In our guide, on artificial intelligence (AI) we explore a wide range of topics including the history, current state and potential future of AI. We also delve into the ethical and regulatory challenges that may emerge in the field of AI.
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The idea of Artificial intelligence (AI) holds promise for individuals, nations and the entire planet. It has the potential to simplify our lives tackle issues like climate change and social inequality improve peoples quality of life worldwide and ultimately pave the way for a future.
Nevertheless artificial intelligence (AI) also raises a host of concerns. These encompass issues related to plagiarism, reduced involvement or participation well as discrimination and bias. Additionally numerous societal challenges arise from it—such as disparities in wealth and power distribution job scarcity along with opportunities degradation—and many other intricate problems.
As stated in an article by The Guardian newspaper; human choices will shape the future of AI with its human capabilities. It is crucial, for our well being. That of future generations that we make wise decisions.
The primary objective of this article is to provide a guide, on the aspects concerning AI. We will explore the history and origins of AI giving credit to the pioneers who laid the foundation. Furthermore we will examine the state of AI. How it significantly impacts various aspects of our everyday lives often without us even realizing it.
Looking towards the future we will analyze advancements in AI. Consider different scenarios that may unfold. Lastly we will address the regulatory challenges associated with AI empowering individuals and organizations with insights to make informed decisions.
Without Further ado lets begin our journey by diving, into the captivating realm of science fiction.
Defining AI
For a long time, the idea of AI has been present in the human imagination. Even before the actual creation of AI, science fiction had already explored the idea of robots with artificial intelligence. Examples of this can be seen in popular works such as the Tin Man in Wizard of Oz, the humanoid robot Maria in Metropolis, and the War-Robot in Master of the World.
The concept of artificial intelligence (AI) expanded and evolved in the realm of science fiction, as depicted in works such as Philip K. Dick’s Do Androids Dream of Electric Sheep? or Ian McEwan’s Machines Like Me. These stories portrayed machines with increasingly human-like traits, eventually leading to the creation of robots that were nearly indistinguishable from humans. This development was further explored in later science fiction literature, as evidenced by the list of recommended books on artificial intelligence by Shepherd.com.
However, there are significant differences between fictional AI and real-life AI. While AI is a part of our daily lives, we do not currently interact with humanoid robots as far as we know. AI does not refer to intelligent beings that imitate or aim to destroy humanity, but rather practical applications such as Google Maps and Siri, automated systems that demonstrate human-level intelligence, or machines that act in a more human-like manner.
The functioning of AI involves the utilization of fast and intelligent algorithms, combined with vast quantities of data. Through this process, the technology is able to learn from data patterns and features, which then enhances its algorithms and processing capabilities. Essentially, AI serves as a replication of human intelligence in machines that are designed to mimic human thought processes.
The concept of ‘artificial intelligence’ can be attributed to any device that displays characteristics commonly associated with the human mind, including the ability to learn and solve problems.
Various Divisions of Artificial Intelligence
The field of artificial intelligence is vast, covering a variety of technologies, methodologies, and intellectual concepts, with ongoing discussions surrounding the ethical and philosophical implications of its application in various fields. Currently, there are seven main branches of AI being utilized, each with the potential to address real-world issues, improve efficiency, cut expenses, and save time. Below, we will outline each branch and provide examples of practical use cases.
Artificial intelligence
Machine learning refers to a type of analytic modeling that is widely recognized as one of the most common forms of artificial intelligence. It is also used as a broad term to encompass several other branches. By utilizing machine learning, software applications can improve their ability to predict outcomes without the need for explicit programming.
Machines are able to continuously evolve through the use of historical data inputs in machine learning, which enables them to predict present or future outputs. While it may often go unnoticed, predictive text is just one example of the widespread presence of machine learning.
Tensorflow, a popular commercial software library, is often the subject of discussion due to its capabilities in machine learning and artificial intelligence. This programme offers a range of features such as AutoDifferentiation, Eager execution, and multiple optimisers designed for training neural networks. Additionally, Tensorflow is freely available as an open-source platform.
A machine learning model using interconnected nodes
Neural networks are artificial intelligence systems that have the ability to acquire knowledge by processing external inputs and transmitting information between input units. This technology consists of interconnected units, which are likened to neurons by SAS, and they repeatedly perform processes to establish links and extract significance from data that was previously meaningless.
