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There is a black hole in our knowledge of women and girls around the world. They are often missing from official statistics, and areas of their lives are ignored completely. So campaigners say - but what needs to be done?

Public sector employers with more than 250 staff are legally obliged to publish their gender pay gap by Friday, while private firms and charities have until Wednesday 4 April. About 7,000 of a estimated total of 9,000 organisations had filed results by Thursday.companies have filed mathematically impossible figures - at least 17 have reported a bonus gap of more than 100%. One company reported an hourly mean gender pay gap of 106.4%, implying that for every £100 earned by a man a woman would "pay" £6.40. A spokesperson at the company declined to comment.

Top Hollywood actresses have complained that they are paid less than their male co-stars - analysis of this data and how female actresses are represented in film

Protests against racial injustice and the COVID-19 pandemic have exposed racial inequalities rife within social and economic systems around the world. Fed up with police brutality and systemic racism against African Americans and other racialised groups, people staged protests against racial injustice in all 50 states across the United States.Apart from these examples, however, there is surprisingly little data or discourse about the impact of the disease on racial and ethnic minorities in the rest of Europe. This silence speaks volumes about Europe's approach to racism.The vast majority of EU member states do not use the concept of race or ethnic origin in data collection, in spite of policies like the European Racial Equality Directive and the Employment Equality Directive which prohibit racial or ethnic discrimination. France outright prohibits it.Without disaggregated data, it is virtually impossible to quantify the extent of discrimination experienced by racial and ethnic groups or the impacts of COVID-19 on their lives.

When creating a machine-learning algorithm, designers have to make many choices: what data to train it on, what specific questions to ask, how to use predictions that the algorithm produces. These choices leave room for discrimination, particularly against people who have been discriminated against in the past. For example, training an algorithm to select potential medical students on a data set that reflects longtime biases against women and people of color may make these groups less likely to be admitted. In computing, the phrase "garbage in, garbage out" describes how poor-quality input leads to poor-quality output. In this case we might say, "White male doctors in, white male doctors out."

The data seems to overwhelmingly point to a criminal justice system riven by racial bias. But, remarkably, it could be even more overwhelming than some studies make it seem. That's because of a statistical quirk called "collider bias," a kind of selection bias that means that the crime data that shows racial bias is, itself, biased by racist practices. If you thought crime data showed clear evidence of racism before, understanding how collider bias affects these analyses might make it even clearer.

Mashable's series Algorithms explores the mysterious lines of code that increasingly control our lives - and our futures. The world in 2020 has been given plenty of reasons to be wary of algorithms. Depending on the result of the U.S. presidential election, it may give us one more. Either way, it's high time we questioned the impact of these high-tech data-driven calculations, which increasingly determine who or what we see (and what we don't) online. The impact of algorithms is starting to scale up to a dizzying degree, and literally billions of people are feeling the ripple effects. This is the year the Social Credit System, an ominous Black Mirror-like "behavior score" run by the Chinese government, is set to officially launch. It may not be quite as bad as you've heard, but it will boost or tighten financial credit and other incentives for the entire population. There's another billion unexamined, unimpeachable algorithms hanging over a billion human lives.

Blame the algorithm. That's become the go-to refrain for why your Instagram feed keeps surfacing the same five people or why YouTube is feeding you questionable "up next" video recommendations. But you should blame the algorithm - those ubiquitous instructions that tell computer programs what to do - for more than messing with your social media feed. Algorithms are behind many mundane, but still consequential, decisions in your life. The code often replaces humans, but that doesn't mean the results are foolproof. An algorithm can be just as flawed as their human creators. These are just some of the ways hidden calculations determine what you do and experience.

Roughly put, building a machine-learning model involves training it on a large number of examples and then testing it on a bunch of similar examples that it has not yet seen. When the model passes the test, you're done. What the Google researchers point out is that this bar is too low. The training process can produce many different models that all pass the test but-and this is the crucial part-these models will differ in small, arbitrary ways, depending on things like the random values given to the nodes in a neural network before training starts, the way training data is selected or represented, the number of training runs, and so on. These small, often random, differences are typically overlooked if they don't affect how a model does on the test. But it turns out they can lead to huge variation in performance in the real world. In other words, the process used to build most machine-learning models today cannot tell which models will work in the real world and which ones won't.

Social media algorithms, artificial intelligence, and our own genetics are among the factors influencing us beyond our awareness. This raises an ancient question: do we have control over our own lives? This article is part of The Conversation's series on the science of free will.

We learned this week that the Trump campaign may have tried to dissuade millions of Black voters from voting in 2016 through highly targeted online ads. The investigation, by Channel 4, highlighted a still little-understood online advertising technique, microtargeting. This targets ads at people based on the huge amount of data available about them online. Experts say Big Tech needs to be much more transparent about how microtargeting works, to avoid overblown claims but also counter a potential threat to democracy.

Until the first Abbott-Hockey budget in 2014, a statement of budget measures that disproportionately affect women was published at budget time. At times given different names, the first was delivered with the Hawke government's 1984 budget. In its foreword, then Prime Minister Hawke promised that "within the overall economic objectives of the government" important budget decisions would from then on be made "with full knowledge of their impact on women". These women's budget statements shed light on the impact of decisions that might have been thought to have little to do with gender, such as the Hawke government's reduction of tariffs on imports of clothing, textiles and footwear. The statement pointed out that two-thirds of the workers in these industries were women and that without special support for retraining (which was given) they would be disproportionately disadvantaged. Increasingly, and especially during the Rudd and Gillard governments, the statements made visible the economic impact of women's greater responsibility for unpaid care work.

Today's Examine dives into the maths. We'll look at the best estimates on catching and being injured by COVID-19, the chances of being harmed by the AstraZeneca vaccine, and the other broader risks and benefits. Hopefully, at the end of this, you are armed to make a better decision.

As Omicron surges through the community, getting the right answer from a Rapid Antigen Test (RAT) is not as straightforward as reading one or two lines off the kit. RATs are a convenient diagnostic tool to detect COVID virus fragments in nasal secretions or saliva. They are designed to be self-administered and give an answer in minutes. Detecting infection early is critical to preventing spread and allowing persons at risk of severe disease to get timely access to close monitoring and new life-saving therapies. As governments plan to distribute tens of millions of RAT kits to schools and workplaces in coming weeks to help Australians work and study safely, it is important that we understand how to best use this diagnostic tool to reduce transmission and unnecessary disruptions to our lives and economy.