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AI has many existential risks like the immediate issues of bias, discrimination, and intellectual property violations. AI has been found to display racial bias in things like hiring, and creating harmful deepfakes. Things like legal uncertainties also remain regarding data usage, and copyright.

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Regulators are focusing on real AI risks over theoretical ones. Good. - Document - Gale In Context: Opposing Viewpoints

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Ai can make the quality of life better for many by helping people with disabilities, and accessibility in general. AI is already helping elderly with mobility. Its improving life for many disabled people.

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There is no denying that Artificial Intelligence has changed our lives. However, some might argue if it’s for the better.

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AI can be misused, and have unfair bias. AI may not treat all users the same. If its not trained it can lead to racial and gender biases, causing users to be treated unequally and unfairly.

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There is no denying that Artificial Intelligence has changed our lives. However, some might argue if it’s for the better.

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AI is being incorporated into everyday life which benefit's businesses and people. It is enhancing many industries and things like medical progression, customer experience, data analysis, ad more. It's more efficient in fields from retail to healthcare, even manufacturing and minimalize human error. Its creating more job opportunities and reshaping industries.

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Artificial intelligence (AI) is quickly becoming an essential part of everyday life. If you've used a self-service kiosk to check in before a flight, typed into a search bar and been given suggested keywords, or even switched the cruise control on during a long road trip, you've benefited from AI. Even more so, businesses are finding ways to optimize daily operations, stay connected with customers, and gain a competitive edge to accelerate growth through AI. The presence of AI is reshaping our world, and every day more job opportunities -from data scientists to information managers to software developers-are opening to new employees.

Here are seven ways artificial intelligence is impacting and improving our lives:

Companies are using AI technology to streamline their daily processes, analyze upcoming trends, forecast growth, and predict outcomes. For instance, anytime a customer places an item into their shopping cart on the websites of some major retailers, they are immediately given an additional suggested item to purchase based on an advanced algorithm. This algorithm has been programmed to compare thousands of other customers who have purchased similar items and make an informed suggestion. Additionally, social media platforms use a form of applied AI, known as machine learning, to display specific content to their users, and the more an individual uses the platform, the more the AI learns about them. By utilizing extensive neural networking, machine learning becomes superior in smart-decision making.

Automation is a major benefit of artificial intelligence in the business world. Businesses use automation to stay connected with new and returning customers through auto-reply emails, appointment reminders, and feedback surveys. If you've ever purchased a coffee and received an instantaneous text receipt, that's just one example of how AI is improving business practices. Furthermore, many online retailers rely on automation through drop shipment suppliers to streamline their processes, reduce the need for large storage facilities, and increase their efficacy. Through limiting human input by way of automation, businesses can make better use of their employees' skills and time.

Modern medicine has also embraced AI in helping doctors and nurses diagnose and treat patients without requiring an expensive or time-consuming hospital visit. For example, doctors can track a diabetic patient's glucose levels with the assistance of a glucose monitoring app, and that same patient can get real-time data about their health from the comfort of their home. Patient records and medical history can be shared within seconds from hospital to hospital through online portals, and crucial information can be gathered for community health outcomes, as seen with recent at-home tracing during the COVID-19 pandemic. Essentially, medical professionals can focus more on the needs of the patient and community while AI does the busy work.

Improved Customer Experience

The days of calling for customer service and waiting on hold to speak with someone are quickly becoming a thing of the past. Many companies now use online chatbots to make responding to and problem-solving for customer concerns a simpler process. Through programmed natural language processing (NLP), chatbots can learn and mimic natural human language. Chatbots also use prediction software to learn and adapt to each customer's inquiry, providing fast and customer-centered solutions.

Not only does AI improve customer experience but it also allows for heightened security measures. Using deep learning, an advanced level of machine learning, companies can employ encryption software and deep neural networks to protect sensitive information. And as more and more personal information is online, the demand for cybersecurity professionals will only increase.

Research and Data Analysis

With the assistance of AI, research and data scientists are able to better analyze patterns, predict outcomes, and make adjustments in half the time. Information that would have taken months to collect now can be done in minutes, if not seconds. For example, a language learning app, like Duolingo or Babbel, might discover that half of their users plateau in fluency after three months of learning and incorporate more supportive lessons to fill that gap. Or a meal delivery service might use an algorithm to learn that stay-at-home moms more regularly check their emails and meal plan in the mornings and pivot their email marketing to gain the best results. The wealth of knowledge that's gained from artificial intelligence research and data analysis is indispensable.

