The Pros And Cons Of Migration Into The United States

Sunday, October 17, 2021 8:17:18 AM

The Pros And Cons Of Migration Into The United States



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This includes many of the daily and often unpleasant interactions we have with government today, from traffic offenses, manifestation of civil discontent, unfair treatment in commercial and legal processes, and the like. A simple example: One of the most persistent political problems in the United States is the gerrymandering of political boundaries to benefit incumbents. Electoral divisions created by an algorithm to a large degree eliminate gerrymandering and when open and debatable, can be modified to improve on that result. Participants in this study were in substantial agreement that the abundant positives of accelerating code-dependency will continue to drive the spread of algorithms; however, as with all great technological revolutions, this trend has a dark side.

Most respondents pointed out concerns, chief among them the final five overarching themes of this report; all have subthemes. Advances in algorithms are allowing technology corporations and governments to gather, store, sort and analyze massive data sets. Experts in this canvassing noted that these algorithms are primarily written to optimize efficiency and profitability without much thought about the possible societal impacts of the data modeling and analysis.

The goal of algorithms is to fit some of our preferences, but not necessarily all of them: They essentially present a caricature of our tastes and preferences. My biggest fear is that, unless we tune our algorithms for self-actualization , it will be simply too convenient for people to follow the advice of an algorithm or, too difficult to go beyond such advice , turning these algorithms into self-fulfilling prophecies, and users into zombies who exclusively consume easy-to-consume items. Every time you design a human system optimized for efficiency or profitability you dehumanize the workforce.

That dehumanization has now spread to our health care and social services. When you remove the humanity from a system where people are included, they become victims. Who is collecting what data points? Do the human beings the data points reflect even know or did they just agree to the terms of service because they had no real choice? Who is making money from the data?

There is no transparency, and oversight is a farce. A sampling of excerpts tied to this theme from other respondents for details, read the fuller versions in the full report :. Two strands of thinking tie together here. One is that the algorithm creators code writers , even if they strive for inclusiveness, objectivity and neutrality, build into their creations their own perspectives and values. The other is that the datasets to which algorithms are applied have their own limits and deficiencies. Moreover, the datasets themselves are imperfect because they do not contain inputs from everyone or a representative sample of everyone. The two themes are advanced in these answers:. Most people in positions of privilege will find these new tools convenient, safe and useful.

The harms of new technology will be most experienced by those already disadvantaged in society, where advertising algorithms offer bail bondsman ads that assume readers are criminals, loan applications that penalize people for proxies so correlated with race that they effectively penalize people based on race, and similar issues. Much of it either racial- or class-related, with a fair sprinkling of simply punishing people for not using a standard dialect of English. To paraphrase Immanuel Kant, out of the crooked timber of these datasets no straight thing was ever made.

A sampling of quote excerpts tied to this theme from other respondents for details, read the fuller versions in the full report :. One of the greatest challenges of the next era will be balancing protection of intellectual property in algorithms with protecting the subjects of those algorithms from unfair discrimination and social engineering. First, they predicted that an algorithm-assisted future will widen the gap between the digitally savvy predominantly the most well-off, who are the most desired demographic in the new information ecosystem and those who are not nearly as connected or able to participate.

Second, they said social and political divisions will be abetted by algorithms, as algorithm-driven categorizations and classifications steer people into echo chambers of repeated and reinforced media and political content. Two illustrative answers:. And that divide will be self-perpetuating, where those with fewer capabilities will be more vulnerable in many ways to those with more.

Brushing up against contrasting viewpoints challenges us, and if we are able to actively or passively avoid others with different perspectives, it will negatively impact our society. It will be telling to see what features our major social media companies add in coming years, as they will have tremendous power over the structure of information flow. The overall effect will be positive for some individuals. It will be negative for the poor and the uneducated. As a result, the digital divide and wealth disparity will grow. It will be a net negative for society. The spread of artificial intelligence AI has the potential to create major unemployment and all the fallout from that. What will then be the fate of Man?

The respondents to this canvassing offered a variety of ideas about how individuals and the broader culture might respond to the algorithm-ization of life. They argued for public education to instill literacy about how algorithms function in the general public. They also noted that those who create and evolve algorithms are not held accountable to society and argued there should be some method by which they are.

