February 8, Code-Dependent: Pros and Cons of the Algorithm Age Algorithms are aimed at optimizing everything.
They can save lives, make things easier and conquer chaos. Recipes are algorithms, as are math equations. Computer code is algorithmic. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms.
Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms.
GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence AI is naught but algorithms. The material people see on social media is brought to them by algorithms.
In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars.
Hacking, cyberattacks and cryptographic code-breaking exploit algorithms. Self-learning and self-programming algorithms are now emerging, so it is possible that in the future algorithms will write many if not most algorithms. Algorithms are often elegant and incredibly useful tools used to accomplish tasks. They are mostly invisible aids, augmenting human lives in increasingly incredible ways. However, sometimes the application of algorithms created with good intentions leads to unintended consequences.
Recent news items tie to these concerns: The British pound dropped 6.
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First, it had a team of humans edit the featurebut controversy erupted when some accused the platform of being biased against conservatives. So, Facebook then turned the job over to algorithms only to find that they could not discern real news from fake news. How Big Data Increases Inequality and Threatens Democracy, pointed out that predictive analytics based on algorithms tend to punish the poorusing algorithmic hiring practices as an example.
Well-intentioned algorithms can be sabotaged by bad actors. An internet slowdown swept the East Coast of the U. Algorithmic regulation will require federal uniformity, expert judgment, political independence and pre-market review to prevent — without stifling innovation — the introduction of unacceptably dangerous algorithms into the market.
On January 17,the Future of Life Institute published a list of 23 Principles for Beneficial Artificial Intelligencecreated by a gathering of concerned researchers at a conference at Asimolar, in Pacific Grove, California.
The use of algorithms is spreading as massive amounts of data are being created, captured and analyzed by businesses and governments. Some are calling this the Age of Algorithms and predicting that the future of algorithms is tied to machine learning and deep learning that will get better and better at an ever-faster pace.
While many of the U. Some 1, responded to this question about what will happen in the next decade: Will the net overall effect of algorithms be positive for individuals and society or negative for individuals and society? Respondents were allowed to respond anonymously; these constitute a slight majority of the written elaborations.
These findings do not represent all the points of view that are possible to a question like this, but they do reveal a wide range of valuable observations based on current trends. In the next section we offer a brief outline of seven key themes found among the written elaborations. All responses are lightly edited for style.
Algorithms will continue to spread everywhere There is fairly uniform agreement among these respondents that algorithms are generally invisible to the public and there will be an exponential rise in their influence in the next decade. We have already turned our world over to machine learning and algorithms. The question now is, how to better understand and manage what we have done?
Namely, how can we see them at work? Consider and assess their assumptions? Like fish in a tank, we can see them swimming around and keep an eye on them.
How are we thinking and what does it mean to think through algorithms to mediate our world? After all, algorithms are generated by trial and error, by testing, by observing, and coming to certain mathematical formulae regarding choices that have been made again and again — and this can be used for difficult choices and problems, especially when intuitively we cannot readily see an answer or a way to resolve the problem. In a technological recapitulation of what spiritual teachers have been saying for centuries, our things are demonstrating that everything is — or can be — connected to everything else.
Algorithms with the persistence and ubiquity of insects will automate processes that used to require human manipulation and thinking. These can now manage basic processes of monitoring, measuring, counting or even seeing. Our car can tell us to slow down. Our televisions can suggest movies to watch. A grocery can suggest a healthy combination of meats and vegetables for dinner. How can we see, and fully understand the implications of, the algorithms programmed into everyday actions and decisions?
The rub is this: Whose intelligence is it, anyway? So prediction possibilities follow us around like a pet.
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As information tools and predictive dynamics are more widely adopted, our lives will be increasingly affected by their inherent conclusions and the narratives they spawn. All of our extended thinking systems algorithms fuel the software and connectivity that create extended thinking systems demand more thinking — not less — and a more global perspective than we have previously managed. The expanding collection and analysis of data and the resulting application of this information can cure diseases, decrease poverty, bring timely solutions to people and places where need is greatest, and dispel millennia of prejudice, ill-founded conclusions, inhumane practice and ignorance of all kinds.
Our algorithms are now redefining what we think, how we think and what we know. We need to ask them to think about their thinking — to look out for pitfalls and inherent biases before those are baked in and harder to remove. That, by itself, is a tall order that requires impartial experts backtracking through the technology development process to find the models and formulae that originated the algorithms. Then, keeping all that learning at hand, the experts need to soberly assess the benefits and deficits or risks the algorithms create.
Who is prepared to do this? Who has the time, the budget and resources to investigate and recommend useful courses of action? This is a 21st-century job description — and market niche — in search of real people and companies. In order to make algorithms more transparent, products and product information circulars might include an outline of algorithmic assumptions, akin to the nutritional sidebar now found on many packaged food products, that would inform users of how algorithms drive intelligence in a given product and a reasonable outline of the implications inherent in those assumptions.
Good things lie ahead A number of respondents noted the many ways in which algorithms will help make sense of massive amounts of data, noting that this will spark breakthroughs in science, new conveniences and human capacities in everyday life, and an ever-better capacity to link people to the information that will help them.
They perform seemingly miraculous tasks humans cannot and they will continue to greatly augment human intelligence and assist in accomplishing great things. A representative proponent of this view is Stephen Downes, a researcher at the National Research Council of Canada, who listed the following as positive changes: Today banks provide loans based on very incomplete data.
It is true that many people who today qualify for loans would not get them in the future. However, many people — and arguably many more people — will be able to obtain loans in the future, as banks turn away from using such factors as race, socio-economic background, postal code and the like to assess fit.
Health care is a significant and growing expense not because people are becoming less healthy in fact, society-wide, the opposite is true but because of the significant overhead required to support increasingly complex systems, including prescriptions, insurance, facilities and more.
New technologies will enable health providers to shift a significant percentage of that load to the individual, who will with the aid of personal support systems manage their health better, coordinate and manage their own care, and create less of a burden on the system.
As the overall cost of health care declines, it becomes increasingly feasible to provide single-payer health insurance for the entire population, which has known beneficial health outcomes and efficiencies. A significant proportion of government is based on regulation and monitoring, which will no longer be required with the deployment of automated production and transportation systems, along with sensor networks.
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. 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.
The efficiencies of algorithms will lead to more creativity and self-expression. Digital agents will find the materials you need. Most respondents pointed out concerns, chief among them the final five overarching themes of this report; all have subthemes.
Humanity and human judgment are lost when data and predictive modeling become paramount 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 adviceturning 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.
Worse, they repackage profit-seeking as a societal good. We are nearing the crest of a wave, the trough side of which is a new ethics of manipulation, marketing, nearly complete lack of privacy. The Common Good has become a discredited, obsolete relic of The Past. Humans will lose their agency in the world. We are heading for a nightmare. I exaggerate for effect. But not by much. Biases exist in algorithmically-organized systems Two strands of thinking tie together here.
One is that the algorithm creators code writerseven 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.
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. The people writing algorithms, even those grounded in data, are a non-representative subset of the population. Garbage in, garbage out. Many dimensions of life will be affected, but few will be helped.
Oversight will be very difficult or impossible. 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.
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