More and more, businesses are looking for ways to increase productivity, and AI helps eliminate monotonous, repetitive tasks that often take time away from an efficient workday. It's estimated that workers spend two and a half hours every day reading and responding to emails, making "inbox zero" (the email management strategy that aims to keep one's inbox empty) truly a myth. Browser extensions, such as Grammarly or Hemingway, use an AI program to automatically correct spelling and writing errors, reducing the time needed for proofreading, and email plug-ins, like Boomerang, perform repetitive tasks by automatically scheduling email responses.

Additionally, companies are now using robotic process automation (RPA) that can be programmed to interact with a system in the same way human intelligence would. RPA takes on repetitive tasks, like cross-checking invoices with purchase orders or ordering products when stock levels hit a limit, enabling workers to focus on value-added work versus repetition.

Minimizing human error is another essential benefit of AI. Learning algorithms help determine potential scenarios for error and make real-time corrections. When applied, manufacturing companies can closely monitor output, increase employee safety, and reduce the chances of production errors. Shipping industries can account for potential input inaccuracies, shipping delays, or lost goods, therefore limiting revenue loss. And even healthcare providers can increase patient care and outcomes by ensuring a patient's test result does not go overlooked. Through using AI as a tool to help minimize human error, every industry increases its potential for success.

Are you interested in joining the cutting-edge field of AI? Then earning a degree to set you up for success is essential. WGU's bachelor's degree in computer science provides students with all the knowledge and tools needed to jump into an exciting career in artificial intelligence. Graduates will have the mobility to become data scientists, computer engineers, software designers, and so much more. Additionally, the master's degree in data analytics provides further mastery in the field of data knowledge and development.

Frequently Asked Questions

What is artificial intelligence?

Artificial intelligence is the use of computer programs to complete tasks and solve problems previously required by human intelligence and time.

How does artificial intelligence work?

Artificial intelligence works by processing data through pre-determined algorithms, looking at patterns, and predicting usable measurables and outcomes.

How will artificial intelligence change the world?

Most importantly, AI will give individuals and industries the ability to strengthen their customer care, increase their job performance, and reimagine new possibilities for the future.

Take a look at other articles from WGU. Our articles feature information on a wide variety of subjects, written with the help of subject matter experts and researchers who are well-versed in their industries. This allows us to provide articles with interesting, relevant, and accurate information.

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All the Benefits Of Artificial Intelligence

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To make the most of new technology like AI, we need to learn all about its potential first. Find out how artificial intelligence affects the world around us.

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AI can benefit things like society, governments and businesses. It has already improved efficiency in industries along with productivity. Its good in items such as automation of repetitive tasks and research processes. Ai is used in healthcare, making serious impacts and improvement's like patient diagnosis, medical document transcription, and drug discovery and development. Its also used in transportation, customer service, and even education.

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Even people with computer science or mathematics degrees may find AI difficult to understand - no less define - and those of us who are merely tech savvy (or not) may find it impossible to understand the nitty gritty of an AI model.

The good news is, we don't have to. Simply put, artificial intelligence is a broad field of computer science that develops software, systems and machines that are able to complete tasks that, until recently, could only be accomplished through human intelligence.

Though there has been quite a stir about AI since late 2022, it's actually been around much longer. Examples of AI include:

Digital maps from Google and Apple

Autocorrect spelling features

Chatbots

Search and recommendation algorithms

On a broader basis, many like to think of AI in two ways, narrow AI and artificial general intelligence. In this construct, narrow AI specializes in a dedicated area, such as analyzing X-rays or MRI images. Artificial general intelligence, on the other hand, refers to acquiring and synthesizing knowledge from a variety of sources to provide a more humanlike output on a given topic.

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Is AI Good or Bad for Society? | University of Phoenix

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Are you asking, "Is AI good or bad for society?" Read how AI can be used for good, as well as the bad aspects of AI technology.

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AI has aided in many areas of our modern world. AI has impacted things like healthcare, transportation, customer service, finance, agriculture, climate, cybersecurity and much more. Ai has future regulations to ensure responsible AI use from consumers. Ai is shaping the future of our personal lifes, industries.

