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Facebook agrees to revamp adtech over discrimination charges

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Facebook agrees to revamp adtech over discrimination charges

Facebook’s parent company Meta will revamp its targeted advertising system following accusations it allowed landlords to run discriminatory ads. This is part of a sweeping settlement to a Fair Housing Act lawsuit announced Tuesday by the U.S. Justice Department.

This is the second time the company has settled a lawsuit over adtech discrimination issues. However, yesterday’s settlement goes further than the previous one. It requires the company to overhaul its ad targeting tool, Lookalike Audiences, which makes it possible to target housing ads by race, gender, religion or other sensitive characteristics that enable discrimination.


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“Because of this groundbreaking lawsuit, Meta will — for the first time — change its ad delivery system to address algorithmic discrimination,” Damian Williams, a U.S. attorney, said in a statement. “But if Meta fails to demonstrate that it has sufficiently changed its delivery system to guard against algorithmic bias, this office will proceed with the litigation.”

Facebook must build a new ad system that will ensure housing ads are delivered to a more equitable mix of people. It must also submit the system to a third party for review and pay a $115,054 fine, the maximum penalty available under the law.

Read next: Major brands commit to mitigating adtech bias

This new system will use machine learning to fix bias. It “will work to ensure the age, gender and estimated race or ethnicity of a housing ad’s overall audience matches the age, gender, and estimated race or ethnicity mix of the population eligible to see that ad,” the company said in a statement.

Worth noting. An MIT study released in March found “machine-learning models that are popular for image recognition tasks actually encode bias when trained on unbalanced data. This bias within the model is impossible to fix later on, even with state-of-the-art fairness-boosting techniques, and even when retraining the model with a balanced dataset.” Earlier this month MIT released a study which found that “explanation methods designed to help users determine whether to trust a machine-learning model’s predictions can perpetuate biases and lead to less accurate predictions for people from disadvantaged groups.”

Why we care. Adtech bias is getting a lot of attention, it needs to get more. On the same day as the Facebook settlement, a coalition of major brands, the IAB and the Ad Council announced a plan to address the issue. Automated marketing and ad targeting can result in unintentional discrimination. They can also scale up intentional discrimination. Intended or not the impact of discrimination is real and has a huge impact on the entire society. Technology can’t fix this. Machine learning and AI can suffer from the same biases as their programmers. This is a problem which people caused and only people can fix.


About The Author

Constantine von Hoffman is managing editor of MarTech. A veteran journalist, Con has covered business, finance, marketing and tech for CBSNews.com, Brandweek, CMO, and Inc. He has been city editor of the Boston Herald, news producer at NPR, and has written for Harvard Business Review, Boston Magazine, Sierra, and many other publications. He has also been a professional stand-up comedian, given talks at anime and gaming conventions on everything from My Neighbor Totoro to the history of dice and boardgames, and is author of the magical realist novel John Henry the Revelator. He lives in Boston with his wife, Jennifer, and either too many or too few dogs.

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SEO Recap: ChatGPT – Moz

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SEO Recap: ChatGPT - Moz

The author’s views are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.

We’re back with another SEO recap with Tom Capper! As you’ve probably noticed, ChatGPT has taken the search world by storm. But does GPT-3 mean the end of SEO as we know it, or are there ways to incorporate the AI model into our daily work?

Tom tries to tackle this question by demonstrating how he plans to use ChatGPT, along with other natural language processing systems, in his own work.

Be sure to check out the commentary on ChatGPT from our other Moz subject matter experts, Dr. Pete Meyers and Miriam Ellis:

Video Transcription

Hello, I’m Tom Capper from Moz, and today I want to talk about how I’m going to use ChatGPT and NLP, natural language processing apps in general in my day-to-day SEO tasks. This has been a big topic recently. I’ve seen a lot of people tweeting about this. Some people saying SEO is dead. This is the beginning of the end. As always, I think that’s maybe a bit too dramatic, but there are some big ways that this can be useful and that this will affect SEOs in their industry I think.

The first question I want to ask is, “Can we use this instead of Google? Are people going to start using NLP-powered assistants instead of search engines in a big way?”

So just being meta here, I asked ChatGPT to write a song about Google’s search results being ruined by an influx of AI content. This is obviously something that Google themselves is really concerned about, right? They talked about it with the helpful content update. Now I think the fact that we can be concerned about AI content ruining search results suggests there might be some problem with an AI-powered search engine, right?

No, AI powered is maybe the wrong term because, obviously, Google themselves are at some degree AI powered, but I mean pure, AI-written results. So for example, I stole this from a tweet and I’ve credited the account below, but if you ask it, “What is the fastest marine mammal,” the fastest marine mammal is the peregrine falcon. That is not a mammal.

Then it mentions the sailfish, which is not a mammal, and marlin, which is not a mammal. This is a particularly bad result. Whereas if I google this, great, that is an example of a fast mammal. We’re at least on the right track. Similarly, if I’m looking for a specific article on a specific web page, I’ve searched Atlantic article about the declining quality of search results, and even though clearly, if you look at the other information that it surfaces, clearly this has consumed some kind of selection of web pages, it’s refusing to acknowledge that here.

Whereas obviously, if I google that, very easy. I can find what I’m looking for straightaway. So yeah, maybe I’m not going to just replace Google with ChatGPT just yet. What about writing copy though? What about I’m fed up of having to manually write blog posts about content that I want to rank for or that I think my audience want to hear about?

