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The American Public’s Attitudes Concerning Artificial Intelligence

A report published by the Center for the Governance of AI (GovAI), housed in the Future of Humanity Institute, surveys Americans’ attitudes on artificial intelligence.

The survey, carried out by Baobao Zhang and Allan Dafoe, is one of the most comprehensive surveys focusing on the American public’s opinions on artificial intelligence to date, including 2000 participants using the survey firm YouGov.

Key findings from our report include: Allan Dafoe, commenting on the report, said: “Our results show that the public regards as important the whole space of AI governance issues, including privacy, fairness, autonomous weapons, unemployment, and other extreme risks that may arise from advanced AI.

How artificial intelligence will affect the future of energy and climate

In a 2017 article for Foreign Affairs, Kassia Yanosek and I advanced the hypothesis that the biggest impacts of the information technology (IT) revolution may be felt far outside IT—in the traditional industries of oil, gas, and electricity.1 That’s because IT was transforming how those industries function.

Other essays in this series explain what’s happening with AI and why it is such an important technical revolution.2 In this essay, I’ll look at how AI might be affecting the supply and demand for energy and the implications of AI for how modern society uses energy: climate change.

Even a big effort to control emissions will leave a lot of climate change—meaning that, in the future, much of “climate policy” will be focused on adapting to climate impacts and implementing quick responses in case of climate emergencies.3 Extremely intelligent systems for adapting to climate change impacts may make the cost of that adaptation more transparent and thus politically difficult to muster.

The impacts of AI are numerous, but four clusters of impacts seem most likely to affect energy and climate—two will alter the supply and demand for energy, and two will affect the ability of societies to understand how emissions are affecting the climate and how to manage those impacts.

This is a hard question to answer because it requires disentangling the effects of many other technological changes (e.g., improved drill bits, control systems for horizontal drilling, better materials for wind turbine blades, and less costly solar cells) from the specific effects of AI.

At the moment, my sense is that AI is having a bigger impact in oil and gas than in renewables because the kinds of activities that are unlocking new hydrocarbon resources—notably the shale revolution in oil and gas which requires mapping complex underground reservoirs and tailoring drilling methods4—are particularly well-suited to the recursive, complex learning processes that AI is well-suited to deliver.

The effects are likely larger than one percent—already, simple “nudge” interventions in power markets, for example reminding customers about the need to reduce energy consumption during peak periods and changing the default settings on thermostats—yield energy savings up to a few percentage points.

A few utilities are experimenting with systems, some large customers are actively managing energy systems with AI-based systems (because they can afford to amortize the cost over large savings), and some firms like Stem are emerging as intermediaries—making explicit AI offers to customers and providing the expertise needed so that even small customers can utilize these systems.

If the central message from the above discussion is that AI makes it possible for energy markets to reflect real-world conditions—and to be more efficient in matching consumer preferences with supplies—then there is no reason to believe that these more efficient markets, on their own, will tackle the carbon problem.

Better and more efficient markets that can help consumers become more responsive to real-world conditions could help tamp down that enthusiasm for regulation and make practical a greater reliance on market-based instruments—such as carbon taxes.5 There is no reason to believe that these more efficient markets, on their own, will tackle the carbon problem.

The downscaling process is complex and imperfect, in part because lots of local factors affect how broad changes in the climate are manifest where people actually live—along coastlines, near wildfire zones, in cities struggling with heat stress, and the like.

Existing research shows that there is a huge difference in the impact on public welfare from scenarios where climate change affects a society that doesn’t have an adaptation plan compared with a society that takes active adaptive measures.

For example, the most recent U.S. climate-impact assessment released in November 2018 demonstrates that active adaptation measures can radically reduce losses from some climate impacts—often with benefits that far exceed the costs.8 Extreme climate change is going to be ugly and will require hard choices—such as which coastlines to protect or abandon.

Such techniques might also make it possible to rely more heavily on market forces to weigh which options generate private and public welfare—if so, AI could help reduce one of the greatest dangers as societies develop adaptation strategies, which is that they commit vast resources to adaptation without guiding resources to their greatest value.

High levels of uncertainty, along with acute private incentives that can mis-allocate resources—for example, local construction firms and organized labor might favor some kinds of adaptive responses (e.g., building sea walls and other hardened infrastructure) even when other less costly options are available—mean that adaptation needs could generate a massive call on resources and thus a massive opportunity for mischief and mis-allocation.

As a matter of geophysics, climate change harms public welfare when general perturbations in the oceans and atmosphere get translated into specific climatological events that are manifest in specific places—specific coastlines, mountainous regions, public lands, and natural ecosystems.

Climate change is perhaps the largest market failure the world has seen so far—emissions of warming gases have global external consequences and the failure to impose emission taxes or other incentives means that firms and individuals are causing higher emissions and greater externalities than warranted.

For example, one way to cut the impacts of climate change on agriculture is to adopt early warning crop forecasting systems—so that farmers can adjust seeds, cropping methods, and planting times (among other variables) to reduce harmful impacts from the vagaries of weather.

To save us from a Kafkaesque future, we must democratise AI

Picture a system that makes decisions with huge impacts on a person’s prospects – even decisions of life and death.

Fast forward 100 years and artificial intelligence and data-driven computer systems are frequently portrayed in a similar way by their critics: increasingly consequential, yet opaque and unaccountable.

In other words, the “intelligence” in “artificial intelligence” is not the intelligence of the human individual – not that of the composer, the care worker or the doctor – it is the systemic intelligence of the bureaucracy, of the machine that processes vast amounts of data about people’s lives, then categorises them, pigeonholes them, makes decisions about them, and puts them in their place.

Of course, there are countless ways in which AI and related technologies can be used to empower people: for example, to bring better medical care to more of us, and to provide access to many other services, from digital personal assistants to tailored online learning.

But at the same time, they risk perpetuating injustice because, for all that they are the newest and shiniest of technologies, they also embody the biases of the past – the reductionist systemic thinking and institutional biases of their origins.

By default, these Kafkaesque systems will perpetuate existing forms of discrimination, and even exacerbate them – a case in point being Amazon’s now-abandoned recruitment algorithm, which learned from previous records what kind of people the company usually employs, and on the basis of this downgraded new applicants whose CVs indicated they were women.

The situation for people of colour is equally difficult: last month more than 100 researchers were denied visas for travel to Canada to attend NeurIPS, one of the most important AI conferences.

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