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9 Benefits of Artificial Intelligence (AI) in 2024

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Explore 9 key benefits of AI in 2024, from enhanced healthcare to advanced cybersecurity and economic growth

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The world is quickly revolutionizing through artificial intelligence. There are significant affects of AI in our world already such as deployments in finance, national security, health care, criminal justice, transportation, and more. The developments are making impactful economic and social benefits. Although all this, human choices about software greatly affect the way that decisions and how AI is integrated into organizational routines. AI may be the single most influential innovation humans made in history.

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Research

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. ¹ A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values. ²

In order to maximize AI benefits, we recommend nine steps for going forward:

Encourage greater data access for researchers without compromising users' personal privacy,

invest more government funding in unclassified AI research,

promote new models of digital education and AI workforce development so employees have the skills needed in the 21st-century economy,

create a federal AI advisory committee to make policy recommendations,

engage with state and local officials so they enact effective policies,

regulate broad AI principles rather than specific algorithms,

take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,

maintain mechanisms for human oversight and control, and

penalize malicious AI behavior and promote cybersecurity.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics. ⁵ Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved "experience" is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways. ⁶

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that "artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030." ⁷ That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that "AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption." ⁸ Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Finance

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion.⁹ According to observers in that sector, "Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check."¹⁰ In addition, there are so-called robo-advisers that "create personalized investment portfolios, obviating the need for stockbrokers and financial advisers."¹¹ These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels. ¹⁴

National security

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time-if not eventually in real time-thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position "humans in the loop" in a hyperwar scenario will ultimately dictate the West's capacity to be competitive in this new form of conflict. ¹⁷

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a "thinking" defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating "WannaCry" and "Petya" viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China's State Council issued a plan for the country to "build a domestic industry worth almost $150 billion" by 2030.¹⁸ As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs.¹⁹ The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well.²⁰

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that "detects lymph nodes in the human body in Computer Tomography (CT) images." ²¹ According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they "predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital." ²²

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven "Strategic Subject List" that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity. ²³

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates. ²⁴

However, critics worry that AI algorithms represent "a secret system to punish citizens for crimes they haven't yet committed. The risk scores have been used numerous times to guide large-scale roundups." ²⁵ The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector. ²⁸

Autonomous vehicles-cars, trucks, buses, and drone delivery systems-use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps. ²⁹

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key-not the physical car or truck itself.

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services-such as Uber and Lyft in the United States, Daimler's Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China-demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service. ³²

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred. ³³ Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call-whether a patient can be treated on-site or needs to be taken to the hospital-by taking into account several factors, such as the type of call, location, weather, and similar calls. ³⁴

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.

Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a "City Hall To Go" that makes sure underserved communities receive needed public services. It also has deployed "cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots." San Francisco has certified 203 buildings as meeting LEED sustainability standards. ³⁵

Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology. Among the top applications noted in the report are "smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement." ³⁶

These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times.

At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices? What about questions of legal liability in cases where algorithms cause harm? ³⁷

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a "data-friendly ecosystem with unified standards and cross-platform sharing." AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are "accessible for exploration" in the research community is a prerequisite for successful AI development. ³⁸

According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U.S. ranks eighth overall in the world, compared to 93 for China. ³⁹

But right now, the United States does not have a coherent national data strategy. There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers.

Biases in data and algorithms

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person's face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, "If your facial recognition data contains mostly Caucasian faces, that's what your program will learn to recognize." ⁴² Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody's risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated-what school our children go to and what opportunities they have. We don't have to bring the structural inequalities of the past into the future we create. ⁴³

AI ethics and transparency

Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made. ⁴⁴

In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans-based Bricolage Academy "gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school's broad geographic area." ⁴⁵ Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don't like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. ⁴⁶

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have "the right to opt out of personally tailored ads" and "can contest 'legal or similarly significant' decisions made by algorithms and appeal for human intervention" in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the "black box" operates. ⁴⁷

Legal liability

There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation's facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms. ⁴⁸ The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.

In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm "requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service." By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. ⁴⁹ But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity. ⁵⁰ Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. ⁵¹ As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.

Google long has made available search results in aggregated form for researchers and the general public. Through its "Trends" site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy. ⁵² That helps people track movements in public interest and identify topics that galvanize the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events.

In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients.

There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning.

Increase government investment in AI

Promote digital education and workforce development

As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.

For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. ⁵⁷ The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be "big picture" thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM's Teacher Advisor program, utilizing Watson's free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. ⁵⁸ As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.

