cent) of employers believe qualified applicants are filtered out by the screening software. Campaigners and policy makers are also questioning the impact that AI is having on the quality of work – whether it be algorithms that decide how much work app-based couriers receive, or automated performance monitoring pushing warehouse staff to forego toilet breaks in order to meet packing targets. The issue was summarised by a report from the Institute for the Future of Work, which said: "We find that it is not the replacement of humans by machines but the treatment of humans as machines" that defined the current era of work. Last week, a group of MPs decried the "growing body of evidence" pointing towards a "significant negative impact on the conditions and quality of work across the country" caused by the use of algorithms in the workplace.

Data plays a critical role in machine learning. Every machine learning model is trained and evaluated using data, quite often in the form of static datasets. The characteristics of these datasets fundamentally influence a model's behavior: a model is unlikely to perform well in the wild if its deployment context does not match its training or evaluation datasets, or if these datasets reflect unwanted societal biases. Mismatches like this can have especially severe consequences when machine learning models are used in high-stakes domains, such as criminal justice,1,13,24 hiring,19 critical infrastructure,11,21 and finance.18 Even in other domains, mismatches may lead to loss of revenue or public relations setbacks.

Liu Leqi is a Ph.D. student in the Machine Learning Department at Carnegie Mellon University, Pittsburgh, PA, USA. Her research interests include AI and human-centered problems in machine learning. Dylan Hadfield-Menell is an assistant professor of artificial intelligence and decision-making at the Massachusetts Institute of Technology, Cambridge, MA, USA. His recent work focuses on the risks of (over-) optimizing proxy metrics in AI systems. Zachary C. Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University, Pittsburgh, PA, USA, and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research spans core machine learning methods, applications to clinical medicine and NLP, and the impact of automation on social systems. He can be found on Twitter (@zacharylipton), GitHub (@zackchase), or his lab's website (acmilab.org).

A bill passed by the New York City council early this month aims to ban companies from using artificial intelligent-powered hiring tools that discriminate based on an applicant's gender or race. If signed into law, the legislation will require providers the technology to systems evaluated each year by an audit service and provide the results to companies using those systems. Employers using systems that do not meet requirements could be fined up to $1,500 per violation, but the law states it will be left up to the vendors to conduct the audits and show employers that their tools meet the city's requirements. If the bill is pushed to law, it would go into affect January 2023 and make New York City the first place in the US to rein in AI hiring tools. A bill passed by the New York City council early this month aims to ban companies from using artificial intelligent-powered hiring tools that discriminate based on an applicant's gender or race However, Alexandra Givens, president of the Center for Democracy & Technology, notes that this legislation does not protect against disabilities or age.

Job candidates rarely know when hidden artificial intelligence tools are rejecting their resumes or analyzing their video interviews. But New York City residents could soon get more say over the computers making behind-the-scenes decisions about their careers. A bill passed by the city council in early November would ban employers from using automated hiring tools unless a yearly bias audit can show they won't discriminate based on an applicant's race or gender. It would also force makers of those AI tools to disclose more about their opaque workings and give candidates the option of choosing an alternative process -- such as a human -- to review their application. Proponents liken it to another pioneering New York City rule that became a national standard-bearer earlier this century -- one that required chain restaurants to slap a calorie count on their menu items.

Artificial Intelligence has struck almost every domain and results are getting accurate day by day. We present a study of how AI is automating law and legal domain and what can be some of the future applications which can help the legal industry to automate their process. In the past, contract review auditors had two tasks that appeared to be in direct conflict with each other: to perform a contract review quickly and to conduct a thorough contract review. To balance these competing ends, auditors take representative samples from contracts rather than working on the entire data community. It's no surprise then that accounting departments and corporate finance institutions are turning to cognitive technologies to help Deliver more value and improve the bottom line of the organization.

Your manager is in Santa Clara, CA, but you'll be working remotely from anywhere in Canada. This position is not eligible to be performed in Alaska, Colorado, Mississippi, North Dakota, or the Virgin Islands. What you'll get to do... Our Domain Registrars and Investors team is looking for a software engineer to join us in building and improving Domain Search at GoDaddy. Domain Search is one of the key strategic pillars of GoDaddy, with an important contribution to the company's revenue, customer growth and innovation. As a software engineer, you would play a major role in improving the domain search experience, architecture, and analytics across every market we support worldwide, and the add-on products and advertising we offer when the user searches for a domain.