Neural networks, which are examples of machine learning, are based on the functioning of the human brain. Some applications of neural networks are sales forecasting, industrial process control, customer research, data validation, and targeted marketing for charities.
Artificial neural networks
Deep learning utilizes complex neural networks composed of numerous layers of processing units.
The technology makes use of the significant progress in computer processing and training methods to comprehend complex patterns, utilizing extensive datasets. For instance, Face ID authentication (https://machinelearning.apple.com/research/face-detection) utilizes biometric technology that employs a deep learning framework to identify characteristics from individuals’ faces and compare them with existing records.
Through the use of camera devices, deep learning technology is able to identify barcodes, text, and landmarks.
NLP (Natural Language Processing)
One of the frequently utilized AI systems is called natural language processing. It makes use of the capability of computers to interpret, comprehend, and create human language, specifically in regards to speech.
Currently, the most prevalent type of natural language processing is chatbots. As natural language processing continues to advance, it enables individuals to interact with computers using everyday language and instruct them to complete specific actions.
The potential and risks of natural language processing are on display with the newest developments in AI chatbots.
Advanced knowledge-based systems
According to the Encyclopedia Britannica, an expert system employs artificial intelligence to imitate the actions of individuals or groups with specialized knowledge or expertise. Its purpose is not to substitute for specific roles, but to aid experts in making complex decisions.
An expert system assists with decision-making by utilizing data, extensive knowledge, as well as facts and heuristics. It is a specialized machine designed to solve highly intricate problems. These systems are commonly used in technical fields such as science, mechanics, mathematics, and medicine.
Artificial intelligence is commonly utilized to detect cancer in its initial phases, as illustrated by its application in identifying cancer in its early stages. Additionally, it can also be employed to notify dentists of unfamiliar organic compounds, as demonstrated in its use in alerting dentists to unknown organic molecules.
Automation Technology
Surprisingly, the field of robotics focuses on the development and use of robots. The primary goal of robotics is to incorporate human-like intelligence into machines, with a specific focus on utilizing these machines, also known as robots, to assist with human tasks and employment.
Robotic technology depends on other types of artificial intelligence, however, it guarantees that machines carry out tasks automatically or partially automatically, ultimately benefiting humans.
A clear instance of utilizing robotics can be seen in the development of self-driving cars, also referred to as robotic cars or robo-cars. These advanced vehicles have the ability to operate without human involvement, potentially leading to time and cost savings for humans.
Fuzzy logic
Fuzzy logic is a type of mathematical logic that allows for approximate or uncertain values to be used in computations. It is a form of multi-valued logic that is based on the concept of degrees of truth rather than the traditional true or false values. This allows for more flexibility and accuracy in decision-making processes.
According to an article from Scientific American, fuzzy logic is an AI system that operates based on rules and assists in making decisions. This type of logic relies on experience, knowledge, and data to improve decision-making processes and determine the degree of truth on a scale of 0-1. Instead of providing a simple true or false response, fuzzy logic assigns a number, such as 0.4 or 0.8, in order to capture the nuances of ambiguous concepts.
Fuzzy logic is commonly utilized in entry-level machines, specifically in consumer goods like cameras, air conditioners, and washing machines, to regulate exposure, temperature, and timing.
As demonstrated earlier, AI encompasses various fields and applications. It is highly probable that you have utilized AI-based technology in your daily activities. The demand for AI has significantly increased in recent times, and many organizations, non-profit organizations, and individuals are seeking to participate in this field.
AI has been in development for over 70 years, initially starting with theoretical machines and mouse robots made of wires, but now advancing to technologies such as Google Maps, self-driving cars, and Chat GPT. Therefore, before discussing the future of AI and its ethical implications, it is important to revisit its origins and trace its evolution to the present.
AI’s Origins
Returning to the start, the initial efforts towards AI were carried out by the British logician and computer pioneer, as well as the individual currently featured on the £50 note: Alan Turing. In 1935, Turing conceptualized a theoretical computing device that has infinite memory and the ability to navigate through that memory, examining one symbol at a time, comprehending its findings, and gaining knowledge from what it has comprehended. According to Turing, this machine would then have the capacity to independently generate new symbols.