In order to move forward in this area, several members of Congress have introduced the "Future of Artificial Intelligence Act," a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a "climate of investment and innovation to ensure the global competitiveness of the United States," "optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce," "support the unbiased development and application of artificial intelligence," and "protect the privacy rights of individuals." ⁵⁹

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration 540 days after enactment regarding any legislative or administrative action needed on AI.

This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

According to the legislation's developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won't go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. ⁶² It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, "Regulations prohibit any automated decision that 'significantly affects' EU citizens. This includes techniques that evaluates a person's 'performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.'" ⁶⁵ In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the "black boxes" and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, "The key problem confronting predictive analytics is really transparency. We're in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing." ⁶⁶

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning "cyberbullying, stock manipulation or terrorist threats," as well as "entrap[ping] people into committing crimes." Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, "An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information." ⁶⁷ His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for "nondeception" and "honesty," according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness. ⁶⁸

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a "voting-based system" that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account. ⁶⁹ This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. ⁷⁴ Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.
Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project. The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars. Support for this publication was generously provided by Amazon. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.

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How artificial intelligence is transforming the world | Brookings

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Darrell West and John Allen examine the societal and political aspects of developing artificial intelligence technologies.

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AI can have many issues such as things such as fraud, theft, scams, political disruption, mass unemployment, education and much more. AIs already taking many jobs in the workforce causing many to lose jobs and stuff like the customer service industry almost being completely taken over by artifical intelligence. Its impacting education a lot, by disrupting many children's learning, and the education system as a whole. Laws and regulations will eventually need to come into place to stop these things. Finally there's many ethical/personal issues with it like standards of society, human autonomy, and so much more. AI growing rapidly can cause many more issues.

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AI, like most major technological advancements, can be used for good or for ill. In the worst cases, there will be bad people and organisations who use the new technology to perpetrate physical, psychological, financial and other harms. In other cases, people will be negligent in their use of AI and not take reasonable steps to foresee the harms it could cause. In still other cases, in the rush to get their application to market, unintended negative or harmful consequences may occur. Below are just a few examples.

Fraud, theft, scams, political disruption and more

AI makes it possible to create deep-fakes, ie fake representations of real people or events. Anyone who spends anytime on the Internet will know that there is a plethora of images, audio and video freely accessible. While some uses of deep-fakes are done for innocent fun, others can amount to spreading false information in the middle of a tight political race. In business deep-fakes can mislead people into believing that a product or service has the endorsement of a particular person or company. Deep-fakes can be used to commit fraud or make other deceitful representations made for illegal and dishonest purposes. It is also possible that deep-fakes become so common that people doubt the legitimacy of all such people or products.

Fake content can cause havoc and panic in the middle of a pandemic, cause chaos in share markets, obstruct justice or falsify information. Criminals are taking advantage of the technology to conduct misinformation campaigns, commit fraud , obstruct justice, sow dissent and division, and even bring down an organisation or even a government.

Another important issue is the potential of AI to breach security, facilitate identity theft, fraud, promote scams, and other crimes. Once an AI developer has such information it is a small step to create multiple versions of that person. These deep-fakes can be used to get access to health data, bank accounts and other important documents and information.

Designers of AI systems need to be aware of the harms that may be facilitated by this new technology. Ethical behaviour within organisations, effective systems of quality control, security and authenticity, and a culture of ethical behaviour are all vital. Externally, governments and industry bodies must also develop laws, regulations and standards that promote the ethical use of AI.

Lessening of Individual Autonomy and human interaction

Some experts fear that with increasing automation, we humans will become bored and lazy. We are not too far away from the reality that the world will be dominated and run more by artificial intelligence. What will this mean for the human race. Will we become passive, bored, lazy and out of touch? Will this disempowerment create and invite a takeover by artificial intelligence systems? Or, will a few elites, using these new and powerful systems, be in total charge with the result that individual freedom will be severely limited

An example of how AI can empower those whose agenda are to gain control over people and limit freedom is found in Carol Roth’s NY Times Best Seller, You Will Own Nothing: Your War with a New Financial World Order and How to Fight Back. The author and entrepreneur paints a picture of what would happen if a new financial world order controlled by global elites are able to gain the type of control made possible, for example, by eliminating all hard currency and enforcing a system of digital currency under the control of a group of government, international organisations, business and technology elites. She argues that a system of ‘social credits’ would accompany such a system enabling the elites to shut down dissent and control the general population. It will result in debt, deprivation and desperation. It will mean people own fewer assets and that we have less control over their lives thereby lessening the ability to protect one’s wealth now or for future generations.

