The Human Algorithm: Automating a More Ethical Future

The Human Algorithm: Automating a More Ethical Future Over the past decade, automation has become one of the most defining forces reshaping how societies function and how economies grow. Artificial Intelligence (AI) and Robotic Process Automation (RPA) are no longer abstract technologies discussed in research papers or confined to film scripts, they have quietly become part of everyday life. From online customer service systems to energy management in global corporations, automation now influences the way people work, communicate, and even make decisions. The potential benefits of these technologies are enormous, allowing people to focus on more creative or complex tasks. However, this rapid shift toward an automated world also raises important questions about its wider consequences. AI consumes large amounts of energy, contributes to electronic waste, and relies on resource-intensive hardware. It changes the structure of the job market, creating opportunities for some while displacing others, while also impacting the way businesses face accountability for their decisions. Because of this, discussions about automation can no longer focus only on efficiency or innovation. Rather, they should also address sustainability and responsibility. Companies and policymakers are turning to Environmental, Social, and Governance (ESG) principles and the United Nations Sustainable Development Goals (SDGs) to guide this transformation. These frameworks provide a foundation for understanding how automation can serve not only economic progress, but also environmental protection, fair labour practices, and ethical governance. This essay explores automation’s environmental and social consequences, while highlighting the growing role of AI and RPA in shaping both challenges and solutions. It argues that automation, if developed and managed responsibly, can become a tool for achieving long-term sustainability. The question is not whether automation will define the future, but whether humanity can guide its development toward outcomes that are equitable, ethical, and sustainable for all. Environmental Impacts of AI as Automation With environmental concerns growing worldwide, the impact of artificial intelligence (AI) on the planet is now impossible to ignore. AI systems are often celebrated for their efficiency, creativity, and problem-solving power, but running these massive models comes with real environmental costs. Behind every AI-generated image, chatbot reply, or recommendation algorithm is an energy-hungry infrastructure that keeps it all running. Recent studies, including one from MIT News, show that data-centre energy demand has exploded in just the past few years, primarily because of AI’s growth. Just in North America alone, total capacity jumped from 2,688 MW in 2022 to over 5,341 MW by 2023. Globally, data centres consumed around 460 terawatt-hours (TWh) in 2022, roughly the same as France’s total consumption. If this trend continues, that number could rise to 1,050 TWh by 2026, making data centres one of the world’s top five power consumers. Since most electricity still comes from fossil fuels, this kind of digital expansion is clearly unsustainable without a major shift toward renewable energy. Even a single AI model can have a shocking footprint. Training OpenAI’s GPT-3, for example, required about 1,287 MWh of electricity and emitted 552 metric tons of CO₂, enough to drive a car over a million miles, purely to train the model. Every single query to ChatGPT, or to other large models, consumes significantly more power than a normal Google search, about five times more. Multiply that by millions of users and billions of queries, and the scale of energy use becomes enormous. But it’s not only about electricity. AI systems also demand huge amounts of water to keep their processors cool. According to MIT’s analysis, about two litres of water are used per kilowatt-hour of data-centre energy. In water-scarce regions, this can put major pressure on local supplies and ecosystems. On top of that, manufacturing AI hardware like GPUs depends on rare minerals such as lithium, cobalt, and tungsten, which are mined and processed in energy-intensive ways that also create toxic waste. And then there’s the growing mountain of electronic waste. As AI hardware evolves so quickly, data centres frequently replace entire racks of GPUs and servers to keep up. One IEEE Spectrum report estimates that AI-related hardware upgrades could generate 2.5 million tons of e-waste each year by 2030. Considering the world produced around 62 million tons in total in 2022, AI could soon account for a sizable chunk of it. This waste contains hazardous materials like lead and mercury, which can pollute soil and water when dumped in landfills, especially in countries without strong recycling systems. Where RPA Fits In This is where RPA (Robotic Process Automation) enters the picture. While AI often grabs the spotlight, RPA offers a quieter, more sustainable kind of automation. Instead of training massive neural networks, RPA relies on smaller, rule-based systems that automate repetitive office tasks, things like invoice processing, payroll, compliance checks, or data entry. These systems don’t require vast computing power, which means they consume far less energy and generate less electronic waste. RPA can also support sustainability goals directly. For example, companies can use RPA bots to automate the collection and reporting of environmental data by tracking energy use, emissions, or supply chain sustainability metrics. This helps organisations meet the reporting requirements of ESG (Environmental, Social, and Governance) standards, which are now becoming central to corporate accountability. From an SDG (Sustainable Development Goals) perspective, this type of automation supports several of the UN’s key targets, especially SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). RPA can streamline data gathering for sustainability reports, improve energy monitoring in production lines, and reduce paper waste by fully digitising workflows. Basically, RPA shows that automation doesn’t have to come at a huge environmental cost. When combined thoughtfully, AI and RPA together can create a more balanced approach to digital transformation. RPA can handle predictable, rule-based work while AI focuses on higher-level problem solving, ideally using “green AI” models that are optimised for energy efficiency. Companies integrating both technologies under a strong ESG framework could significantly reduce their environmental footprint while still benefiting from automation. ESG and Corporate Responsibility However, all of this depends