Every major technological innovation brings social potential or damage. The data processing and analysis capability of artificial intelligence (AI) can help alleviate some of the most pressing problems in the world, from the development of diagnosis and treatment of diseases to transportation, urban development, and climate change mitigation. However, the same skills can also help monitor the most vulnerable to an unprecedented degree, identify and discriminate against individuals, and revolutionize the economy in ways no job retraining program can keep up with. Despite the critical steps in AI development, the so-called “artificial intelligence revolution” is only a decade old.
Below are some ways in which AI can help or harm societies. Note that even “helpful” AI uses can have negative consequences. Many healthcare AI applications, for example, pose severe threats to protecting privacy and risk to underserved communities and focus on data ownership in large companies.
At the same time, using AI to mitigate harm may not solve underlying problems and should not be considered a cure for social issues. For example, although AI may alleviate the need for underserved medical practitioners, it does not provide the resources or incentives that professionals need to relocate. Similarly, there were some cases of a use classified as “harmful” due to good intentions but which have caused significant harm.
Benefits of AI
Improved Access to Healthcare and Disease Outbreak Prediction
Significant progress has been made in diagnosing and preventing diseases through AI. AI is also used in regions lacking access to healthcare, providing remote diagnostics and even robotic surgery assistance. During the COVID-19 pandemic, AI models predicted outbreaks and helped manage resources efficiently. For example, BlueDot’s AI predicted the outbreak of COVID-19 days before it was officially recognized by health authorities, demonstrating AI’s potential in early detection and intervention.
Making Life Easier for the Visually Impaired
Image recognition tools help people with visual impairments better navigate the internet and the real world. Applications like Seeing AI by Microsoft describe the environment, read text, and recognize faces in real time, greatly enhancing independence and quality of life. These tools bridge significant gaps, offering a level of autonomy previously unattainable.
Optimization of Agriculture and Adaptation of Farmers to Change
AI combines global satellite imagery with weather and agricultural data to support farmers in improving crop production, diagnosing and treating plant diseases, and adapting to changing environments. This approach, known as precision farming, can increase agricultural productivity to feed more people worldwide. For example, John Deere’s AI-powered systems provide farmers with precise planting and harvesting guidance, boosting yield and reducing resource usage.
Climate Change Mitigation, Natural Disaster Prediction, and Wildlife Conservation
With the effects of climate change globally, machine learning helps scientists make climate models more accurate. AI is already used to classify climate models, forecast extreme weather events, and respond to natural disasters. AI also aids in wildlife conservation by identifying poaching activities and monitoring endangered species. For instance, the EarthRanger system uses AI to process data from various sensors to help rangers protect wildlife effectively.
Increased Efficiency and Accessibility of Government Services
While new technologies are often slow, governments worldwide use AI to make public services more efficient and accessible at local and national levels, emphasizing “smart cities” development. AI helps allocate government resources and optimize budgets, streamlining administrative processes. Estonia, for example, has integrated AI into its public services to handle tasks like tax returns and digital IDs, making the government more efficient and citizen-friendly.
Enhanced Education and Personalized Learning
AI has the potential to transform education by providing personalized learning experiences tailored to individual students’ needs. Adaptive learning platforms like DreamBox and Knewton use AI algorithms to analyze students’ learning patterns and provide customized content and feedback. For example, students struggling with algebra can receive additional practice problems and explanations in real time, helping them master the subject more effectively.
Revolutionizing Transportation
AI is at the forefront of transforming transportation through autonomous vehicles and smart traffic management systems. Companies like Tesla and Waymo are developing self-driving cars that promise to reduce traffic accidents caused by human error. Moreover, AI-powered traffic management systems, such as those implemented in cities like Singapore, optimize traffic flow, reduce congestion, and improve public transportation efficiency by analyzing real-time data from various sources.
Advancements in Scientific Research
AI accelerates scientific discovery by analyzing vast datasets and identifying patterns humans might miss. For instance, Google’s DeepMind used its AI system, AlphaFold, to rapidly predict protein structures, a breakthrough that could revolutionize drug discovery and understanding of diseases. This demonstrates AI’s potential to drive significant advancements in various scientific fields.
Improving Customer Service
AI-powered chatbots and virtual assistants enhance customer service by providing instant support and resolving issues efficiently. Companies like Amazon and Bank of America use AI-driven chatbots to handle routine customer inquiries, freeing human agents to focus on more complex issues. This not only improves customer satisfaction but also reduces operational costs for businesses.
Risks of AI
Continuing Bias in Criminal Justice
In the criminal justice system, many documented AI cases are wrong. The use of AI often occurs in this context in two different areas: risk assessment—to assess whether an accused is likely to re-offend to recommend sentence and bail—and the so-called “predictive policing”—using insights from various data points to predict when and where a crime will take place and direct law enforcement action. These efforts, while well-intentioned, often exacerbate the prejudicial nature of these systems. For example, COMPAS, a widely used risk assessment tool in the US, has been found to exhibit racial bias, leading to disproportionately harsher outcomes for minorities.