Instructions programmed in the memory dictate the act of scanning, which in turn modifies and enhances its own program. This theoretical machine, capable of self-improvement and optimization, was named the Turing machine and was proposed 20 years prior to John McCarthy’s coining of the term ‘artificial intelligence’ in 1956.
Before the term AI was coined, there were several functional AI programs in existence. One noteworthy program is Theseus, created by Claude Shannon in 1950. Theseus was a mouse that could be controlled remotely and had the ability to navigate through a maze and recall its path. Other computers, considered as forerunners of AI today, contributed to the development of the theory and laid the foundation for future advancements.
In 1951, Christopher Strachey created an AI program known as Ferranti Mark I, which demonstrated its ability in playing checkers. Shortly after, Dietrich Prinz also designed a machine for chess with similar capabilities. This is why Game AI has remained a significant benchmark for measuring AI progress, as numerous earlier and current developments in AI are evaluated based on their gaming proficiency. The impact of these achievements is evident, as shown in the history of AI beating humans in various games.
The term ‘artificial intelligence’ had not yet been created, until the Dartmouth Conference in 1956, organized by McCarthy and Marvin Minsky. This event is considered by many to mark the beginning of AI, even though it was only attended by a limited number of individuals.
The conference managed to gather some of the leading experts in the field and stimulated thought-provoking debates about the direction of computing. Additionally, it popularized the term ‘artificial intelligence’. Notably, the conference also featured the debut of the Logic Theorist designed by Allen Newell, Cliff Shaw, and Herbert Simon. This program is widely regarded as the earliest instance of AI, as it imitated the problem-solving abilities of a human.
The field of AI experienced a significant growth in the years that followed. Computers became capable of storing larger quantities of data and processing information at a faster rate, all while becoming more affordable. The technology of machine learning also advanced, with early examples like Newell and Simon’s “General Problem Solver” and Joseph Weizenbaum’s “ELIZA” demonstrating progress in tackling problems and understanding spoken language.
Despite making some progress, there were numerous challenges that hindered further advancements. The primary obstacle was the insufficient computational capabilities of computers in the 1970s. The issue lies in the fact that effective AI goes beyond standard computing power, and instead requires exceptional power to handle vast amounts of data and process multiple combinations. In short, computers lacked the strength to demonstrate human-like intelligence.
The field of AI experienced a significant growth in the 1980s when John Hopfield and David Rumelhart introduced deep learning to the public. This breakthrough enabled computers to learn from experience. Additionally, Edward Feigenbaum introduced expert systems that allowed computers to imitate the decision-making abilities of experts.
The groundwork was set by these developments, paving the way for future achievements and even greater progress. Additionally, there was a similar surge in computer power, increasing its capacity and enabling the implementation of intellectual concepts behind AI.
During the 1990s and 2000s, Artificial Intelligence (AI) experienced a period of growth and success. Despite a lack of financial support from the government, many of the initial objectives of AI were successfully accomplished. An example of this is the renowned Game AI goal, which aimed to develop a computer that could defeat a grandmaster chess player.
In 1997, IBM’s Deep Blue made headlines by defeating the then world chess champion Gary Kasparov. This highly publicized event marked a significant moment in AI history. Kasparov, the reigning grandmaster, compared his defeat to losing to a highly advanced programmable alarm clock. Despite the defeat, he did not feel any better knowing that he was beaten by a machine worth $10 million.
In the same year, Windows introduced speech recognition software. Shortly after, Kismet, a robot created by Cynthia Breazeal, was able to detect and comprehend human emotion through analyzing facial expressions.
Starting from 2010, there was a subsequent surge, possibly the most significant one, that has persisted until now. This was mainly due to the vast amount of data that became easily accessible and the improved processors that could speed up the learning of more intricate algorithms. In essence, computers gained more strength, data became more reachable and abundant, resulting in significant advancements.
The recent increase in power has resulted in significant advancements, evidenced by the success of Game AI as seen in the various achievements in the field. For instance, in 2011, IBM’s Watson outperformed two Jeopardy champions. Additionally, in 2012, Google X successfully identified cats in a video, which utilized over 16,000 processors and revolutionized the field of deep learning.
