This paper deals with optimal policy learning (OPL) with observational data, i.e. data-driven optimal decision-making, in multi-action (or multi-arm) settings, where a finite set of decision options is available. It is organized in three parts, where I discuss respectively: estimation, risk preference, and potential failures. The first part provides a brief review of the key approaches to estimating the reward (or value) function and optimal policy within this context of analysis. Here, I delineate the identification assumptions and statistical properties related to offline optimal policy learning estimators. In the second part, I delve into the analysis of decision risk. This analysis reveals that the optimal choice can be influenced by the decision maker's attitude towards risks, specifically in terms of the trade-off between reward conditional mean and conditional variance. Here, I present an application of the proposed model to real data, illustrating that the average regret of a policy with multi-valued treatment is contingent on the decision-maker's attitude towards risk. The third part of the paper discusses the limitations of optimal data-driven decision-making by highlighting conditions under which decision-making can falter. This aspect is linked to the failure of the two fundamental assumptions essential for identifying the optimal choice: (i) overlapping, and (ii) unconfoundedness. Some conclusions end the paper.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Political ads featuring deceptive synthetic media would be required to include disclosure language under a bill passed Thursday by the New Hampshire House. Sophisticated artificial intelligence tools, such as voice-cloning software and image generators, already are in use in elections in the U.S. and around the world, leading to concerns about the rapid spread of misinformation. The New Hampshire State House, in Concord, New Hampshire, as photographed in April 2017.

Former Secretary of State Hillary Clinton described herself as a victim of election disinformation during a panel discussion on Thursday, and warned that the advancement of artificial intelligence (AI) will make her experience "look primitive." Clinton was taking part in a Columbia University event titled, "AI's Impact on the 2024 Global Elections." She discussed her own experience in 2016 when she lost to former President Donald Trump, pointing out that the internet was populated with memes, fake content and conspiracies about her in the lead-up to Election Day. "I don't think any of us understood it. I did not understand it. I can tell you, my campaign did not understand it. Their, you know, the so-called'Dark Web' was filled with these kinds of memes and stories and videos of all sorts…portraying me in all kinds of… less than flattering ways," Clinton said.

At the International Joint Conference on Artificial Intelligence (IJCAI) I was lucky enough to catch up with Francesca Rossi, IBM fellow and AI Ethics Global Leader, and President of AAAI. There were so many questions I wanted to ask, and we covered some pressing topics in AI today. Andrea Rafai: My first question concerns the UN Sustainable Development Goals (SDGs). It seems that there is a lot of potential for using AI in helping to work towards the 17 goals. What is your view on these goals and the long-term outlook?

It's been five months since President Joe Biden signed an executive order (EO) to address the rapid advancements in artificial intelligence. The White House is today taking another step forward in implementing the EO with a policy that aims to regulate the federal government's use of AI. Safeguards that the agencies must have in place include, among other things, ways to mitigate the risk of algorithmic bias. "I believe that all leaders from government, civil society and the private sector have a moral, ethical and societal duty to make sure that artificial intelligence is adopted and advanced in a way that protects the public from potential harm while ensuring everyone is able to enjoy its benefits," Vice President Kamala Harris told reporters on a press call. Harris announced three binding requirements under a new Office of Management and Budget (OMB) policy.

The US government issued new rules Thursday requiring more caution and transparency from federal agencies using artificial intelligence, saying they are needed to protect the public as AI rapidly advances. But the new policy also has provisions to encourage AI innovation in government agencies when the technology can be used for public good. The US hopes to emerge as an international leader with its new regime for government AI. Vice President Kamala Harris said during a news briefing ahead of the announcement that the administration plans for the policies to "serve as a model for global action." She said that the US "will continue to call on all nations to follow our lead and put the public interest first when it comes to government use of AI."

Istanbul, Turkey – As nationwide local elections approach on March 31, there are concerns in Turkey about the growing threat of disinformation and fake media created through artificial intelligence. Earlier this year, a video spread across social media purportedly showing Istanbul's opposition mayor praising President Recep Tayyip Erdogan's ruling Justice and Development Party (AK Party). Ekrem Imamoglu, of the Republican People's Party (CHP), is seen in the video commending the "great steps" taken in public transport projects when the AK Party controlled Istanbul. While the video was widely discredited due to the substance of Imamoglu's "comments", it raised fears about media manipulation in an election where the AK Party is trying to retake cities won by the opposition in 2019. Political scandals over "leaked" recordings are nothing new in Turkey.

Within minutes of walking through an Israeli military checkpoint along the Gaza Strip's central highway on Nov. 19, Palestinian poet Mosab Abu Toha was asked to step out of the crowd. He put down his 3-year-old son, whom he was carrying, and sat in front of a military jeep. Half an hour later, Abu Toha heard his name called. Then he was blindfolded and led away for interrogation. "I had no idea what was happening or how they could suddenly know my full legal name," said the 31-year-old, who added that he had no ties to the militant group Hamas and had been trying to leave Gaza for Egypt.

Hate speech detection models are only as good as the data they are trained on. Datasets sourced from social media suffer from systematic gaps and biases, leading to unreliable models with simplistic decision boundaries. Adversarial datasets, collected by exploiting model weaknesses, promise to fix this problem. However, adversarial data collection can be slow and costly, and individual annotators have limited creativity. In this paper, we introduce GAHD, a new German Adversarial Hate speech Dataset comprising ca.\ 11k examples. During data collection, we explore new strategies for supporting annotators, to create more diverse adversarial examples more efficiently and provide a manual analysis of annotator disagreements for each strategy. Our experiments show that the resulting dataset is challenging even for state-of-the-art hate speech detection models, and that training on GAHD clearly improves model robustness. Further, we find that mixing multiple support strategies is most advantageous. We make GAHD publicly available at https://github.com/jagol/gahd.

The Space Domain Awareness (SDA) community routinely tracks satellites in orbit by fitting an orbital state to observations made by the Space Surveillance Network (SSN). In order to fit such orbits, an accurate model of the forces that are acting on the satellite is required. Over the past several decades, high-quality, physics-based models have been developed for satellite state estimation and propagation. These models are exceedingly good at estimating and propagating orbital states for non-maneuvering satellites; however, there are several classes of anomalous accelerations that a satellite might experience which are not well-modeled, such as satellites that use low-thrust electric propulsion to modify their orbit. Physics-Informed Neural Networks (PINNs) are a valuable tool for these classes of satellites as they combine physics models with Deep Neural Networks (DNNs), which are highly expressive and versatile function approximators. By combining a physics model with a DNN, the machine learning model need not learn astrodynamics, which results in more efficient and effective utilization of machine learning resources. This paper details the application of PINNs to estimate the orbital state and a continuous, low-amplitude anomalous acceleration profile for satellites. The PINN is trained to learn the unknown acceleration by minimizing the mean square error of observations. We evaluate the performance of pure physics models with PINNs in terms of their observation residuals and their propagation accuracy beyond the fit span of the observations. For a two-day simulation of a GEO satellite using an unmodeled acceleration profile on the order of $10^{-8} \text{ km/s}^2$, the PINN outperformed the best-fit physics model by orders of magnitude for both observation residuals (123 arcsec vs 1.00 arcsec) as well as propagation accuracy (3860 km vs 164 km after five days).