Facebook has worked for years to revamp its handling of political ads -- but researchers who conducted a comprehensive audit of millions of ads say the social media company's efforts have had uneven results. The problems, they say, include overcounting political ads in the U.S. -- and undercounting them in other countries. And despite Facebook's ban on political ads around the time of last year's U.S. elections, the platform allowed more than 70,000 political ads to run anyway, according to the research team that is based at the NYU Cybersecurity for Democracy and at the Belgian university KU Leuven. Their research study was released early Thursday. They also plan to present their findings at a security conference next August.
For children with autism spectrum disorder (ASD), receiving an early diagnosis can make a huge difference in improving behavior, skills and language development. There is no lab test and no single identified genetic cause--instead, clinicians look at the child's behavior and conduct structured interviews with the child's caregivers based on questionnaires. But these questionnaires are extensive, complicated and not foolproof. "In trying to discern and stratify a complex condition such as autism spectrum disorder, knowing what questions to ask and in what order becomes challenging," said USC University Professor Shrikanth Narayanan, Niki and Max Nikias Chair in Engineering and professor of electrical and computer engineering, computer science, linguistics, psychology, pediatrics and otolaryngology. "As such, this system is difficult to administer and can produce false positives, or confound ASD as other comorbid conditions, such as attention deficit hyperactivity disorder (ADHD)."
In probability, Bayes is a type of conditional probability. It predicts the event based on an event that has already happened. You can use Naive Bayes as a supervised machine learning method for predicting the event based on the evidence present in your dataset. In this tutorial, you will learn how to classify the email as spam or not using the Naive Bayes Classifier. Before doing coding demonstration, Let's know about the Naive Bayes in a brief.
Donald Trump and his former White House chief of staff Mark Meadows are peddling a new story about the ex-president's coronavirus infection. Their first story was that Trump didn't test positive until Oct. 1, 2020, two days after he debated Joe Biden. Then Meadows admitted in his new book, The Chief's Chief, that Trump actually tested positive on Sept. 26, three days before the debate. That admission was problematic, since Trump never informed Biden--or hundreds of other unwitting people who interacted closely with the maskless president in the intervening five days--about the test result. So now Trump and Meadows have concocted yet another story: The Sept. 26 result was a "false positive."
In order to gauge the skills of a machine learning algorithm, we must design a quantitative measure of its performance. Usually, this performance measure P is restricted to the task T being administered by the system. Accuracy is simply the proportion of examples that the model produces the right output. We will also obtain equivalent information by measuring the error rate, the proportion of examples for which the model produces incorrect output. The 0–1 loss on a specific example is 0 if it's correctly classified and 1 if it's not.
The topic of AI fairness metrics is as important to society as it is confusing. Confusing it is due to a number of reasons: terminological proliferation, abundance of formulae, and last not least the impression that everyone else seems to know what they're talking about. This text hopes to counteract some of that confusion by starting from a common-sense approach of contrasting two basic positions: On the one hand, the assumption that dataset features may be taken as reflecting the underlying concepts ML practitioners are interested in; on the other, that there inevitably is a gap between concept and measurement, a gap that may be bigger or smaller depending on what is being measured. In contrasting these fundamental views, we bring together concepts from ML, legal science, and political philosophy.
In machine learning, a confusion matrix is an nxn matrix such that each row represents the true classification of a given piece of data and each column represents the predicted classification (or vise versa). By looking at a confusion matrix, one can determine the accuracy of the model by looking at the values on the diagonal to determine the number of correct classifications - a good model will have high values along the diagonal and low values off the diagonal. Further, one can tell where the model is struggling by assessing the highest values not on the diagonal. Together, these analyses are useful to identify cases where the accuracy may be high but the model is consistently misclassifying the same data. Here is an example of a confusion matrix created by a neural network analyzing the MNIST dataset.
Sixty-five-year-old Michael Williams was released from jail last month after spending almost a year in jail on a murder charge. The "gunshot" sound that pointed the finger at Williams was initially classified as a firework by the AI. After the charges were dropped due to "insufficient evidence" it was revealed that one of ShotSpotter's human "reviewers" had changed the data to fit the crime, reclassifying the sound as a gunshot instead of a firework . The case highlighted the dangers that the system poses to civil liberties and brings to question how much power we should give to AI "witnesses", especially those that can easily be tampered with. Shotspotter is a patented acoustic gunshot detection system of microphones, algorithms, and human reviewers that alerts police to potential gunfire .
A thought-leader in cyber technology, Adam Mansour has over 15 years' experience spanning endpoint, network and cloud systems security; audits and architecture; building and managing SOCs; and software development. He is the creator of the IntelliGO Managed Detection and Response platform, acquired by ActZero. When you hear cybersecurity firm Darktrace's customers talk about their experience with the company, they will tell you about'the box' from Darktrace they installed. The idea behind the box is that it allows you to see malicious network traffic and coordinate to the cloud directly so you can react quickly. The main customer feedback is that the box was pretty and showed them lots of nice graphics -- beautiful network maps, gorgeous matrixes, pipe diagrams.
AI biases are common, persistent, and hard to address. We wish people see what AI can do but not its flaws. But this is like driving a Lamborghini with the check engine light on. It may run fine for the next few weeks but accidents are waiting to happen. To address the problem, we need to know what is fairness. Can it be judged or evaluated? In the previous article, we look at the complexity of AI bias. All AI designs need to follow the laws if applicable. In this section, we will discuss these issues. Sensitive characters are bias factors that are practically or morally irrelevant to a decision.