Back in 2013, DeepMind, a company under DeepMind Technologies, achieved victory in numerous Atari games with just one model. Three years later, another DeepMind creation, Google’s Alpha Go, surpassed both the European and World Champion in the Asian game of Go.
The year 2017 saw the emergence of Google’s AlphaZero as a skilled player in Chess, Go, and Shogi. Two years later, in 2019, Google’s AlphaStar rose to the top 0.2% of players in Starcraft, a challenging real-time strategy game, marking the first time an AI had ever claimed the top spot in e-sport rankings.
The aforementioned examples are solely based on the concept of Game AI. However, the impressive capabilities that facilitated these accomplishments have also resulted in significant advancements in real-world implementations, several of which have been previously discussed.
The 2020s have witnessed a continued increase in AI development and a corresponding surge in public interest. This brings us to the current year, 2023, where AI has become a prominent topic in the tech industry and its utilization has significantly expanded.
The present situation of Artificial Intelligence (AI)
The use of AI is becoming more prevalent in daily tasks. These systems are responsible for evaluating loan applications, determining eligibility for government assistance, and deciding whether to advance a candidate to a second-round interview. Additionally, AI is utilized for surveillance and monitoring dietary requirements in healthcare applications.
Recommender systems powered by AI are responsible for curating your social media feed, displaying advertisements on your browser, suggesting videos on YouTube, showcasing products on online stores, and recommending the next show you might enjoy. AI is constantly at work, impacting various aspects of our daily routines at all times. It has become an integral part of modern life.
Not only is AI being utilized for everyday tasks, but it is also making significant advancements in various fields. AI is now being used to tackle some of the most challenging problems in mathematics and science, while also playing a crucial role in finance. Large companies are relying on AI-powered algorithms to guide their investment decisions. Additionally, the military is using AI in various ways such as monitoring threats, operating drones, identifying targets, and deploying autonomous vehicles.
The use of AI is vital in the field of cyber-security, as it examines patterns of cyber-attacks and devises protective strategies against them. Additionally, AI plays a significant role in the battle against climate change by enhancing modeling techniques, developing more efficient monitoring programs, and analyzing extensive and intricate data based on environmental factors. In summary, AI is rapidly expanding and making significant contributions to human progress in multiple sectors.
The rapid development of AI is not only introducing new philosophical and ethical concerns, but these concerns are emerging at a pace that does not allow for thorough discussion. The future of AI will ultimately be shaped by its ethical considerations and regulatory measures. However, considering the current state of affairs, we can anticipate certain trends to arise in the future.
The potential of Artificial Intelligence
The field of AI has come a long way since the introduction of Theseus, the robot mouse, as mentioned in the article on Technology Review (2018). Today, we have more advanced systems like DALL-E (OpenAI, n.d.) and PaLM (Palmai, n.d.) that are capable of generating high-quality images and processing large volumes of complex language, including interpretation and translation.
Over the last 60 years, the training of artificial intelligence has followed the trend of Moore’s Law, doubling approximately every 20 months. However, since around 2010, there has been an increase in this growth rate, with AI now doubling every six months. The progress of AI is set to continue at an even faster pace in the future – and it is already advancing at an alarming rate.
The future of AI can be predicted by analyzing long-term trends. One of the most widely discussed trends, according to Our World in Data, is the one presented by AI researcher Ajeya Cotra. Cotra’s goal was to determine when AI systems would reach the same level of intelligence as the human brain. Based on extensive research and data, the latest estimation suggests that there is a 50% chance of AI achieving human-level intelligence by 2040. This could have a significant impact on society.
It should be noted that Cotra’s prediction may not be entirely accurate. Her prediction is just one of many possibilities and some models suggest a faster growth while others suggest a slower pace. However, all indications point towards the continuous growth of AI. What we must acknowledge is that in the future, AI will undoubtedly play a significant role, not only in technology but also in shaping humanity, as stated in the European Parliament’s article on the threats and opportunities of artificial intelligence.
This is precisely why it is crucial to discuss the ethics and regulation surrounding artificial intelligence, as highlighted in the article “Why It’s Time to Change the Debate Around AI Ethics”. It is necessary to comprehend the ethical challenges arising from technology and determine the appropriate approach to addressing them. Without delay, let us now delve into some of the key ethical concerns surrounding AI and explore their core arguments.
