Wednesday, June 16

Care bots are on the rise, replacing human caregivers | US News

IIf you use “care bots” on Google, you’ll see an army of robot butlers and nurses taking vital signs in hospitals, delivering red roses to patients, and serving juice to the elderly. For the most part, these are just sci-fi fantasies. Attention bots that already exist look different.

These attention robots look less like robots and more like invisible pieces of code, webcams, and algorithms. They can control who gets what test in the doctor’s office or how many hours of care a person with Medicaid receives. And they are everywhere. Increasingly, human caregivers work through and in conjunction with automated systems that establish recommendations, manage and monitor their work, and allocate resources.

They are emerging because the US has chronically underinvested in care infrastructure, relying heavily on informal family support and an industry sustained by low-paid workers, mostly immigrants and women of color. These workers have a median annual salary of $ 25,000 and nearly a quarter of the workforce lives below the federal poverty line. However, the demand for your work will skyrocket. In the United States, more than 50 million people are over the age of 65, and this number is expected to nearly double by 2060. The question looms: who will care for them?

There is growing faith that technology can fill this gap by rapidly building care systems at scale, with the help of artificial intelligence and remote monitoring. Exhausted and understaffed nursing home workers may have sensors and webcams to help them monitor the health and well-being of residents. The growing “AgeTech” industry could help seniors age in the comfort of their own homes.

As The Guardian reports today, for example, a company called CarePredict has produced a watch-like device that alerts caregivers if repetitive eating motions are not detected as expected, and one of its patents notes that it can infer whether someone he’s “using the bathroom.” . Another firm has created a technology that watches when someone fell asleep and if they took a bath.

Questions and answers

What is AI?


Artificial intelligence (AI) refers to computer systems that do things that normally require human intelligence. While the holy grail of AI is a computer system indistinguishable from a human mind, there are several specialized, but limited, forms of AI that are already part of our everyday lives. AI can be used with cameras to identify someone based on their face, to feed virtual companions, and to determine if a patient is at high risk for disease.

AI should not be confused with other types of algorithms. The simplest definition of an algorithm is that it is a series of instructions necessary to complete a task. For example, a thermostat in your home is equipped with sensors to detect the temperature and instructions to turn it on or off as needed. This is not the same as artificial intelligence.

The deployment of AI today has been made possible by decades of research on topics including computer vision, which allows computers to perceive and interpret the visual world; natural language processing, allowing them to interpret the language; Y machine learning, a way that computers improve as they find new data.

AI enables us to automate tasks, gather information from huge data sets, and complement the human experience. But a plethora of studies have also begun to document its pitfalls. For example, automated systems often train on huge amounts of historical digital data. As many widely publicized cases show, these data sets often reflect past racial disparities, from which artificial intelligence systems learn and replicate.

Furthermore, some of these systems are difficult to interpret by outsiders due to an intentional lack of transparency or the use of genuinely complex methods.

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Some of the uses of assistive technology are valid and valuable. But these tools can also hide human costs.

Automated decision making and artificial intelligence can undermine the autonomy of the very people these systems are meant to help. Home cameras, facial recognition systems, portable motion trackers, and hazard prediction models can make elderly and disabled people feel compelled to convert their homes into nursing homes. This undermines the focus on dignity and self-determination fundamental to independent living and community care.

Automated decision systems can also enforce policies that treat the poor, the elderly, the disabled, the immunosuppressed, and communities of color as throwaway. Within healthcare, technology is increasingly used to assess patients, direct the care of nurses, and support clinical judgments. But these systems often reproduce, and even worsen, the bias, because the data they use reflects long-standing inequalities in health care. For example, Zaid Obermeyer and his colleagues reported in Science In 2019, a system used to assign medical care to 200 million people a year in hospitals across the United States drastically underestimated the medical needs of African Americans.

In some states, governments have adopted automated decision-making tools to assess eligibility for Medicaid services, often without much public debate and little transparency about how decisions are made. For example, an algorithm in Arkansas was intended to more fairly distribute the hours of care allocated to individuals receiving home and community-based services. But he faced a wave of scrutiny for dramatically reducing the hours of people who depend on personal care assistants for basic activities of daily living, such as bathing, eating and going to the bathroom.

Surveillance in the name of care raises troubling questions about the privacy and autonomy of those in need of support. Technologies such as Electronic Visit Verification (EVV) have been introduced to monitor the delivery of care within homes using features such as GPS location tracking, but have left disabled and elderly service recipients and their workers with the feeling of being chained to an ankle monitor.

Many efforts to build caring bots are motivated by a genuine desire to repair cracks in a tense and fragmented system. The devastation caused by the Covid pandemic made clear our need for better care, not only in hospitals and clinics, but also in our homes, schools and streets. As the director of the National Domestic Workers Alliance, Ai-jen Poo, has urged us to recognize, the care industry was a “house of cards on the brink of collapse” long before the pandemic.

The pandemic and decades of grassroots organizing have encouraged the Biden administration to focus on investing in care work, sparking a new public conversation about care as critical public infrastructure. The Biden plan proposes to invest $ 400 billion to provide health care and personal care services to the elderly at home. While the plan places significant public investment at the heart of a revitalized care system, it does not reconcile the thorniest issues – policing, erosion of autonomy, and biases – that accompany the inevitable government reliance on health care management technologies. attention.

Attention bots are here. But their forays don’t have to lead to techno-dystopia. Our visions of the future for a caring society must be built on a foundation of justice and equity, dignity and autonomy, not just efficiency and scale. The most essential aspects of caring for one another (presence, compassion, connection) are not always easy, or even possible, to measure. The rise of attention robots runs the risk of creating a system in which we only value the parts of attention that can be converted into data.

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