In the current era, people and society have grown increasingly reliant on artificial intelligence (AI) technologies. AI has the potential to drive us towards a future in which all of humanity flourishes. It also comes with substantial risks for oppression and calamity. Discussions about whether we should (re)trust AI have repeatedly emerged in recent years and in many quarters, including industry, academia, healthcare, services, and so on. Technologists and AI researchers have a responsibility to develop trustworthy AI systems. They have responded with great effort to design more responsible AI algorithms. However, existing technical solutions are narrow in scope and have been primarily directed towards algorithms for scoring or classification tasks, with an emphasis on fairness and unwanted bias. To build long-lasting trust between AI and human beings, we argue that the key is to think beyond algorithmic fairness and connect major aspects of AI that potentially cause AI’s indifferent behavior. In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives. We further discuss how to leverage this framework to improve societal well-being through protection, information, and prevention/mitigation. This article appears in the special track on AI & Society.

The development of Artificial Intelligence has been one of the most impactful innovations in the past couple of years. It has and will continue to have significant transformational impact on technology as well as humans. With massive amount of data, AI enables machine to become smarter and more intelligent over time and perform tasks on their own with little or without intervention. Analyzing and responding to cyber threat is not human scalable problem. Applying AI to Security tools and techniques are critical in helping to detect and respond to security issues faster, saving costs and greatly reducing risk.

After NASA's Perseverance rover came up empty in its attempt to collect rock samples from Mars earlier this month, it's ready for another go-round. The US space agency said on Thursday that the rover will abrade, or scrape, a rock nicknamed'Rochelle' with a tool on its robotic arm. By scraping the rock, it will let researchers see inside to see if it's worth taking a sample, which would'slightly thicker than a pencil,' NASA wrote in a statement. After NASA's Perseverance rover came up empty in its attempt to collect rock samples from Mars earlier this month, it's ready for another go-round. The US space agency said on Thursday that the rover will abrade, or scrape, a rock nicknamed'Rochelle' (pictured) with a tool on its robotic arm If the team decides the rock is good to go, the sampling process would start next week.

With the 2020 US Census results having been delivered to the states, now the process begins for using the population results to draw new Congressional districts. Gerrymandering, a practice intended to establish a political advantage by manipulating the boundaries of electoral districts, is expected to be practiced on a wide scale with Democrats having a slight margin of seats in the House of Representatives and Republicans seeking to close the gap in states where they hold a majority in the legislature. Today, more powerful redistricting software incorporating AI and machine learning is available, and it represents a double-edged sword. The pessimistic view is that the gerrymandering software will enable legislators to gerrymander with more precision than ever before, to ensure maximum advantages. This was called "political laser surgery" by David Thornburgh, president of the Committee of Seventy, an anti-corruption organization that considers the 2010 redistricting as one of the worst in the country's history, according to an account in the Columbia Political Review.

Chuck Brooks, president of Brooks Consulting, globally recognized as a subject-matter expert on Cybersecurity and Emerging Technologies, sees the coming proliferation of IoT devices as expanding the threat landscape. His experience helps to put it in perspective. In government, he has received two Presidential appointments, by George W. Bush to a legislative position at the Department of Homeland Security, and by Ronald Reagan as an assistant to the director of Voice of America. In industry, Chuck has served in executive roles for General Dynamics, Xerox, Rapiscan Systems, and SRA. Today, Chuck is on the Adjunct Faculty at Georgetown University's Graduate Applied Intelligence Program and the Graduate Cybersecurity Program, where he teaches courses on risk management, homeland security, and cybersecurity. He has an MA in International relations from the University of Chicago, a BA in Political Science from DePauw University, and a Certificate in International Law from The Hague Academy of International Law. He recently spent a few minutes with AI Trends Editor John P. Desmond to discuss the state of cybersecurity today.

The Japan Air Self Defense Force (JASDF) had to scramble fighter jets three times over the week to monitor Chinese drones that flew over the East China Sea and the strategic Miyako Strait that opens to the Philippine Sea and the broader Western Pacific Ocean. A People's Liberation Army Tengoen TB-001 Scorpion medium-altitude, long-endurance (MALE) drone flew into the East China Sea northwest of Okinawa Tuesday, prompting JASDF to send fighters to investigate its activities, reported The Drive. A PLA Harbin BZK-005 MALE drone then flew a sortie back and forth through the Miyako Strait Wednesday, followed by another TB-001 through Miyako Strait, which lies southwest of the island of Okinawa, on Thursday. According to the Japanese officials, one Shaanxi Y-8Q maritime patrol plane and one Shaanxi Y-9JB electronic intelligence aircraft accompanied the drones on their flights the last two days. This comes as a testament to PLA's growing unmanned aircraft capabilities and its focus on deploying increasingly sophisticated unmanned aerial vehicles.

The Artificial intelligence will appear as a game changer for military. AI integration will advance the military operations by many ways. By all means, this includes administration, training, organization, personal assistance as well as daily activities. The Military-AI-System will bring an evolution. Consequently, this will cause enhancement in the planning of logical and efficient battle plans.

One of our customers is a German Automotive Company. We help the company steer towards a data-driven, AI-led company, by implementing and maintaining ML models on their platform. Their main objectives are data democratization and the culture of self service. We're talking about data from mileage, speed, consumption, RPM, engine faults, driver behavior, road driving patterns and car components that we need to process and carefully tailor for the machine learning models. To accomplish that, we're using S3, Glue, Lambda, Kinesis, Athena, Codepipeline, CLI, PySpark and Terraform together with AWS SageMaker for the machine learning solutions.

We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent  by seeds and spreading through the network according to the independent cascade model.  We provide a comprehensive theoretical study of the election control problem, investigating  two forms of manipulations: seeding to buy influencers given a social network and removing  or adding edges in the social network given the set of the seeds and the information sent.  In particular, we study a wide range of cases distinguishing in the number of candidates or  the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in  the worst-case, even when one accepts to get an approximation of the optimal margin of  victory, except for the case of seeding when the number of hard-to-manipulate voters is not  too large, and the number of uncertain voters is not too small, where we say that a voter  that does not vote for the manipulator's candidate is hard-to-manipulate if there is no way  to make her vote for this candidate, and uncertain otherwise. We also provide some results showing the hardness of the problems in special cases.  More precisely, in the case of seeding, we show that the manipulation is hard even if the  graph is a line and that a large class of algorithms, including most of the approaches  recently adopted for social-influence problems (e.g., greedy, degree centrality, PageRank, VoteRank), fails to compute a bounded approximation even on elementary networks, such  as undirected graphs with every node having a degree at most two or directed trees. In the  case of edge removal or addition, our hardness results also apply to election manipulation  when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary  number of edges, and to the basic case of social influence maximization/minimization in  the restricted case of finite budget. Interestingly, our hardness results for seeding and edge removal/addition still hold  in a re-optimization variant, where the manipulator already knows an optimal solution  to the problem and computes a new solution once a local modification occurs, e.g., the  removal/addition of a single edge.

Rule-based IDS (intrusion detection systems) are being replaced by more robust neural IDS, which demonstrate great potential in the field of Cybersecurity. However, these ML approaches continue to rely on ad-hoc feature engineering techniques, which lack the capacity to vectorize inputs in ways that are fully relevant to the discovery of anomalous cyber activity. We propose a deep end-to-end framework with NLP-inspired components for identifying potentially malicious behaviors on enterprise computer networks. We also demonstrate the efficacy of this technique on the recently released DARPA OpTC data set.