AI and its ethical implications
The progression of artificial intelligence (AI) has sparked a rise in philosophical discourse surrounding its existence. While there are evident advantages of AI in multiple industries, it is not devoid of potential hazards. As AI continues to expand and take on a larger role in the economy, the associated risks are becoming more prominent. In the following sections, we examine several key ethical dilemmas concerning AI and delve into the overarching debates surrounding them.
Disparity
The implementation of AI technology raises ethical concerns regarding its impact on economics, particularly in regards to economic inequality. This includes questioning the role of AI in either reducing or exacerbating existing inequalities.
Governments often implement economic policies in an effort to decrease the unequal distribution of wealth (http://encyclopedia.uia.org/en/problem/inequitable-distribution-wealth). However, the gap between the wealthy and the poor has been growing (https://www.theguardian.com/global-development/2020/jan/22/wealth-gap-widening-for-more-than-70-per-cent-of-global-population-researchers-find) in many countries around the world in recent decades. One common criticism of artificial intelligence is that it allows companies to significantly reduce their reliance on human labor (https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces), potentially leading to higher unemployment rates and exacerbating the wealth gap, which can result in increased poverty levels.
According to data from the World Economic Forum, the three largest companies in Silicon Valley generated similar profits to the three largest companies in Detroit. However, Silicon Valley companies employed ten times less workers.
The growing use of AI in decision-making roles has raised ethical concerns, especially regarding the potential for individuals in charge of AI-driven companies to significantly profit compared to others. This is a major concern, as these companies are expected to play a bigger role in the economy in the future, according to findings from AI research.
There are differing opinions on the matter, arising from both economic and ethical perspectives. According to the economic viewpoint, the potential for redistribution is influenced by the ever-changing future. This is a decision based on economics, as stated by renowned physicist, Stephen Hawking, who believes that automation and artificial intelligence will greatly impact jobs and that either everyone can have a life of leisure if the wealth created by machines is shared, or most people will suffer if the owners of these machines oppose redistribution of wealth.
The economy’s stability is reliant on individuals making specific economic choices and guaranteeing the equal distribution of benefits from AI and automation across all sectors of the economy. The issue of inequality caused by AI is a result of political and economic decisions that are not influenced by technology. The ethical concerns surrounding this matter pertain to the economy, not the technology itself.
The concept of “creative destruction”, which is attributed to economist Joseph Schumpeter and inspired by Karl Marx’s work, offers an economic counter-argument to rising inequality. This idea explains the process of industrial transformation and adaptation that brings about significant changes to existing economic systems, dismantling the old structure and building a new one. This concept is also known as “industrial mutation”.
According to Schumpeter, who was influenced by Marx and sociologist Werner Sombart, the concept of creative destruction would ultimately weaken capitalism. However, in mainstream economics, this term is often used to refer to a beneficial transformation that generates new resources and allows for reinvestment in more efficient methods.
One perspective suggests that creative destruction causes obsolete and outmoded structures and technologies to be replaced with more efficient ones that will better serve society. This viewpoint argues that while AI may lead to job losses and unemployment, it will also generate new job opportunities and decrease overall unemployment levels.
The basis of ethical behavior in this situation revolves around making economically responsible choices that align with societal values. It is crucial to guarantee that these decisions promote fairness and that all individuals, regardless of their status, have access to both the advantages and potential drawbacks of AI.
Preconceived notions and discrimination
The use of machines to interpret data brings up the issue of data ethics, specifically regarding potential biases (https://hbr.org/2019/10/what-do-we-do-about-the-biases-in-ai). While AI can handle large amounts of information beyond human capabilities, it is not inherently impartial. Among the front-runners in AI technology is Google, whose AI-driven services have faced justified backlash.
According to the New York Times, Google’s AI-powered Photos feature, which was designed to recognize individuals, items, and environments, has been found to display internal biases, leading to a lack of racial sensitivity. The program’s ability to forecast potential criminals was found to exhibit a significant and concerning prejudice against black individuals.
