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Societal Challenges of Data, Artificial Intelligence (AI), and Automation We live in a digital era in which the influence of technology can be seen in almost every aspect of our lives. Advancements in technology have created many opportunities in almost every facet of human life. Technology has also brought efficiency and revolutionized how human beings do many things, including commerce, transport, communication, and learning. Three of the most significant aspects of the digital era that are changing human lives are data, artificial intelligence (AI), and automation. While the benefits of these three are countless, there are enormous societal challenges associated with data, AI, and automation. Data, AI, and automation are highly connected, and their prevalence in today's society is increasingly visible. Data is a collection of raw statistics and images such as observations, numbers, and descriptions (Gregory et al., 2021). Today, data has become the biggest buzzword, changing people's perspectives towards almost everything and helping individuals and organizations make critical decisions. Data can be collected through various ways, including research, analysis, observations, and measurements. In today’s era of technology, data is available in large and complex forms, often difficult for traditional data-processing applications to handle. This has resulted in a new terminology known as big data. Data provides the raw materials and foundation for AI and automation. Artificial intelligence (AI) and automation are distinct yet connected things. AI is a term referring to a set of technologies that enable the simulation of human intelligence through machines, particularly computers. Examples of AI applications include natural language processing, machine vision, and speech recognition (Nagarhalli et al., 2021). AI works by taking in large amounts of data, analyzing it for correlations and patterns, and using the emerging patterns to make future predictions. Automation, on the other hand, automation is the application
of technology to perform tasks with minimal human intervention. The significant difference between AI and automation is that the former seeks to simulate human thinking. However, automation can use AI. While data, AI, and automation have been game-changers in how we live, work, and play, they have also presented significant risks to society. Privacy Concerns in the Age of Big Data In modern digital data landscapes, individuals and organizations are increasingly living in fear that their privacy is in a state of constant decline. As individuals and organizations collect and analyze enormous amounts of data, they position themselves as attractive targets for cybercriminals. In the recent past, many companies, such as LinkedIn, eBay, and Marriott, have fallen victim to cybercriminals, leading to privacy concerns (Aswathy and Tyagi, 2022). Whether it is an insider threat or a sophisticated attack on databases holding critical data, the breaches can lead to the misuse of personal information. Social media sites, governments, healthcare, and insurance providers are some of the institutions with significant access to our data. These institutions are bound by data protection laws to ensure data is protected access. However, that does not entirely eliminate the potential of access by hackers or internal misuse. This is why it is critical for companies to have robust security protocols, incident response plans, and regular security audits. Collecting, storing, and utilizing vast amounts of personal information needs significant ethical considerations. The presence of inadequate consent requirements for data or information that is available publicly raises autonomy concerns because generators of this information often do not understand to what extent it can be misused without their consent. For example, big data presents significant challenges to observing anonymity in research as it is threatened by hackers and malicious persons.
trend of wages to as low as 70 percent since the early 1980s (Nah et al., 2023). These statistics demonstrate the many challenges created by AI and automation in the workforce. It is also critical to acknowledge that some industries and job roles are more prone to losses than others. For example, workers in routine and repetitive tasks are more likely to lose their jobs to automation than most. The challenges created by AI and automation have negative economic implications. Increased unemployment due to job displacements can lead to reduced consumer spending, which leads to slow economic growth. The level of consumer spending is what drives demand and influences production. Therefore, reduced consumer demand due to declining consumer purchasing power caused by job losses and job insecurity can drive down the economy. Bias and Fairness in AI Algorithms As the world continues to adopt AI and exploit the opportunities it presents, questions emerge about the extent to which human biases have infiltrated AI systems. In the same manner that systemic and racial biases have affected society for centuries and have proven difficult to eliminate, AI bias can be a real threat to the same challenges it was designed to address. AI bias refers to AI systems that generate biased outcomes that amplify human biases in our societies, including social and historical inequalities. Initial training data, the algorithms, and forecasts produced by the algorithm are examples of how bias in AI manifests. When bias is left to dominate AI systems, it hinders people's economic participation and reduces the potential of AI. Furthermore, AI systems that generate biased predictions and promote mistrust among people, particularly the most vulnerable, including people with disabilities, women, people of Color, and LGBTQ, cannot benefit businesses. Rather than making society better and more inclusive, AI bias brings even more division.