Representative comments:. What is the supply chain for that information? Is there clear stewardship and an audit trail? Were the assumptions based on partial information, flawed sources or irrelevant benchmarks? Did we train our data sufficiently? Were the right stakeholders involved, and did we learn from our mistakes? The upshot of all of this is that our entire way of managing organizations will be upended in the next decade. The power to create and change reality will reside in technology that only a few truly understand. So to ensure that we use algorithms successfully, whether for financial or human benefit or both, we need to have governance and accountability structures in place.

Easier said than done, but if there were ever a time to bring the smartest minds in industry together with the smartest minds in academia to solve this problem, this is the time. That coping strategy has always been co-evolving with humanity, and with the complexity of our social systems and data environments. Becoming explicitly aware of our simplifying assumptions and heuristics is an important site at which our intellects and influence mature.

What is different now is the increasing power to program these heuristics explicitly, to perform the simplification outside of the human mind and within the machines and platforms that deliver data to billions of individual lives. It will take us some time to develop the wisdom and the ethics to understand and direct this power. The first and most important step is to develop better social awareness of who, how, and where it is being applied.

We need some kind of rainbow coalition to come up with rules to avoid allowing inbuilt bias and groupthink to effect the outcomes. Finally, this prediction from an anonymous participant who sees the likely endpoint to be one of two extremes:. I suspect utopia given that we have survived at least one existential crisis nuclear in the past and that our track record toward peace, although slow, is solid. Following is a brief collection of comments by several of the many top analysts who participated in this canvassing:.

When they make a change, they make a prediction about its likely outcome on sales, then they use sales data from that prediction to refine the model. Their model also makes predictions about likely outcomes on reoffending , but there is no tracking of whether their model makes good predictions, and no refinement. This frees them to make terrible predictions without consequence. The algorithms are not in control; people create and adjust them. However, positive effects for one person can be negative for another, and tracing causes and effects can be difficult, so we will have to continually work to understand and adjust the balance.

The methods behind the decisions it makes are completely opaque, not only to those whose credit is judged, but to most of the people running the algorithm as well. In some cases there is no way to tell exactly why or how a decision by an algorithm is reached. And even if the responsible parties do know exactly how the algorithm works, they will call it a trade secret and keep it hidden.

There is already pushback against the opacity of algorithms, and the sometimes vast systems behind them. These things have the size, scale, and in some ways the importance of nuclear power plants and oil refineries, yet enjoy almost no regulatory oversight. This will change. At the same time, so will the size of the entities using algorithms. They will get smaller and more numerous, as more responsibility over individual lives moves away from faceless systems more interested in surveillance and advertising than actual service.

Machines have literally become black boxes — even the developers and operators do not fully understand how outputs are produced. There is a larger problem with the increase of algorithm-based outcomes beyond the risk of error or discrimination — the increasing opacity of decision-making and the growing lack of human accountability. We need to confront the reality that power and authority are moving from people to machines. That is why AlgorithmicTransparency is one of the great challenges of our era.

I have heard that people who refuse to be used by Facebook are discriminated against in some ways. Perhaps soon they will be denied entry to the U. Even if the U. These outcomes will probably differ in character, and in our ability to understand why they happened, and this reality will make some people fearful. But as we see today, people feel that they must use the internet to be a part of society. Even if they are fearful of the consequences, people will accept that they must live with the outcomes of these algorithms, even though they are fearful of the risks.

If the algorithms are built well and robustly, the opportunity to insert this inefficiency e. Contextualized benefits always come at the cost of broadcasting that extended contextual data: in order to serve you better, those providing services need access to more information about you. Access control. Allowing companies to scan your chip for identification inherently also gives them access to where you are within their establishment. Data leaks. Any new technology is always rife with bugs and exploits. Putting so much information and reliance on a single chip makes it a prime target for hackers and other no-gooders.

If information is writable in addition to readable , there is also potential for impersonation or data corruption. Replacement hardware. No universal standards. Unfortunately due to the wide variety of digital identification systems in place, no one card handles them all. Bodily migration. If proper care is not taken of implanted chips, they are capable of migrating within the body. Medical treatment. Utopian claims: Digital world augmentations.

Seamless 2-factor authentication. The security adage for optimal security is to use something you know passwords in combination with something you have often your phone, right now. Your phone has plenty of downsides it can be stolen, lost, dead, hacked, wiped, etc that are solved by a seamless solution that could augment passwords for increased security without the admittedly mild inconvenience of current 2FA solutions. Dystopian claims: Revolutionary firepower.

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