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How to survive as an AI ethicist

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Responsible AI has a burnout problem


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Welcome to the Algorithm! 

It’s never been more important for companies to ensure that their AI systems function safely, especially as new laws to hold them accountable kick in. The responsible AI teams they set up to do that are supposed to be a priority, but investment in it is still lagging behind.

People working in the field suffer as a result, as I found in my latest piece. Organizations place huge pressure on individuals to fix big, systemic problems without proper support, while they often face a near-constant barrage of aggressive criticism online. 

The problem also feels very personal—AI systems often reflect and exacerbate the worst aspects of our societies, such as racism and sexism. The problematic technologies range from facial recognition systems that classify Black people as gorillas to deepfake software used to make porn videos of women who have not consented. Dealing with these issues can be especially taxing to women, people of color, and other marginalized groups, who tend to gravitate toward AI ethics jobs. 

I spoke with a bunch of ethical-AI practitioners about the challenges they face in their work, and one thing was clear: burnout is real, and it’s harming the entire field. Read my story here.

Two of the people I spoke to in the story are pioneers of applied AI ethics: Margaret Mitchell and Rumman Chowdhury, who now work at Hugging Face and Twitter, respectively. Here are their top tips for surviving in the industry. 

1. Be your own advocate. Despite growing mainstream awareness about the risks AI poses, ethicists still find themselves fighting to be recognized by colleagues. Machine-learning culture has historically not been great at acknowledging the needs of people. “No matter how confident or loud the people in the meeting are [who are] talking or speaking against what you’re doing—that doesn’t mean they’re right,” says Mitchell. “You have to be prepared to be your own advocate for your own work.”

2. Slow and steady wins the race. In the story, Chowdhury talks about how exhausting it is to follow every single debate on social media about the possible harmful side effects of new AI technologies. Her advice: It’s okay not to engage in every debate. “I’ve been in this for long enough to see the same narrative cycle over and over,” Chowdhury says. “You’re better off focusing on your work, and coming up with something solid even if you’re missing two or three cycles of information hype.”

3. Don’t be a martyr. (It’s not worth it.) AI ethicists have a lot in common with activists: their work is fueled by passion, idealism, and a desire to make the world a better place. But there’s nothing noble about taking a job in a company that goes against your own values. “However famous the company is, it’s not worth being in a work situation where you don’t feel like your entire company, or at least a significant part of your company, is trying to do this with you,” says Chowdhury. “Your job is not to be paid lots of money to point out problems. Your job is to help them make their product better. And if you don’t believe in the product, then don’t work there.”

Deeper Learning

Machine learning could vastly speed up the search for new metals

Machine learning could help scientists develop new types of metals with useful properties, such as resistance to extreme temperatures and rust, according to new research. This could be useful in a range of sectors—for example, metals that perform well at lower temperatures could improve spacecraft, while metals that resist corrosion could be used for boats and submarines. 

Why this matters: The findings could help pave the way for greater use of machine learning in materials science, a field that still relies heavily on laboratory experimentation. Also, the technique could be adapted for discovery in other fields, such as chemistry and physics. Read more from Tammy Xu here.

Even Deeper Learning

The evolution of AI 

On Thursday, November 3, MIT Technology Review’s senior editor for AI, William Heaven, will quiz AI luminaries such as Yann LeCun, chief AI scientist at Meta; Raia Hadsell, senior director of research and robotics at DeepMind; and Ashley Llorens, hip-hop artist and distinguished scientist at Microsoft Research, on stage at our flagship event, EmTech. 

On the agenda: They will discuss the path forward for AI research, the ethics of responsible AI use and development, the impact of open collaboration, and the most realistic end goal for artificial general intelligence. Register here.

LeCun is often called one of the “godfathers of deep learning.” Will and I spoke with LeCun earlier this year when he unveiled his bold proposal about how AI can achieve human-level intelligence. LeCun’s vision includes pulling together old ideas, such as cognitive architectures inspired by the brain, and combining them with deep-learning technologies. 

Bits and Bytes

Shutterstock will start selling AI-generated imagery
The stock image company is teaming up with OpenAI, the company that created DALL-E. Shutterstock is also launching a fund to reimburse artists whose works are used to train AI models. (The Verge)

The UK’s information commissioner says emotion recognition is BS
In a first from a regulator, the UK’s information commissioner said companies should avoid the “pseudoscientific” AI technology, which claims to be able to detect people’s emotions, or risk fines.  (The Guardian)

Alex Hanna left Google to try to save AI’s future
MIT Technology Review profiled Alex Hanna, who left Google’s Ethical AI team earlier this year to join the Distributed AI Research Institute (DAIR), which aims to challenge the existing understanding of AI through a community-­focused, bottom-up approach to research. The institute is the brainchild of Hanna’s old boss, Timnit Gebru, who was fired by Google in late 2020. (MIT Technology Review)

Thanks for reading! 

Melissa

Tech

The hunter-gatherer groups at the heart of a microbiome gold rush

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The hunter-gatherer groups at the heart of a microbiome gold rush


The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania. 

Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.

Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.

A changing way of life

The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.

Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.

One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.

Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.

The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan


Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.

Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost. 

But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.

—Jessica Hamzelou

Eric Schmidt has a 6-point plan for fighting election misinformation

—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures

The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.

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Navigating a shifting customer-engagement landscape with generative AI

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Navigating a shifting customer-engagement landscape with generative AI


A strategic imperative

Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.

According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.

Mapping out a generative AI pathway

One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.

The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.

Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.

All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.

The benefits of third-party experience

Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).

Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”

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