According to experts, human beings, both consciously and unconsciously, are responsible for the creation of artificial intelligence (AI). This bias is then acquired by AI and can even be amplified in certain cases. Moreover, additional biases may arise from insufficient or biased data sets, as well as reliance on inaccurate or flawed information that perpetuates past inequalities.
Those involved in AI must address the issue by maintaining thorough and inclusive data sets and implementing additional precautionary measures. As stated, regulations should strive to reduce data bias in AI, as mentioned in the publication on algorithmic bias by the European Union Agency for Fundamental Rights.
Currently, the responsibility mainly lies with the organizations and individuals who are handling the data.
Legal concerns
The legal system presents another moral dilemma when it comes to the topic of liability. The issue becomes more complex and unclear when artificial intelligence is involved. A prime example is the liability for robots that replace human soldiers, potentially causing harm to innocent individuals. This begs the question, how do we determine who is at fault?
An example often cited is driverless cars, which have already caused a multitude of legal issues according to numerous sources. However, their implementation has been limited. The question of responsibility remains largely unsolved – in the event of an accident, should the driver, who was not driving, be held liable, or is it the manufacturer’s responsibility? This continues to be a topic of debate, with strong opposing views.
When it comes to punishment, the issue becomes more complicated. The use of AI can make it difficult to determine who is at fault, providing opportunities for plausible deniability and making it challenging to hold individuals or organizations responsible. While mistakes in AI implementation and integration can result in disastrous consequences and loss of life, it is challenging to impose punishment due to the difficulty of assigning culpability.
The topic of AI rights has brought up another legal concern, as machines are becoming more and more similar to humans and possess many human-like qualities. This raises ethical questions about how we should treat robots and machines, as discussed in this article. Should they be treated similarly to animals with similar intelligence? Is it important to recognize their potential for suffering? These are important considerations for the legal system to address.
The topic of AI rights is a controversial one, as seen in the ongoing debates on Bytes. While some argue that AI should have comparable rights to humans, the focus should instead be on establishing appropriate rights that are backed by legal and moral standards. The implementation of these rights will largely depend on the regulations put in place at both national and international levels. However, it is evident that the legal system will have to devise ethical solutions to minimize potential risks and maximize the advantages offered by AI.
The phenomenon of Climate Change
The issue of environmental destruction has been growing in importance, particularly in the tech industry. In the past, we have discussed the conflicting relationship between technology and the environment, demonstrating that technology is both a contributor to and a potential solution for the issue. Within this dilemma, AI fits perfectly.
As stated by the Council on Foreign Relations, the training of a single AI system can result in a release of over 250,000 pounds of carbon dioxide. The utilization of AI in various industries emits a comparable amount of carbon dioxide as the aviation industry.
In 2019, the University of Massachusetts conducted a study which revealed that creating AI models for natural language processing results in an energy usage that is equivalent to emitting 280 tons of carbon dioxide. The training process for this system is comparable to taking 125 round trips between New York and Beijing. This is a significant concern, especially when considering the findings of a recent study by OpenAI, which states that the energy consumption for running large AI models increases by double every three and a half months. This could potentially lead to major issues in the future.
AI has a significant impact on addressing climate change. For example, AI-powered self-driving cars have the potential to decrease emissions by 50% by 2050, as stated by a report from the University of California, Davis. In addition, the utilization of AI in agriculture can lead to higher crop yields, reducing waste and promoting the growth of local economies. Through the implementation of AI-driven monitoring systems, there is a greater level of oversight and accountability for governments and other relevant organizations, ensuring that they are adhering to environmental regulations.
According to a recent study, utilizing artificial intelligence to enhance the effectiveness of electric grids has the potential to greatly decrease overall emissions.
Moreover, in addition to the aforementioned benefits, artificial intelligence (AI) has the potential to address the repercussions of climate change. Through the use of AI-powered data analysis, it can detect and forecast severe weather patterns, ultimately aiding in the development of more effective responses.
The evidence presented above emphasizes the crucial role of ethical decision-making in the success of AI. AI poses various challenges in terms of economy, politics, society, law, and the environment, and the fate of our civilization hinges on making sound choices within this context. These decisions will require collaboration between national and international governments in order to ensure that AI benefits all of society.