So I’m just going to outsource it to a robot. Well, here’s an example. “Write a blog post about the future of NLP in SEO.” Now, at first glance, this looks okay. But actually, when you look a little bit closer, it’s a bluff. It’s vapid. It doesn’t really use any concrete examples.

It doesn’t really read the room. It doesn’t talk about sort of how our industry might be affected more broadly. It just uses some quick tactical examples. It’s not the worst article you could find. I’m sure if you pulled a teenager off the street who knew nothing about this and asked them to write about it, they would probably produce something worse than this.

But on the other hand, if you saw an article on the Moz blog or on another industry credible source, you’d expect something better than this. So yeah, I don’t think that we’re going to be using ChatGPT as our copywriter right away, but there may be some nuance, which I’ll get to in just a bit. What about writing descriptions though?

I thought this was pretty good. “Write a meta description for my Moz blog post about SEO predictions in 2023.” Now I could do a lot better with the query here. I could tell it what my post is going to be about for starters so that it could write a more specific description. But this is already quite good. It’s the right length for a meta description. It covers the bases.

It’s inviting people to click. It makes it sound exciting. This is pretty good. Now you’d obviously want a human to review these for the factual issues we talked about before. But I think a human plus the AI is going to be more effective here than just the human or at least more time efficient. So that’s a potential use case.

What about ideating copy? So I said that the pure ChatGPT written blog post wasn’t great. But one thing I could do is get it to give me a list of subtopics or subheadings that I might want to include in my own post. So here, although it is not the best blog post in the world, it has covered some topics that I might not have thought about.

So I might want to include those in my own post. So instead of asking it “write a blog post about the future of NLP in SEO,” I could say, “Write a bullet point list of ways NLP might affect SEO.” Then I could steal some of those, if I hadn’t thought of them myself, as potential topics that my own ideation had missed. Similarly you could use that as a copywriter’s brief or something like that, again in addition to human participation.

My favorite use case so far though is coding. So personally, I’m not a developer by trade, but often, like many SEOs, I have to interact with SQL, with JavaScript, with Excel, and these kinds of things. That often results in a lot of googling from first principles for someone less experienced in those areas.

Even experienced coders often find themselves falling back to Stack Overflow and this kind of thing. So here’s an example. “Write an SQL query that extracts all the rows from table2 where column A also exists as a row in table1.” So that’s quite complex. I’ve not really made an effort to make that query very easy to understand, but the result is actually pretty good.

It’s a working piece of SQL with an explanation below. This is much quicker than me figuring this out from first principles, and I can take that myself and work it into something good. So again, this is AI plus human rather than just AI or just human being the most effective. I could get a lot of value out of this, and I definitely will. I think in the future, rather than starting by going to Stack Overflow or googling something where I hope to see a Stack Overflow result, I think I would start just by asking here and then work from there.

That’s all. So that’s how I think I’m going to be using ChatGPT in my day-to-day SEO tasks. I’d love to hear what you’ve got planned. Let me know. Thanks.

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What Is a White Paper? [FAQs]

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What Is a White Paper? [FAQs]

The definition of a whitepaper varies heavily from industry to industry, which can be a little confusing for marketers looking to create one for their business.

The old-school definition comes from politics, where it means a legislative document explaining and supporting a particular political solution.

(more…)

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HubSpot to cut around 7% of workforce by end of Q1

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HubSpot to cut around 7% of workforce by end of Q1

This afternoon, HubSpot announced it would be making cuts in its workforce during Q1 2023. In a Securities and Exchange Commission filing it put the scale of the cuts at 7%. This would mean losing around 500 employees from its workforce of over 7,000.

The reasons cited were a downward trend in business and a “faster deceleration” than expected following positive growth during the pandemic.

Layoffs follow swift growth. Indeed, the layoffs need to be seen against the background of very rapid growth at the company. The size of the workforce at HubSpot grew over 40% between the end of 2020 and today.

In 2022 it announced a major expansion of its international presence with new operations in Spain and the Netherlands and a plan to expand its Canadian presence in 2023.

Why we care. The current cool down in the martech space, and in tech generally, does need to be seen in the context of startling leaps forward made under pandemic conditions. As the importance of digital marketing and the digital environment in general grew at an unprecedented rate, vendors saw opportunities for growth.

The world is re-adjusting. We may not be seeing a bubble burst, but we are seeing a bubble undergoing some slight but predictable deflation.


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About the author

Kim Davis

Kim Davis is the Editorial Director of MarTech. Born in London, but a New Yorker for over two decades, Kim started covering enterprise software ten years ago. His experience encompasses SaaS for the enterprise, digital- ad data-driven urban planning, and applications of SaaS, digital technology, and data in the marketing space.

He first wrote about marketing technology as editor of Haymarket’s The Hub, a dedicated marketing tech website, which subsequently became a channel on the established direct marketing brand DMN. Kim joined DMN proper in 2016, as a senior editor, becoming Executive Editor, then Editor-in-Chief a position he held until January 2020.

Prior to working in tech journalism, Kim was Associate Editor at a New York Times hyper-local news site, The Local: East Village, and has previously worked as an editor of an academic publication, and as a music journalist. He has written hundreds of New York restaurant reviews for a personal blog, and has been an occasional guest contributor to Eater.

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