Facilitating Mass Surveillance
As AI can process and analyze multiple data streams in real-time, mass surveillance is already being used worldwide. AI in facial recognition software is the most pervasive and dangerous example. While technology remains imperfect, governments are looking at facial recognition as a tool to monitor citizens, facilitate profiling of groups, and even identify and locate individuals. China’s extensive use of facial recognition for surveillance and social credit systems exemplifies the potential for abuse and the erosion of privacy.
Discriminatory Profiling
Facial recognition software is used not only to monitor and identify but also to target and discriminate. Studies have shown that facial recognition systems often misidentify people of color at higher rates, leading to wrongful arrests and surveillance. This discriminatory profiling can have profound social and legal implications, reinforcing systemic biases.
Spread of Disinformation
AI can develop and disseminate targeted propaganda, compounded by social media algorithms that promote content. Machine learning allows social media companies to develop targeted advertising user profiles, creating echo chambers and spreading disinformation. Additionally, bots disguised as real users spread material in targeted social media circles by sharing links to false sources and interacting with users as chatbots through natural language processing. The rise of deepfake technology, which creates realistic but fake audio and video recordings, poses a significant threat to truth and can be used maliciously to manipulate public opinion and incite conflict.
Perpetuating Job-Market Bias
Recruitment procedures have long been full of prejudice and discrimination. A whole industry emerged that uses AI to remove human prejudice. However, many products risk perpetuating their own biases. The main reason is the common use of historical data from past “successful” employees to train the machine learning model, naturally reproducing the bias in previous recruitment. For instance, Amazon’s AI recruiting tool was found to be biased against women because it was trained on resumes submitted over a decade, which were predominantly from men.
Financial Discrimination Against the Marginalized
Algorithms have long been used to create credit scores and inform credit screening. With the rise of big data, systems now incorporate and analyze non-financial data points with machine learning methods to determine creditworthiness, including how people live, browse the internet, and shop. These systems’ outputs, known as e-scores, are mostly unregulated and can be discriminatory, causing harmful feedback loops. Data scientist Cathy O’Neil has highlighted how these scores often perpetuate economic disparities, making it harder for marginalized communities to access credit and other financial services.
Ethical Concerns and Autonomous Weapons
The development of autonomous weapons systems powered by AI raises significant ethical concerns. These weapons can select and engage targets without human intervention, potentially leading to unintended and catastrophic consequences. The use of AI in military applications, such as drones, has sparked a global debate on the need for regulations to prevent the misuse of AI in warfare. The Campaign to Stop Killer Robots is one example of an initiative advocating a ban on fully autonomous weapons.
Threats to Democracy and Political Stability
AI can be used to influence political outcomes and undermine democratic processes. For example, during the 2016 US presidential election, AI-driven bots and algorithms were used to spread misinformation and manipulate voter opinions on social media platforms. The Cambridge Analytica scandal highlighted how personal data and AI can be exploited to target and influence voters, raising concerns about the integrity of democratic elections.
Energy Consumption and Environmental Impact
The training and deployment of AI models, particularly deep learning algorithms, require significant computational power, leading to high energy consumption. Data centers supporting AI operations contribute to carbon emissions and environmental degradation. For instance, a study by the University of Massachusetts Amherst found that training a single AI model can emit as much carbon as five cars over their lifetimes, emphasizing the need for sustainable AI practices.
Privacy Invasion and Data Security
AI’s ability to process and analyze vast amounts of personal data raises significant privacy and data security concerns. Companies like Facebook and Google collect extensive user data to improve their services and target advertisements, making them attractive targets for cyberattacks. Data breaches, such as the Equifax incident in 2017 compromise sensitive information and highlight the vulnerabilities associated with storing and processing large datasets.
Dependence on AI and Loss of Human Skills
As AI systems become more integrated into daily life and various industries, there is a risk of over-reliance on technology, leading to the erosion of human skills. For instance, the widespread use of GPS navigation systems has been linked to a decline in people’s ability to navigate without assistance. In professional settings, over-dependence on AI tools for decision-making could result in workers losing critical thinking and problem-solving skills.
Conclusion
AI holds immense potential to transform various aspects of society, offering numerous benefits such as improved healthcare, personalized education, and enhanced transportation. However, it also presents significant risks, including ethical concerns, threats to privacy and democracy, and environmental impact. As AI technology evolves, it is crucial to balance innovation with ethical considerations, ensuring that its development and deployment lead to positive outcomes for all. Addressing these challenges requires collaboration between policymakers, industry leaders, and researchers to create robust frameworks and regulations that guide the responsible use of AI.
