The US Federal Trade Commission (FTC) warns of extortion scammers targeting the LGBTQ community via online dating apps such as Grindr and Feeld. As the FTC revealed, the fraudsters would pose as potential romantic partners on LGBTQ dating apps, sending explicit photos and asking their targets to reciprocate. If they fall for the scammers' tricks, the victims will be blackmailed to pay a ransom, usually in gift cards, under the threat of leaking the shared sexual imagery with their family, friends, or employers. "To make their threats more credible, these scammers will tell you the names of exactly who they plan to contact if you don't pay up. This is information scammers can find online by using your phone number or your social media profile," the FTC said.

An Israeli defense contractor on Monday unveiled a remote-controlled armed robot it says can patrol battle zones, track infiltrators and open fire. The unmanned vehicle is the latest addition to the world of drone technology, which is rapidly reshaping the modern battlefield. Proponents say such semi-autonomous machines allow armies to protect their soldiers, while critics fear this marks another dangerous step toward robots making life-or-death decisions. The four-wheel-drive robot presented Monday was developed by the state-owned Israel Aerospace Industries' "REX MKII." It is operated by an electronic tablet and can be equipped with two machine guns, cameras and sensors, said Rani Avni, deputy head of the company's autonomous systems division.

I went to my first in person meeting since February 2020 last week! How to be? Wonderful and scary!) It was an enjoyable Responsible Tech Meet Up hosted by Cennydd Bowles and inspired by the All Tech Is Human meet ups held elsewhere. It was great and depressing to hear the inside stories from folks at the heart of trying to make tech responsible and accountable, the problems they face and their indefatigable efforts to create change. We got talking about'What Worked' to persuade companies and governments to change to more'responsible' behaviour.

If someone were to describe a person of colour as an animal, their comments would be rightly called out as racist. When artificial intelligence does the same thing, however, the creators of that AI are careful to avoid using the "r" word. Earlier this month, a video on Facebook featuring a number of black men ended with a prompt asking the viewer if they wanted to "keep seeing videos about Primates". Facebook's subsequent apology described the caption as an "error" which was "unacceptable". An ever-growing catalogue of algorithmic bias against people of colour is referred to by the offending companies using increasingly familiar language: "problematic", "unfair", a "glitch" or an "oversight".

In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. As the latter is known to exhibit a heavy-tail distribution, an algorithm is normally stopped when exceeding a predefined upper time limit. As a consequence, machine learning methods used to optimize an algorithm selection strategy in a data-driven manner need to deal with right-censored samples, a problem that has received little attention in the literature so far. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. Moreover, we adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon. In an extensive experimental evaluation on an adapted version of the ASlib benchmark, we demonstrate that theoretically well-founded methods based on Thompson sampling perform specifically strong and improve in comparison to existing methods.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Why Explainable #AI need Designers and Data Scientists with Question-Driven User-Centered Design? XAI applications must be developed based on what the user needs, making AI understandable by the user. Who needs explainability and for what? The short answer might be "anyone who comes in contact with AI". Model developers, to improve or debug the model.

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assess the ethical capabilities of AI systems and provide best practices for further improvements.

Within the national innovation system literature, empirical analyses are severely lacking for developing economies. Particularly, the low- and middle-income countries (LMICs) eligible for the World Bank's International Development Association (IDA) support, are rarely part of any empirical discourse on growth, development, and innovation. One major issue hindering panel analyses in LMICs, and thus them being subject to any empirical discussion, is the lack of complete data availability. This work offers a new complete panel dataset with no missing values for LMICs eligible for IDA's support. I use a standard, widely respected multiple imputation technique (specifically, Predictive Mean Matching) developed by Rubin (1987). This technique respects the structure of multivariate continuous panel data at the country level. I employ this technique to create a large dataset consisting of many variables drawn from publicly available established sources. These variables, in turn, capture six crucial country-level capacities: technological capacity, financial capacity, human capital capacity, infrastructural capacity, public policy capacity, and social capacity. Such capacities are part and parcel of the National Absorptive Capacity Systems (NACS). The dataset (MSK dataset) thus produced contains data on 47 variables for 82 LMICs between 2005 and 2019. The dataset has passed a quality and reliability check and can thus be used for comparative analyses of national absorptive capacities and development, transition, and convergence analyses among LMICs.

I had the pleasure of talking to Bogdan Grigorescu, Head of Quality Assurance at Afiniti, about the ethics and biases in Artificial Intelligence (AI). AI-enabled technology has many use cases. In the military, the same type of technology is used to detect war crimes by analyzing the images taken in the field of banned ordnance prohibited by international conventions. Pictures and images can be taken with great details and then compared and analyzed. But for individuals, the main question arising is: what happens with my data?

Professor Bennett Moses has written about the limitations of AI and data-driven approaches to decision-making in government, law enforcement and …