AI

  • Sexism in Machine Learning

    Could our technology be partly to blame for the sexism in the workforce?

    Gender inequalities in the workplace are not something new to our ears. On average women are paid less and make fewer advances in their careers. The New York Times reported that in 2015 more men named John ran S&P 1500 firms than women alone. Sexism and racism are rife throughout tech companies. The pay gap is closing but at the current rate, women will not be paid equally until the year 2119. But as of late, what goes on in these companies is not staying in these companies. Anyone using the technology can be affected by it, not just those who make it.

    The Study “Man is to Computer Programmer as Woman is to Homemaker” says the machine learning has the ability to amplify “female/male stereotypes to a disturbing extent”. Googles natural language processing program was trained using Google News articles and researches have found that the neural network called word2vec has adopted the same sexist and racial characteristics as our very own news.

    To understand more on how this can happen we need to delve into how word2vec actually works.

    The letters A, P, P, L and E form to make the word apple but it gives us little information on what an apple is or if its the company or the fruit we are talking about. Because words are simply a combination of characters that hold very little information when looked at individually, word processing can be difficult. And thus word2vec was born and a computers ability to understand human speech created. It works by compressing the words into a numeric representation of the information that you would need to recreate that same word. Training the programme to comb through Google News articles in order to learn the relationships between words.

    “King is to Man as Queen is to Woman” and “Paris is to France as Tokyo is to Japan” is an example of some of the analogies that the system could come up with. But it didn’t stop there. One would have hoped that it would exhibit little gender stereotypes, as articles the neural network was learning from were written by professional journalists. This was not the case. The neural network simply started to reflect the Google News data that is was built from. Returning relationships such as “Man is to woman as computer programmer is to homemaker,” or “Man is to architect as woman is to interior designer.”

    The effects of this can be huge.

    More and more studies are are showing this has an effect on job searches for women, showing them lower paid search options. And another shocking report claimed that a computer program used by a US court for risk assessment was biased against black prisoners.


    These flaws may seem small and easy to mitigate but they are painting a picture of what our tech industry looks like, an industry that is often out of touch with the people who are using their products. Fundamental change needs to be made or it will continue to be this way. Although a lot of progress has been made, we have so far to go. Not until the hostility to diversity is removed can we start to build incredible technology that works for everyone.


    At Quidtree we pride ourselves on being diverse. Everyone is treated equally.

  • Google amps up machine learning in Ad tools and we are here for it!

    Google has just unveiled responsive search ads that will be powered by machine learning.

    This will optimize creative assets in real time so the consumers see the best-performing ad for their search. Google announced on stage at Tuesdays Google Marketing Live event. Followed up by a blog post.

    Responsive Search Ads:

    Instead of manually developing, testing and optimizing text ads for search, the new “Responsive” search ads will use machine learning to immediately determine the highest-performing combination among 15 headlines and four description lines supplied by you, the advertiser. This will continuously optimize the search results based on what appears to be the most relevant to the searcher.

    Let’s say two people both searched for “best Bluetooth speaker”. Both searchers may see different a different ad based on the signals used in bidding such as the device type they are searching on. Google has claimed that this will increase the clicks on your creatives by 15%.

    “They’re making it simpler for business to create ads and, more importantly, understand the success of those ads,” says Eric Heaton, tech director at global creative agency B-Reel, which works with brands like Nike, H&M, and Google. “The products surrounding campaign tracking and insights are going to be a real game changer, especially the cross-device behaviour tracking, and the new ‘Instant Reporting’ feature on Googles Marketing Platform. Marketers will be able to better understand the way users are engaging with their ads, and adapt them in real time.”

    Maximum Lift for YouTube

    Maximum Lift for YouTube is another new feature with an integrated machine-learning smart-bidding strategy.  Googles been promoting YouTube’s ability to deliver brand lift and making YouTube a destination for premium brand buys. This beta feature will allow marketers to reach viewers most likely to be interested in the brand after seeing a video ad. Bids at auction will be automatically adjusted to maximize the impact the video has on brand perception through the customer’s journey. Improving ad recall and brand favourability.

    An increase in foot traffic.

    Machine learning will also be helping increase foot traffic in stores. In the past, Google has offered local ad formats. The new updates will apply machine learning to the businesses location and its creatives to further improve ads, and drive foot traffic.

    Earlier this year Google introduced Smart Shopping campaigns. Marketers using this tool will now have the ability to optimize ads to boost two additional business goals: physical store visits and new customers. You will be able to create and manage Smart Shopping Campaigns from within Shopify.

    The new features will all be available in the coming months. YouTubes Maximize Lift is still in its beta form and is looking to launch later this year.

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