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Lyons announced the Partnership on AI’s first three working groups, which are dedicated to fair, transparent, and accountable AI; safety-critical AI; and AI, labor, and the economy. Each group will have a for-profit and nonprofit chair and aim to share its results as widely as possible. Lyons says these groups will be like a “union of concerned scientists.” “A big part of this is on us to really achieve inclusivity,” she says. Tess Posner, the executive director of AI4ALL, a nonprofit that runs summer programs teaching AI to students from underrepresented groups, showed why training a diverse group for the next generation of AI workers is essential. Currently, only 13 percent of AI companies have female CEOs, and less than 3 percent of tenure-track engineering faculty in the US are black. Yet an inclusive workforce may have more ideas and can spot problems with systems before they happen, and diversity can improve the bottom line. Posner pointed out a recent Intel report saying diversity could add $500 billion to the US economy.

“It’s good for business,” she says. These weren’t the first presentations at EmTech Digital by women with ideas on fixing AI. On Monday, Microsoft researcher Timnit Gebru presented examples of bias in current AI systems, and earlier on Tuesday Fast.ai cofounder Rachel Thomas talked about her company’s free deep-learning course and its effort to diversify the overall AI workforce. Even with the current problems achieving diversity, there are more women and people of color that could be brou

ght into the workforce.

“It’s clear that Germany has very distinct differences in its business structure and cultural makeup,” says Tony Danker, CEO of Be the Business, which campaigns to spread best practice on productivity to British companies. “There is real interest in continuous improvement and building business networks and institutions that focus on this. This spirit and activity feels eminently replicable even if the institutions are not.”

Indeed, a key difference between the U.K. and Germany lies in the training that workers receive. Although German managers are less likely to have higher educational qualifications, they have often received vocational training that builds workplace expertise. And despite the U.K. having more tertiary-educated managers than Germany, the Organization for Economic Cooperation and Development data show their actual skills, in literacy and data management, are lower.

The design is “based on what we know is affordable housing in Puerto Rico for a single family,” says Hector Ralat, an architect based in the firm’s Puerto Rican office. “But the focus was to alter the DNA of that knowledge and to put in the essential components that someone would need to sustain living conditions for at least two weeks, which is the recommended time here for someone to receive aid after a disaster.” The houses will likely cost around $120,000, a number that lets homeowners access favorable interest rates on mortgages. The units can be stacked on top of each other; in Villalba, most of the community will be three stories high (the solar will serve the whole building).

“We just know our product is better than stick-built construction in these types of dangerous environments,” says Paul Galvin, chairman and CEO of SG Blocks, the parent company of SG Residential. “Heavy-gauge steel structures are just designed to a high tolerance for the effects of climate change.” The houses, most of which will have two bedrooms, will start at $90,000 to $130,000. It might be possible to build cheaper houses, Galvin says, but the company is “trying to deliver product that is quality-driven, in that it’s going to be built once and it’s not going to be destroyed every storm.” The company is also working with a bank to create a mortgage that is similar in monthly cost to a car payment. The design can incorporate solar panels.

HiveCube, another modular housing company, is also using shipping containers, and has targeted a much lower cost–the houses start at $39,000 for a two-bedroom home. “We believe that your safety should not be a matter of income, but a given when you are planning to buy a home for your family,” says María Velasco, cofounder of HiveCube. The homes are designed to be fully off the grid, with solar power and batteries, a rainwater collection system, and a gray and black water treatment system that uses plants and bacteria to treat wastewater instead of septic tanks.

Today, states grapple with decarceration and deinstitutionalization, not necessarily because of an ethical recognition of the continuing harm of confinement and segregation, or because of an understanding of the intertwined histories of capitalism, white supremacy, ableism, and punishment in the United States, but because of a desire to curb public spending on social services. These include the very services that people need as alternatives to more oppressive edifices and as preventive measures to winding up in such places. While public neighborhood urban schools, public housing, and mental health clinics are shuttered, private companies and “not for profit” services partially fill the void.

Alternatives emerge when facilities shut their doors. Closures, as prison justice organizer Angela Davis suggests, provide an opportunity for not only a “radical imagining” of the kind of social landscape desperately needed—but also the moment to build it. As people move between different forms and scales of cages, and as patterns of surveillance and punishment morph, new forms of capture do emerge and yet resistance is also possible. The state often refuses to offer services in place of the ones that were shuttered, leaving the responsibility to the individual (or her family and the market). This is a moment to collectively demand, fund, and build public infrastructure that will move everyone closer towards a world that does not rely on segregation and confinement, or access to private capital, as its mode of dealing with structural inequities.

The biggest concern is that we might one day create conscious machines: sentient beings with beliefs, desires and, most morally pressing, the capacity to suffer. Nothing seems to be stopping us from doing this. Philosophers and scientists remain uncertain about how consciousness emerges from the material world, but few doubt that it does. This suggests that the creation of conscious machines is possible.

If we did create conscious beings, conventional morality tells us that it would be wrong to harm them — precisely to the degree that they are conscious, and can suffer or be deprived of happiness. Just as it would be wrong to breed animals for the sake of torturing them, or to have children only to enslave them, it would be wrong to mistreat the conscious machines of the future.

Anything that looks and acts like the hosts on “Westworld” will appear conscious to us, whether or not we understand how consciousness emerges in physical systems. Indeed, experiments with AI and robotics have already shown how quick we are to attribute feelings to machines that look and behave like independent agents.

Artificial intelligence may have cracked the code on certain tasks that typically require human smarts, but in order to learn, these algorithms need vast quantities of data that humans have produced. They hoover up that information, rummage around in search of commonalities and correlations, and then offer a classification or prediction (whether that lesion is cancerous, whether you’ll default on your loan) based on the patterns they detect. Yet they’re only as clever as the data they’re trained on, which means that our limitations—our biases, our blind spots, our inattention—become theirs as well.

The majority of AI systems used in commercial applications—the ones that mediate our access to services like jobs, credit, and loans— are proprietary, their algorithms and training data kept hidden from public view. That makes it exceptionally difficult for an individual to interrogate the decisions of a machine or to know when an algorithm, trained on historical examples checkered by human bias, is stacked against them. And forget about trying to prove that AI systems may be violating human rights legislation.

Data is essential to the operation of an AI system. And the more complicated the system—the more layers in the neural nets, to translate speech or identify faces or calculate the likelihood someone defaults on a loan—the more data must be collected.