AI Regulation
The contemplation of ethics prompts us to consider the need for regulation. Both national and international regulatory systems are crucial for maximizing the advantages and minimizing the risks of AI. It is imperative that AI remains focused on the needs of humans, rather than the other way around. The legal frameworks surrounding AI must always operate within the boundaries of the law, ensuring consistency, transparency, and accountability. This is being implemented by judicial systems worldwide, as seen in their use of AI systems in decisions involving sentence length and likelihood of reoffending.
The regulation of AI is a challenging task, largely due to its fast-paced developments and differing perspectives on various ethical concerns. Furthermore, decision-makers globally are lacking in knowledge about the potential and dangers of AI, as well as the ethical dilemmas it presents. As a result, the lack of regulation is a result of delays, disagreements, and uncertainty.
According to The Conversation, although there has been some advancement, Australia has taken steps towards regulating AI by establishing the National AI Centre, as referenced in The Conversation and Jisc’s website. Within this initiative is the Responsible AI Network, which strives to promote ethical practices and set a precedent for regulations and standards that other nations may adopt. However, there is currently no specific legislation in place for governing AI and algorithmic decision-making, as the government has chosen a less stringent approach.
The United States has implemented a comparable approach. Legislators have not shown much interest in overseeing AI and, like numerous other countries worldwide, they have endeavored to control AI by utilizing current laws and regulations. The U.S. Chamber of Commerce has requested for the regulation of AI to safeguard against limiting growth or jeopardizing national security, yet no measures have been implemented.
The UK government takes pride in its pro-innovation stance towards regulating AI, as stated in their Policy Paper (https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach). This document presents six principles for cross-sectoral AI governance and affirms that, like many other governments, the UK has no intention of implementing new laws to govern AI. However, these principles – such as ‘Ensuring the safe use of AI’ and ‘Integrating fairness into AI’ – offer little contribution to the discussion and do not add any new legal framework. While they may assist with self-regulation, they do not serve any other purpose.
The EU has been hailed for having the most advanced AI legislation, which can be found on the website https://artificialintelligenceact.eu/. However, the implementation of their Artificial Intelligence Act, available at https://artificialintelligenceact.eu/the-act/, is still pending. The Act proposes a classification system for AI, where ‘unacceptable risk’ is assigned to systems that may require bans, ‘high risk’ is given to tools that require specific legal oversight, and ‘no risk’ is designated to other applications that may not be heavily regulated. Some critics argue that there are loopholes and exceptions in the legislation, but overall, it can be seen as a progressive step. Unlike most other countries, the AI Act provides a clear regulatory stance on AI.
The Recommendation on the Ethics of Artificial Intelligence was adopted by 193 United Nations Members States and is worth mentioning. This recommendation serves as the first global standard-setting instrument in regards to this topic and is intended to safeguard human rights and dignity. It also provides an ethical framework for countries to follow and promotes the establishment of effective governance for AI to uphold the rule of law in the digital realm. This instrument is expected to encourage other countries to prioritize the respect for the rule of law when it comes to AI and its usage.
The framework is considered to be progressive and has the potential to be used in future regulations. However, it is worth noting that AI is currently not subject to much regulation. The future of AI will be determined by its advancements, expansion, and the ethical implementation of emerging technology in practical contexts.
The success of AI in terms of managing its risks and maximizing its benefits will heavily rely on both ethical reasoning and governmental regulations. Collaboration will also play a significant role as the challenges presented by AI often transcend borders and will require universal standards that go beyond national politics.
Concluding Thoughts on Artificial Intelligence
The inevitability of AI growth has been widely acknowledged (source: https://www.forbes.com/sites/danielshapiro1/2019/09/10/artificial-intelligence-its-complicated-and-unsettling-but-inevitable/). The main concern moving forward is the path of this growth. The intended purpose of AI should be to enhance our daily lives, elevate our quality of life, and provide assistance to those in need.
It is important to consider the ethical challenges mentioned above, implement effective regulations to maximize advantages and minimize risks, collaborate on a global level, and guarantee that AI benefits our collective progress.
The achievements or shortcomings of artificial intelligence (AI) – the technology that boasts human-like intelligence – continue to rely on decisions made by humans. It is crucial that we make appropriate decisions in this regard.
