We act as if technology were neutral but its not. The challenge now is to remove the gender bias, says human rights lawyer and novelist Lizzie OShea
Most women in the Bay Area are soft and weak, cosseted and naive, despite their claims of worldliness, and generally full of shit, wrote former Facebook product manager Antonio Garca Martnez in 2016. They have their self-regarding entitlement feminism, and endlessly vaunt their independence. But current realities is, come the epidemic plague or foreign invasion, theyd become precisely the sort of useless baggage youd trade for a box of shotgun shells or a jerry can of diesel. This is from his insider account of Silicon Valley, Chaos Monkeys. The book was a bestseller. The New York Times called it an irresistible and indispensable 360 -degree guide to the new technology establishment. Anyone who is surprised by the recent revelations of sexism spreading like wildfire through the technology industry has not been paying attention.
When Susan Fowler wrote about her experience of being sexually harassed at Uber, it prompted a chain of events that seemed unimaginable months ago, including an investigation led by former us attorney general Eric Holder, and the departure of a number of key members of the companys leadership team. Venture capitalist Justin Caldbeck faced allegations of harassing behaviour, and where reference is offered an unimpressive refusal, companies funded by his firm banded togetherto condemn his tepidity. He subsequently resigned, and the future of his former firm is unclear. Since then, dozens of women have come forward to reveal the sexist culture in numerous Silicon Valley technology and venture capital firms. It is increasingly clear from these accounts that the problem for women in the tech industry is not a failure to lean in, it is a culture of harassment and discrimination that stimulates many of their workplaces unsafe and unpleasant.
At least this issue is being discussed in ways that open up the possibility that it will be addressed. But the problem of sexism in the tech industry runs much deeper and wider. Technological development is undermining the cause of womens equality in other ways.
American academic Melvin Kranzbergs first law to new technologiestells us that technology is neither inherently good nor bad , nor is it neutral. As a black mirror it reflects the problems that exist in society including the oppression of women. Millions of people bark orders at Alexa, every day, but rarely are we encouraged to wonder why the domestic organiser is was put forward by a woman. The entry system for a womens locker room in a gym recently refused entry to a female member because her title was Dr, and it categorised her as male.
But the issue is not only that technology products reflect a backward opinion of the role of women. They often also appear ignorant or indifferent to womens lived experience. As the internet of things expands, more devices in our homes and on our bodies are collecting data about us and sending it to networks, a process over which we often have little control. This presents profound problems for vulnerable members of society, including survivors of domestic violence. Wearable technology can be hacked, autoes and phones can be tracked, and data from a thermostat can reveal whether someone is at home. This potential is frightening for people who have experienced rape, violence or stalking.
Unsurprisingly, technology consumed by abusers: in a survey of domestic violence services organisations, 97% reported that the survivors who use them have experienced harassment, monitoring, and threats by abusers through the misuse of technology. This often happens on telephones, but 60% of those surveyed also reported that abusers have snooped or eavesdropped on the survivor or children employing other forms of technology, including toys and other gifts. Many shelters have resorted to banning the use of Facebook because of fears about uncovering information about their location to stalkers. There are ways to make devices devote control to users and restriction the capacity for abuse. But there is little evidence that this has been a priority for the technology industry.
Products that are more responsive to the needs of women would be a great start. But we should also be thinking bigger: we must avoid reproduction sexism in system design. The word-embedding models used in things like conversation bots and word searches offer an instructive instance. These models operate by feeding huge amounts of text into a computer so it learns how terms relate to each other in space. It is based on the premise that terms which appear near one another in texts share meaning. These spatial relationships are used in natural language-processing so that computers can engage with us conversationally. By reading a lot of text, a computer can learn that Paris is to France as Tokyo is to Japan. It develops a dictionary by association.
But this can create problems when the world is not exactly as it ought to be. For instance, researchers have experimented with one of these word-embedding models, Word2vec, a popular and freely available model trained on three million terms from Google News. They found that it makes highly gendered analogies. For instance, when asked Man is to girl as computer programmer is to ?, the model will answer homemaker. Or for father is to mom as doctor is to ?, the answer is nurse. Of course the model reflects a certain reality: it is true that there are more male computer programmers, and nurses are more often women. But this bias, reflecting social discrimination, will now be reproduction and reinforced when we be participating in computers employing natural language that relies on Word2vec. It is not hard to imagine how this model could also be racially biased, or biased against other groups.
These biases can be amplified during the process of speech learn. As the MIT Technology Review points out: If the phrase computer programmer is more closely associated with men than women, then a search for the term computer programmer CVs might rank men more highly than females. When this kind of language learning has applications across fields including medication, education, job, policymaking and criminal justice, it is not hard to see how much injury such biases can cause.
Removing such gender bias is a challenge, in part because the problem is inherently political: Word2vec entrenches the world as it is, rather than what it could or should be. But if we are to alter the models to reflect aspirations, how do we choose what kind of world what we want is?
Digital technology offers myriad ways to set these appreciations to work. It is not bad, but we have to challenge the presumption that it is neutral. Its potential is being explored in ways that are sometimes promising, often frightening and astounding. To build the most of this moment, we need to imagine a future without the oppressions of the past. We need to allow females to reach their potential in workplaces where they feel safe and respected. But we also need to look into the black mirror of technology and find the crackings of light glistening through.
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