The quality of Artificial Intelligence (AI) algorithms is of significant importance for confidently adopting algorithms in various applications such as cybersecurity, healthcare, and autonomous driving. This work presents a principled framework of using a design-of-experimental approach to systematically evaluate the quality of AI algorithms, named as Do-AIQ. Specifically, we focus on investigating the quality of the AI mislabel data algorithm against data poisoning. The performance of AI algorithms is affected by hyperparameters in the algorithm and data quality, particularly, data mislabeling, class imbalance, and data types. To evaluate the quality of the AI algorithms and obtain a trustworthy assessment on the quality of the algorithms, we establish a design-of-experiment framework to construct an efficient space-filling design in a high-dimensional constraint space and develop an effective surrogate model using additive Gaussian process to enable the emulation of the quality of AI algorithms. Both theoretical and numerical studies are conducted to justify the merits of the proposed framework. The proposed framework can set an exemplar for AI algorithm to enhance the AI assurance of robustness, reproducibility, and transparency.

Airlines are critical today for carrying people and commodities on time. Any delay in the schedule of these planes can potentially disrupt the business and trade of thousands of employees at any given time. Therefore, precise flight delay prediction is beneficial for the aviation industry and passenger travel. Recent research has focused on using artificial intelligence algorithms to predict the possibility of flight delays. Earlier prediction algorithms were designed for a specific air route or airfield. Many present flight delay prediction algorithms rely on tiny samples and are challenging to understand, allowing almost no room for machine learning implementation. This research study develops a flight delay prediction system by analyzing data from domestic flights inside the United States of America. The proposed models learn about the factors that cause flight delays and cancellations and the link between departure and arrival delays.

3D Point cloud is becoming a critical data representation in many real-world applications like autonomous driving, robotics, and medical imaging. Although the success of deep learning further accelerates the adoption of 3D point clouds in the physical world, deep learning is notorious for its vulnerability to adversarial attacks. In this work, we first identify that the state-of-the-art empirical defense, adversarial training, has a major limitation in applying to 3D point cloud models due to gradient obfuscation. We further propose PointDP, a purification strategy that leverages diffusion models to defend against 3D adversarial attacks. We extensively evaluate PointDP on six representative 3D point cloud architectures, and leverage 10+ strong and adaptive attacks to demonstrate its lower-bound robustness. Our evaluation shows that PointDP achieves significantly better robustness than state-of-the-art purification methods under strong attacks. Results of certified defenses on randomized smoothing combined with PointDP will be included in the near future.

Designing and implementing explainable systems is seen as the next step towards increasing user trust in, acceptance of and reliance on Artificial Intelligence (AI) systems. While explaining choices made by black-box algorithms such as machine learning and deep learning has occupied most of the limelight, systems that attempt to explain decisions (even simple ones) in the context of social choice are steadily catching up. In this paper, we provide a comprehensive survey of explainability in mechanism design, a domain characterized by economically motivated agents and often having no single choice that maximizes all individual utility functions. We discuss the main properties and goals of explainability in mechanism design, distinguishing them from those of Explainable AI in general. This discussion is followed by a thorough review of the challenges one may face when working on Explainable Mechanism Design and propose a few solution concepts to those.

The value alignment problem is concerned with the design of systems that provably abide by our human values. One approach to this challenge is through the leverage of prescriptive norms that, if carefully designed, are able to steer a multiagent system away from harmful outcomes and towards more beneficial ones. In this work, we first present a general methodology for the automated synthesis of value aligned normative systems, based on a consequentialist view of values. In the second part, we provide analytical tools to examine such value aligned normative systems, namely the Shapley value of individual norms and the compatibility of several values under a fixed set of norms. We illustrate all of our contributions with a running example of a society of agents where taxes are collected and redistributed according to a set of parametrised norms.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! You are a walking data repository. While outside your residence or vehicle, walking down a street, shopping in a store, or visiting any type of public event or meeting -- you potentially lose your personal privacy and cross the boundary from being a private individual to a virtual public figure. You can be filmed or photographed, your image can be transported to a storage silo anywhere in the world, your voice can be recorded, and your time in public view can be noted.

A drone has been shot down over the headquarters of Russia's Black Sea Fleet in annexed Crimea, a local official said, in the second attempted strike on the command in Sevastopol in less than a month. "The drone was shot down just above the fleet headquarters" in the city of Sevastopol, city Governor Mikhail Razvojaev wrote on Telegram on Saturday, blaming the attempt on Ukrainian forces. "It fell on the roof and caught fire," he said, adding that there was no significant damage or victim. The first reported attack came on July 31, when a presumed Ukrainian drone attacked the Black Sea Fleet on Russia's Navy Day, wounding five people. Russia also reported Ukrainian drone attacks late on Friday.

AI and machine learning are useful tools in helping organizations implement more efficient, unbiased and effective hiring processes. AI can free human recruiters (who often spend 40 percent of their time sorting resumes) to do more high value tasks, like building relationships with candidates, and streamline and automate interview scheduling, candidate screening, and measure specific recruitment KPIs. Critically, AI algorithms used in the hiring process must be trained on diverse historical data representative of real-world populations to ensure that bias is not being perpetuated. Incorporating input and perspectives from various teams and individuals within the company, such as recruiters, data scientists, subject matter experts, and managers, is key to developing algorithms that aren't governed by partiality. AI models should also undergo rigorous testing prior to production, and be continuously evaluated and retrained over time.

Sign up to receive the Future Tense newsletter every other Saturday. As the editor of Future Tense, I have a few rules for writers. Chief among them: You're not allowed to open with a scary or utopian scenario and then write, "It sounds like science fiction, but …" And you need a very good reason to reference some of the biggest works of science fiction--1984, Brave New World, Gattaca, Minority Report, Terminator. Also, please avoid references to the Silicon Valley ethos of "move fast and break things." But if I don't, every article starts to sound the same: dystopian.

Andy Grove (Intel's business leader until 2004) termed "software spiral" the exceptionally resilient business model behind general-purpose CPUs. Application software is the defining component of SWS: Code written once could yet benefit from performance scaling of later CPU generations. SWS is comprised of several abstraction levels. The random access machine, or model (RAM) is most relevant for the current Counterpoint Viewpoint (CPV): each serial step of an algorithm features a basic operation taking unit time ("uniform cost" criterion). The RAM has long been the gold standard for algorithms and data structures.