Algorithmic systems such as search engines and information retrieval platforms significantly influence academic visibility and the dissemination of knowledge. Despite assumptions of neutrality, these systems can reproduce or reinforce societal biases, including those related to gender. This paper introduces and applies a bias-preserving definition of algorithmic gender fairness, which assesses whether algorithmic outputs reflect real-world gender distributions without introducing or amplifying disparities. Using a heterogeneous dataset of academic profiles from German universities and universities of applied sciences, we analyse gender differences in metadata completeness, publication retrieval in academic databases, and visibility in Google search results. While we observe no overt algorithmic discrimination, our findings reveal subtle but consistent imbalances: male professors are associated with a greater number of search results and more aligned publication records, while female professors display higher variability in digital visibility. These patterns reflect the interplay between platform algorithms, institutional curation, and individual self-presentation. Our study highlights the need for fairness evaluations that account for both technical performance and representational equality in digital systems.

This week in tech: Elon Musk and Donald Trump flood the zone and deploy brinkmanship as a negotiating tactic; US Immigration and Customs Enforcement learns search engine optimization amid arrests and deportations; and Spotify tries to soften its algorithmic image with human-centric public relations. Donald Trump has issued a record number of executive orders since his presidency began: ending birthright citizenship, banning gender transitions for anyone under 19, pardoning the rioters of the January 6 attack, and more. Elon Musk, the world's richest man in charge of the "department of government efficiency", has raided an equally dizzying swath of federal agencies with the stated goal of "slashing waste, fraud, and abuse". Among the half-dozen bureaus are the Consumer Financial Protection Bureau, Department of Education, Department of Labor and, most viciously, the US Agency for International Development (USAid). Trump and Musk are doing their utmost to "flood the zone" – a tactic that the former Trump administration strategist Steve Bannon has touted as one that will purposefully overwhelm the opposition and the media.

Investors are interested in predicting future success of startup companies, preferably using publicly available data which can be gathered using free online sources. Using public-only data has been shown to work, but there is still much room for improvement. Two of the best performing prediction experiments use 17 and 49 features respectively, mostly numeric and categorical in nature. In this paper, we significantly expand and diversify both the sources and the number of features (to 171) to achieve better prediction. Data collected from Crunchbase, the Google Search API, and Twitter (now X) are used to predict whether a company will raise a round of funding within a fixed time horizon. Much of the new features are textual and the Twitter subset include linguistic metrics such as measures of passive voice and parts-of-speech. A total of ten machine learning models are also evaluated for best performance. The adaptable model can be used to predict funding 1-5 years into the future, with a variable cutoff threshold to favor either precision or recall. Prediction with comparable assumptions generally achieves F scores above 0.730 which outperforms previous attempts in the literature (0.531), and does so with fewer examples. Furthermore, we find that the vast majority of the performance impact comes from the top 18 of 171 features which are mostly generic company observations, including the best performing individual feature which is the free-form text description of the company.

The spread of misinformation is a massive problem online, and generative AI is only helping boost the creation of inauthentic or real-but-repurposed media. Even in the pre-generative-AI era, an image surfaced through a quick Google search might have been used out of context or attached to a less-than-reliable website. Google believes it has at least one solution for this problem. In Google image search results, users will start seeing an information box called "About this image." It rolls out today in the US (and initially only in English).

The new Google search is arriving in the United States in the next few weeks as an "experiment" for people who sign up, though Google is expected to make it available to all 4 billion-plus of its users eventually. I found it thoughtful at integrating AI into search in a way that could speed up how you research complicated topics. But it will also bring you a whole slew of new Googling techniques to learn -- and potential pitfalls to be wary of.

In recent months, we observed an increase in the number of malicious campaigns that use Google Advertising as a means of distributing and delivering malware. At least two different stealers, Rhadamanthys and RedLine, were abusing the search engine promotion plan in order to deliver malicious payloads to victims' machines. They seem to use the same technique of mimicking a website associated with well-known software like Notepad and Blender 3D. The treat actors create copies of legit software websites while employing typosquatting (exploiting incorrectly spelled popular brands and company names as URLs) or combosquatting (using popular brands and company names combined with arbitrary words as URLs) to make the sites look like the real thing to the end user--the domain names allude to the original software or vendor. The design and the content of the fake web pages look the same as those of the original ones.

'Gutfeld' guests discuss reports a Google software engineer was suspended for saying artificial intelligence is sentient. Google is a divisive company. Maybe you love the ease of Gmail and how easy it is to find anything you want online. Or maybe you get a shiver up your spine thinking of all the data the search giant has on you. You might see a few personal details when you search for yourself online.

Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average.

President Donald Trump is talking about the Iowa college student that was found slain about a month after she disappeared, despite the victim's family asking that her death not be politicized. President Donald Trump speaks during a rally in Charleston, W.Va. Tuesday. WASHINGTON – White House economic adviser Larry Kudlow said Tuesday that President Donald Trump is considering new regulations on Google's search engine to address his concern that it turns up too many stories that are critical of him. Pressed by reporters at the White House on Tuesday about a tweet the president wrote criticizing Google's search engine as "rigged," the director of Trump's National Economic Council said the administration is "taking a look" at federal regulations for the company. "We'll let you know," he said.

Michael Li is founder and CEO at The Data Incubator. The company offers curriculum based on feedback from corporate and government partners about the technologies they are using and learning, for masters and PhDs. Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average.