Privacy check for women- News considers the many technological achievements women have made over the past year as Women’s History Month comes to a close. Consider how, unintentionally, the rise of female-centric apps like the monthly menstrual tracker encourages women to speak up for their own health.
We contrast that with the industry’s darker offshoots, like dumped lovers posting vengeance porn and the government issuing
subpoenas to gather your digital reproductive footprint.
Female empowerment is at an all-time high in society and the workplace, from CEOs to startups and everything in between. Even so, women of all ages continue to face particular obstacles in the industry, particularly when it comes to individual rights and privacy.
We’ve covered a number of topics in the past year, including the dangers of cyberstalking and AI misogyny, while others may be shocking to some readers. As always, staying informed is the first step to more freedom.
The threat of sexual predators, cyberbullying, cyberstalking, revenge porn, and these are just a few of the serious issues that all women, from elementary school age and older, must deal with.
Balancing the pitfalls of contemporary digital norms with female mental health and physical safety continues to be at the forefront of women’s everyday lives, whether it be due to jealous boyfriends, failed romantic relationships, or interacting with strangers on social media, online forums, and through numerous dating apps.
Education is essential for preparing young girls to deal with these persistent dangers. However, we must also put pressure on our governments to enact consistent laws that will effectively defend victims and hold offenders accountable.
Cyberbullying and cyberstalking perpetrators use any and all digital communication channels to threaten and intimidate their targets. These online crimes are typically committed repeatedly, over a long period of time, and by a person the victim knows. Such behaviors can cause serious harm to victims and even serve as a suicide trigger.
By definition, cyberbullying refers to students currently enrolled in school between the ages of 12 and 18, whereas cyberstalking refers to victims who are adults over the age of 18.
The methods that the offenders employ to commit the crimes also differ between the two.
Texting, emailing, social media, online forums, tricking search engines, and building phony websites are all examples of cyberbullying. Predators who engage in cyberstalking often step up their game and employ more sophisticated techniques like targeted smear campaigns, blackmail, and financial extortion.
Cyberstalkers can install digital spyware on a victim’s devices, hack into their online accounts to change information, or even create fictitious accounts or profiles to communicate with their target using a false digital identity or alias.
Another specific type of sexual harassment is revenge pornography, in which the victim’s explicit photos or videos are distributed or published online without their permission.
Nearly 60% of teens reported cyberbullying in 2018, and between that year and this one, 40% of adults complained of similar mistreatment online, according to studies. The situation was made even worse by COVID: online bullying has increased by 70% since the global pandemic started in 2020.
Ironically, research reveals that when it comes to teenage kids, girls are more likely than boys to play both victim and perpetrator. But among adult women, those between the ages of 18 and 30 are most frequently the targets of male intimidation, including acts that are classified as domestic violence.
The UN Gender Equality Organization’s research just this month revealed that online threats of violence against women were a significant deterrent for females all over the world, preventing them from publicly advocating for social change, taking leadership roles, or running for political office. There have even been instances where the victim’s own government has made threats.
The biggest obstacles to protecting women from online harassment, besides education and cybersecurity best practices, are frequently the legal requirements necessary to prosecute the offenders.
Free speech frequently protects cyberstalkers in many Western countries. Offenders can hide behind their own “right to privacy” by posting anonymously.
In the US, there are many obstacles to prosecuting offenders, such as a lack of police resources, high costs to carry out a full prosecution in court, and jurisdictional problems.
Regarding legal jurisdiction, it is frequently very difficult to prosecute a cyberstalker who is located in a different state from the victim unless it is a federal case because each of the fifty states has its own cyberstalking laws.
Since the late 2000s, tech firms have been looking for novel ways to link technology with problems affecting women’s health, forming the “femtech” market.
Femtech is a term that was coined in 2016 by the creator of Clue, one of the first apps to track menstruation. It refers to any technology good, program, or service that keeps track of a woman’s health.
In some ways, the technology sector has assisted women in advocating for their own health in ways they had never felt comfortable doing before, from tracking birth control, ovulation cycles, fertility treatments, and menopause to having access to online health records, scheduling doctor’s appointments, and creating forums for women to share experiences in healthcare.
A 2022 UK study by the research team Thrive found that 44% of women between the ages of 18 and 65 felt that medical professionals did not take them seriously.
Additionally, more than 35% of those surveyed believed that their doctors lacked a basic understanding of women’s lives and bodies.
Although femtech is still primarily used by women in Western countries, the Google Play store reports that the popular menstrual app, Flo, has been downloaded more than 100 million times.
Conor Stewart, a specialist in medical research, estimates that the global femtech market will double in size by 2030 from its current value of $51 billion.
