Explainability techniques are currently developed and incorporated by machine learning engineers, and not surprisingly, their needs (and companies' desire to avoid legal trouble) are being prioritized.Realizing a broader set of XAI objectives will require both greater awareness of their existence and a shift in incentives for accomplishing them. XAI standards and policy guidelines should explicitly include the needs of users, stakeholders, and impacted communities to incentivize this shift. Explainability case studies are one pedagogical tool that can help practitioners and educators understand and develop more holistic explainability strategies. Diverse organizational guidance documents, recommendations, and high-level frameworks can also help guide an organizations' executives and/or developers through key questions to support explainability that is useful and relevant to different stakeholders. While there has been some work done to evaluate AI explanations, most attempts are either computationally expensive or only focus on a small subset of what constitutes a "good explanation" and fail to capture other dimensions.

The Epic v. Apple trial may have come to an end, but it certainly won't be the last of large, attention-grabbing antitrust suits in the video games industry: Sony is facing a federal antitrust class-action suit in California filed May 7 for allegedly overcharging on PlayStation 5 games. That case could take years to make it through court to be potentially decided by a jury. The plaintiff's lawyer, Joseph Saveri, said on the phone, "We think this is a case involving serious anticompetitive activity and economic harm.

ReFED is a national nonprofit working to end food loss and waste across the food system by advancing data-driven solutions to the problem. ReFED leverages data and insights to highlight supply chain inefficiencies and economic opportunities; mobilizes and connects supporters to take targeted action; and catalyzes capital to spur innovation and scale high-impact initiatives. Starting with the 2016 Roadmap to Reduce U.S. Food Waste, ReFED has developed a trusted history of producing first-of-their-kind tools and resources, providing a full-supply-chain picture of U.S. food waste, cost-effective solutions to reduce it, and methods to track progress. In February 2021, ReFED launched its new Roadmap to 2030 and Insights Engine, an online data center designed to serve as the next generation of data, insights, and guidance on U.S. food waste reduction. Solving this problem will have a significant impact on mitigating climate change, optimizing use of water, land, and other resources, and providing meals for the over 50 million people in the United States who currently face food insecurity.

While the working definitions of "artificial intelligence," "machine learning," and their surrounding concepts can be squishy at times, there's no doubt that AI has arrived in the law in concrete ways -- already streamlining some of the industry's' most mundane tasks. In this edition of the Non-Eventcast, host Jared Correia talks with Nathan Wenzel of SimpleLegal and Alex Smith of iManage to nail down just what "artificial intelligence" refers to, specific ways it's being used in the law, and what lawyers should be doing now to capitalize. But the existing practical uses for AI may also surprise you.) And while you're here, feel free to browse prior editions of the Non-Eventcast in the Law Practice Management Software, Legal Document Management Software, and Legal Operations Contract Lifecycle Management rooms of the Non-Event. We will never sell or share your information without your consent.

Confidential VMs allow tenants to have a fully backward-compatible VM experience running existing unmodified applications. In the background, systems record and check attestations to verify the security guarantees and make them auditable. Placing entire VMs in TEEs is important for fast and easy adoption, but it also causes some problems. For example, the administrator for the VM has full read/write control over the VM, which is too coarse in many cases. Another concern is that the TCB for a VM is large: a VM image is far more than just a kernel and an application; it includes a large number of system services.