These questions raise important philosophical, psychological, moral, governance, legal and ethical questions that must be addressed if society is to fully benefit from AI while at the same time managing the significant risks involved.

Mass Unemployment

As mentioned above, the workforce in an AI dominated world may be a dystopia for those who lose their jobs. A lot of industry disruption and job destruction will have to be carefully managed. Governments will have to carefully plan the transition for those industries that are severely disrupted.

Major service functions such as customer service centres are likely to be heavily hit as AI applications coupled with robotics rapidly replace many of these types of roles. For example, most readers have have experienced a chat with a company’s automated answering service. AI ChatBots will become increasingly popular and will replace many of the humans now filling those roles. Customer service A 2022 study from the tech research company Gartner predicted that chatbots will be the main customer service channel for roughly 25% of companies by 2027.

Another example is the fast-food industry. In common with thousands of other young people, my daughters gained their first work experience at a McDonald’s restaurant. Today, customers order via an on-screen kiosk In China AI robotic chefs can cook your food. Other robots can wait on tables and take your order.

At the other end of the spectrum, there are likely to be significant shortages of those people who have the skills to take up the new positions required to support the growth in the AI related fields. There will be a significant battle for talent. And, as in all battles, there will be wounded and casualties and wounded, winners and losers, the dominant and the dominated.

Education

AI presents many challenges for education. Students, faculty and administration will see their systems severely challenged by new AI applications. For academics, it will significantly change aspects of research and scholarship. Many of these developments will bring improvements. The ‘bad’, however, is that it riding this major wave of innovation will require vision, talent, resources, training, resilience and more. Many will fail. The system will be disrupted. The impact of how this all plays out remains unknown, uncertain, and unpredictable. A case in point is the decades long development of computer assisted learning packages. They have been very slow to catch on due to many factors: teacher unions fear job losses; the current model is built on the assumptions based on age level rather than development/skill level; educators do not have adequate technology skills; the financial models have not been right and authors are not sufficiently rewarded; people are generally highly resistant to change.

The other challenge for education is to define how it can best serve society by providing the learning, research and training required to meet the multiple needs and demands if society is to gain the benefits of AI and manage the potential disadvantages and harms that may be caused.

Law

At the macro level, laws and regulations will be required to provide the governance framework to guide society in the Age of the Machine. This will be especially challenging given the geographic limits on the application of law and fact that different countries will have different approaches. Underlying the formal legal regime will also be the reality that countries will have different ethical standards. Some countries will see it in their interest to press ahead, despite the risks, in order to gain a competitive advantage over other countries.

At the ground level it will also be important for designers and users of AI systems to be aware of new problems that might emerge. AI systems can and do discriminate. AI tools have the potential to embed unlawful biases and discrimination and do so on a system-wide scale and in a non-transparent way. This can impact decisions on who gets a loan, who gets hired, who gets favourable administrative decisions, who gets monitored by the police, etc. AI systems also use information, pictures and other intellectual property, all of which is loaded up into the application. This raises serious issues regarding the potential intellectual property violations that might occur.[2] AI also creates IP thus raising further questions about whether the existing intellectual property legal regime will include IP creation by non-humans.

Ethical and Personal Issues

As mentioned above, the growing development and application of AI to all sectors of human activity raises many ethical issues. It threatens the degree of human autonomy, challenges existing rules, laws and standards in society, threatens a loss of control, challenges expectations of privacy, and so on. A major question is the extent to which we can achieve agreement among nations or even between public and private sectors and other groupings within nations regarding central ethical issues raised by AI. For example, what should be the degree of transparency underlying the use of AI systems and applications? How can principles of justice and fairness be promoted and protected by AI development? How can we regulate and promote fairness, non-maleficence, responsibility and privacy in the development and use of AI?

There is also a psychological dimension to AI adoption that must be considered. While AI and its applications have grown rapidly, one should not underestimate the limitations and challenges stemming from the natural tendency of humans to cope with, resist and even fear change. For example, a major roadblock to the implementation of AI applications to augmented medicine is the reality that doctors and other health care providers have resisted such changes and not been prepared for it. Many patients, however, have welcomed its advantages in providing for a greater autonomy and a more personalized treatment.

More than this, as the impact of AI grows it begins to challenge the relationship between humans and machines. In doing so, it challenges traditional notions of identity, sexuality, gender, relationships and rules as new forms of discourse emerge to explain and govern relationships between machine and human intelligence.