There are many ways in which AI bias presents itself, and there are many real examples. The healthcare industry is one of the biggest consumers of AI systems. It is also one of the most vulnerable, where misuse of AI potential can be catastrophic. According to IBM, studies have found computer-aided diagnosis (CAD) to produce lower accuracy results for African-American patients than their white counterparts (Manyika et al., 2022). The impact of AI bias has also been witnessed in many other sectors. Amazon suspended the application of a hiring algorithm after it emerged that it favored job-seekers based on some words like "captured," which commonly appeared on men's resumes. Similarly, gender role boas can be reinforced by AI bias in search engine ad algorithms. Research at Carnegie Mellon University showed that the online advertising system by Google displayed higher profile and higher paying roles to men more often than men. The examples of AI bias and the societal challenges it presents are many and represent the threats it presents even as we continue to embrace AI in our daily lives (Manyika et al., 2022). Bias is all human responsibility. It causes harm to individuals and groups discriminated and hurts everyone else by reducing the ability of these people to fully participate in economic growth. The potential of AI in reshaping society and promoting business growth is also reduced by AI biases because it fosters mistrust and produces distorted outcomes. It is upon business and corporate leaders to ensure that the AI systems used in their institutions enhance human capacity without propagating discrimination or prejudice. Loss of Human Agency and Autonomy In discussions around automation and AI systems, human autonomy is one of the most peculiar themes. A comprehensive analysis of algorithmic systems' potential requires a deeper understanding of them in the context of autonomy, agency, and respect for humans (Rubel, Castro, and Pham, 2020). However, it is also critical to note that ethics in AI is not solely an
groups to access employment opportunities, healthcare, and other necessities of life. Similarly, routine outsourcing of some tasks might lead to a loss of human competence (Prunkl, 2023). Health and Psychological Consequences Over-reliance on automated processes and AI-powered systems has been associated with several adverse health and psychological outcomes. AI and automation are not only replacing human jobs but also affecting human health and psychological well-being. The fear of job loss due to automation can cause anxiety and stress among employees, leading to mental health challenges and reduced productivity. Furthermore, automation and AI can automate functions that require human indulgence, further isolating employees and impacting their mental health negatively (Schwalbe and Wahl, 2020). Moreover, we must also acknowledge that big data may contain biases, which can lead to unfair treatment, causing anxiety and stress. Overuse of technology is addictive. Individuals can develop addictive behaviors from overuse of technological tools and devices. Individuals may be compelled to constantly check their devices or apps powered by AI, which can interfere with many aspects of human lives, such as social interactions and work-life balance. Artificial intelligence and automation, powered by data, are changing the world in diverse ways. The impact will continue growing in the future. Automation is making tasks previously occupied by people, such as data entry, driving, and customer service. The potential for these changes is enormous, including reduced costs and improved safety. Similarly, it has attracted widespread application in healthcare to enhance treatment and diagnosis and develop new therapies and drugs. The benefits are also experienced in business, education, and many other
facets of life. However, amidst these potential benefits lie many threats and challenges to the well-being of our society. Throughout the paper, we have explored the societal challenges associated with data, AI, and artificial intelligence. We have explored the privacy concerns in this digital age, the potential for job displacements and inequalities, and bias in algorithms. As explored throughout this paper, AI and automation threaten human jobs and can significantly displace specific job roles. The challenges associated with AI and automation reliance also have the potential to cause mental health challenges. However, since the future is digital and trends indicate that the world will continue relying AI systems and automated systems powered by data, the critical thing is for the world to put in place measures to reduce the negative societal challenges.
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