According to FemTech Analytics, the lack of funding is one of the biggest obstacles facing the expanding femtech sector. Unsurprisingly, the lack of funding for femtech startups mirrors a similar shortage for female startups. According to Forbes, women-led startups received a pitiful 2.3% of venture capital funding in 2020.
The problem is that as more women use these apps, more women will need to speak out in favor of protecting the data they collect.
In nearly two dozen women’s health apps, a 2022 JMIR European data privacy survey found that the majority allowed behavioral tracking, more than 60% tracked user locations, and more than 90% shared user data with third parties.
Women’s rights and privacy organizations have warned, rightfully so, that the personal information collected from these apps could be used to prosecute women who seek or have abortions since the historic Roe v. Wade ruling in the US Supreme Court last June.
US lawmakers have proposed two bills to help stop this from happening, but they have either run into opposition in state legislatures or have sat idle on Capitol Hill.
The My Body My Data Act proposed legislation aims to restrict how much reproductive data is gathered and stored by apps.
The Health and Location Data Protection Act, if it were to become law, would stop data brokers and big tech from selling sensitive information to third parties, which frequently happens without the owner of the data’s knowledge or consent.
A bill that would have prohibited law enforcement from using a search warrant to obtain data from period-tracking apps was overturned by Virginia courts in February 2023.
Authorities in Nebraska were able to bring charges against the mother last June after obtaining unencrypted Facebook messages between her and her teenage daughter in which they discussed abortion pills.
Additionally, women need to be aware that any security lapse at any of these femtech businesses, medical centers, doctor’s offices, or even personal gadgets could expose this private data to the general public.
Legal action, professional persecution, reputational harm, financial extortion by threat actors, and even discrimination from health insurance companies could result from this exposure.
Finally, as reproductive technology develops, women must take extra precautions to safeguard the private information they have about adoption, surrogacy, and other in-vitro fertilization procedures.
bias in the tech sector and AI
The subject of our discussion today might have a long-term impact on female generations to come.
Industry experts are concerned about how well digital intelligence will be able to disseminate information through machine learning – without adopting an underlying bias towards the female gender – in light of the explosive emergence of AI-driven large language learning models like ChatGPT (which made its seismic debut just six months ago).
Women still struggle to be equally represented throughout the technology industry, both in the trenches and in leadership roles, even after years of blatant discrimination from their “tech bro” counterparts.
According to research, working as a woman in the tech sector is, at best, demoralizing.
According to research led by Professor Vandana Singh at the University of Tennessee, sexist and unfair treatment ranged from normalized abuse and harassment to discrimination and misogyny, and in some instances, explicit death threats. This is in addition to a significant pay gap.
The study also discovered that women’s expertise was misunderstood, their contributions were poorly received, and their work roles were consistently reduced. It’s understandable that, according to research by Accenture and Girls Who Code, more than half of women leave the field by the age of 35.
These instances of discrimination against women serve as further evidence of why it is crucial to be aware of potential bias in society at large, not just in the tech industry, where the majority of AI deep-learning models released to date have been created by white men.
This prejudice exists not just against women but also against many other minority groups. In experiments conducted for the Netflix documentary Coded Bias, which exposed how AI algorithms discriminate in employment, banking, insurance, dating, policing, and social media, it was found that facial recognition software has a tendency to be biased toward people of color.
The first indication of AI bias appeared in 2015, when Amazon engineers tried to create a program that would use AI to help them sort through job applicants. The software team found that the automated program, which was designed to select the best candidates, favored male applicants. The AI essentially learned to exclude women from the mix over the past ten years because the majority of Amazon’s tech employees had been men.
It is almost certain that there will be general biases against women in AI learning because it is based on millions of pieces of data that are scraped from the internet.
The Pulitzer Center’s AI Accountability Network and the Guardian published a study last month that found AI had a clear bias towards incorrectly classifying images of women as “sexual,” as opposed to comparable images of men that didn’t fall under this classification.
A process known as shadowbanning would limit the photos’ online exposure by classifying them as sexually explicit. According to the study, many of the images actually showed women in positive situations, like during a checkup or while swimming in the ocean while wearing a bathing suit.
According to Lorenzo Belenguer’s research on AI ethics, biases in AI can arise in a number of different ways. Examples include data sets with historical references, a lack of geographic diversity, inappropriate evaluation benchmarks, and/or data sets with demographic information such as age, social norms, and cultural references.
According to a study by female researchers from the Center for Equity, Gender, and Leadership, gender biases harm women in about 70% of cases by lowering service quality, misallocating resources, and reinforcing unfavorable stereotypes.
The same study includes a list of recommendations that social justice advocates and AI developers can try to put into practice to help minimize these biases.
Some of the suggestions include integrating diversity into the AI design and training process, assisting deep learning by filling in the blanks with “feminist data,” developing AI governance, performing algorithm audits, and promoting AI literacy.
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