Are you looking for a rewarding career in tech where you can work with dedicated, inspiring people? As the only independent omni-channel advertising and analytics platform built for television, Innovid is looking for the best and brightest to help us in our mission to use data to enable the personalization, delivery, and measurement of ads across TV, video, display, social, and OOH. Our award-winning culture is less about the perks (although we have many), or the office environment (which we're especially proud of), but about the people that make it possible. Innovid serves a global client base of brands, agencies, and publishers through over twelve offices across the Americas, Europe, and Asia Pacific. Please visit www.innovid.com for more information. We are seeking an exceptional and self-motivated Data Scientist to join our growing Data Science team within the advertising measurement group. You will have opportunities to contribute to our data strategy, have an impact on the industry, and work on a large data set in a large market share of the CTV space. What You Will Do: Applying advanced machine learning, data mining, statistical methods, algorithms, and time series models to advertising measurement, insight and optimization. Identifying opportunities to derive valuable insights from internal ad serving data, device graphs, commercial and public datasets. Quantifying statistical distributions, using a variety of techniques. Building high quality production software to power the delivery of algorithmic insights to clients. Establishing objectives; collaborating on architecture, design and implementation; and communicating results with Innovid's Product, Engineering and Data Science teams, including the larger Measurement Team. What You Will Need: A Bachelor of Science with a focus on Machine Learning or a MS in computer science, statistics, mathematics, or applied sciences with a strong background in machine learning. High proficiency with Python and machine learning packages such as Sklearn, Numpy, or TensorFlow/Keras. Experience working within the JVM (Java Virtual Machine) and the Unix environment. Proficiency working with Spark in a Scala environment is a strong plus. Familiarity working with a variety of SQL databases, especially on OLAP analytical databases. Must be a highly motivated self-starter who is able to achieve results with minimal direction. Outstanding written and verbal communication skills. Strong communication and organizational skills and must be detail-oriented; demonstrated history of working with project teams on tight deadlines. Resourceful problem solver with strong interpersonal skills and the ability to work as a team player as well as provide creative out-of-the-box solutions and approaches. High level of maturity, drive and flexibility to work in a rapidly developing environment. A solid understanding of the advertising ecosystem, including DSPs, SSPs, and ad serving is preferred. What We Offer: High visibility role with a tremendous amount of growth potential Competitive compensation package, which includes: health, dental, and vision insurance, life insurance, PTO + Sick Days, 401K + match, a volunteer program Offices in major cities around the world and a cross-company collaboration unlike anywhere else. Remote flexibility through 2022 for COVID. There is no such thing as the perfect resume, or someone that checks every box. At Innovid, we are generous with our time and knowledge, and always ready to teach. So however you identify and whatever background you bring with you, please apply if this is a role that would make you excited to come into work every day and add to Innovid. Equal Opportunity Employer: Innovid is an equal opportunity employer, committed to our diversity and inclusiveness. We consider all qualified applicants regardless of race, color, nationality, gender, gender identity or expression, sexual orientation, religion, disability or age. We strongly encourage women, people of color, members of the LGBTQIA community, people with disabilities and veterans to apply. We are actively working to be an anti-racist organization. We're committing to creating an inclusive and equitable workplace for all of our employees. You can read more about our commitment to DEI here . If you are located within the EEA and subject to GDPR or are a California resident subject to the California Consumer Privacy Act, click here to understand how Innovid processes your personal information and how you can exercise your rights.

On May 19, the European Parliament adopted a resolution on artificial intelligence. In parallel to the announcement of the new regulation of the European Commission on AI, this text will intervene in the fields of education, culture and audiovisual. These three areas are very regularly subject to innovations where AI is exploited. In the first part of this resolution, the European Union underlines the strategic importance of AI and its associated technologies: by focusing these innovations on humans, based on ethics and human rights. The objective is for AI to become a real tool at the service of the European population, with an ethical dimension from its conception.

Meet Moxie, a companion robot made specifically for children to play with every day. As Devindra Hardawar notes, the idea of an R2-D2 of your own is interesting, until it gets dystopian. So which side of the line does this bot fall? According to company co-founder Paolo Pirjanian, two months after launch, customers are averaging 25 minutes of engagement every day -- although that the company knows that isn't exactly encouraging. Devindra's kid is (wisely) robot-averse, so he tried it himself for a few weeks and was surprised by how well the conversations flowed.

Providers and users of artificial intelligence (AI) systems will face new regulation driven by the risk these systems pose to people under plans outlined by the European Commission. Some AI systems will be completely banned from sale or use in the EU. According to the Artificial Intelligence Act (AI Act), AI that poses an "unacceptable risk" to people will be banned. The proposal defines "unacceptable risk" as a set of particularly harmful uses of AI that contravene EU values because they violate fundamental rights (e.g., social scoring by governments, exploitation of children's vulnerabilities, use of subliminal techniques, and – with limited exceptions – remote live biometric identification systems in publicly accessible spaces used for law enforcement) The proposal then defines a limited number of AI systems as'high risk' because they negatively impact the security of individuals or their fundamental rights. Examples of "high risk" AI include that constitutes or is part of the machinery, medical devices, and vehicles used to manage critical infrastructures such as the management and operation of road traffic and the provision of water, gas, heating, and electricity, or for biometric identification and categorisation of people.

Every person engaged with the networked world constantly creates rivers of data. We do this in ways we are aware of, and ways that we aren't. Corporations are eager to take advantage. Take, for instance, NumberEight, a startup, that, according to Wired, "helps apps infer user activity based on data from a smartphone's sensors: whether they're running or seated, near a park or museum, driving or riding a train." New services based on such technology, "will combine what they know about a user's activity on their own apps with information on what they're doing physically at the time."