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Stop talking about AI ethics. It’s time to talk about power.

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Stop talking about AI ethics. It’s time to talk about power.


In doing that,, I wanted to really open up this understanding of AI as neither artificial nor intelligent. It’s the opposite of artificial. It comes from the most material parts of the Earth’s crust and from human bodies laboring, and from all of the artifacts that we produce and say and photograph every day. Neither is it intelligent. I think there’s this great original sin in the field, where people assumed that computers are somehow like human brains and if we just train them like children, they will slowly grow into these supernatural beings.

That’s something that I think is really problematic—that we’ve bought this idea of intelligence when in actual fact, we’re just looking at forms of statistical analysis at scale that have as many problems as the data that it’s given.

Was it immediately obvious to you that this is how people should be thinking about AI? Or was it a journey?

It’s absolutely been a journey. I’d say one of the turning points for me was back in 2016, when I started a project called “Anatomy of an AI system” with Vladan Joler. We met at a conference specifically about voice-enabled AI, and we were trying to effectively draw what it takes to make an Amazon Echo work. What are the components? How does it extract data? What are the layers in the data pipeline?

We realized, well—actually, to understand that, you have to understand where the components come from. Where did the chips get produced? Where are the mines? Where does it get smelted? Where are the logistical and supply chain paths?

Finally, how do we trace the end of life of these devices? How do we look at where the e-waste tips are located in places like Malaysia and Ghana and Pakistan? What we ended up with was this very time-consuming two-year research project to really trace those material supply chains from cradle to grave.

When you start looking at AI systems on that bigger scale, and on that longer time horizon, you shift away from these very narrow accounts of “AI fairness” and “ethics” to saying: these are systems that produce profound and lasting geomorphic changes to our planet, as well as increase the forms of labor inequality that we already have in the world.

So that made me realize that I had to shift from an analysis of just one device, the Amazon Echo, to applying this sort of analytic to the entire industry. That to me was the big task, and that’s why Atlas of AI took five years to write. There’s such a need to actually see what these systems really cost us, because we so rarely do the work of actually understanding their true planetary implications.

The other thing I would say that’s been a real inspiration is the growing field of scholars who are asking these bigger questions around labor, data, and inequality. Here I’m thinking of Ruha Benjamin, Safiya Noble, Mar Hicks, Julie Cohen, Meredith Broussard, Simone Brown—the list goes on. I see this as a contribution to that body of knowledge by bringing in perspectives that connect the environment, labor rights, and data protection.

You travel a lot throughout the book. Almost every chapter starts with you actually looking around at your surroundings. Why was this important to you?

It was a very conscious choice to ground an analysis of AI in specific places, to move away from these abstract “nowheres” of algorithmic space, where so many of the debates around machine learning happen. And hopefully it highlights the fact that when we don’t do that, when we just talk about these “nowhere spaces” of algorithmic objectivity, that is also a political choice, and it has ramifications.

In terms of threading the locations together, this is really why I started thinking about this metaphor of an atlas, because atlases are unusual books. They’re books that you can open up and look at the scale of an entire continent, or you can zoom in and look at a mountain range or a city. They give you these shifts in perspective and shifts in scale.

There’s this lovely line that I use in the book from the physicist Ursula Franklin. She writes about how maps join together the known and the unknown in these methods of collective insight. So for me, it was really drawing on the knowledge that I had, but also thinking about the actual locations where AI is being constructed very literally from rocks and sand and oil.

What kind of feedback has the book received?

One of the things that I’ve been surprised by in the early responses is that people really feel like this kind of perspective was overdue. There’s a moment of recognition that we need to have a different sort of conversation than the ones that we’ve been having over the last few years.

We’ve spent far too much time focusing on narrow tech fixes for AI systems and always centering technical responses and technical answers. Now we have to contend with the environmental footprint of the systems. We have to contend with the very real forms of labor exploitation that have been happening in the construction of these systems.

And we also are now starting to see the toxic legacy of what happens when you just rip out as much data off the internet as you can, and just call it ground truth. That kind of problematic framing of the world has produced so many harms, and as always, those harms have been felt most of all by communities who were already marginalized and not experiencing the benefits of those systems.

What do you hope people will start to do differently?

I hope it’s going to be a lot harder to have these cul-de-sac conversations where terms like “ethics” and “AI for good” have been so completely denatured of any actual meaning. I hope it pulls aside the curtain and says, let’s actually look at who’s running the levers of these systems. That means shifting away from just focusing on things like ethical principles to talking about power.

How do we move away from this ethics framing?

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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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