Top 10 AI Ethical Issues We Need to Address Now

AI systems learn to make decisions based on training data, which includes biased human decisions or historical or social inequities – even if societal factors such as gender, race, or orientation are removed. An infamous example of this bias was recently uncovered in a clinical algorithm that hospitals across the US were using to identify patients who would benefit from extra medical care. A bombshell study found the algorithm was assigning unfairly low-risk scores to black patients, using patients’ past healthcare costs as a way to gauge their medical needs – which ultimately functioned as a proxy for race. 

The problem with AI bias is that it’s difficult to know about it until it’s already programmed into the software. This makes it difficult to prevent, requiring an approach that not only includes rigorous data preprocessing to identify and mitigate bias but also involves diverse development teams and the establishment and adherence to ethical AI frameworks to guide responsible development and usage. Regulatory bodies must also play a pivotal role in setting clear guidelines and penalties for non-compliance, ensuring that AI systems are designed and operated with fairness, transparency, and accountability at their core. 

AI relies on vast amounts of data to train its algorithms and improve its performance. While most of this data is publicly available, it can also include sensitive information such as names, addresses and even financial information that are inadvertently scraped from the internet during the training process. This leads to the possibility of AI accidentally generating content containing sensitive information that exposes people’s personal information and goes against privacy regulations around the world. 

AI-powered surveillance systems and data mining techniques can also pose a significant threat to people’s privacy in public. Facial recognition technologies, for instance, have been used by law enforcement agencies to identify people and monitor their activities in public and private spaces. This data can be used to create detailed profiles of individuals, which could be exploited for a variety of purposes, including targeted advertising, social engineering, and even political repression.

With AI systems growing increasingly complex and influential, the need for transparency in their decision-making processes is more important than ever before. Many AI algorithms operate as "black boxes," meaning that even their creators may not fully comprehend how they arrive at specific decisions. This raises concerns about transparency in their decision-making, as it becomes challenging to trace and explain the reasoning behind AI-generated outcomes, especially in high-stakes applications like healthcare and autonomous vehicles.

Transparency is crucial to identifying and rectifying biases within AI systems and ensuring that they do not unfairly discriminate against certain groups. It also enables accountability by allowing stakeholders to understand how decisions are made and hold developers responsible when things go wrong. 

As development in generative AI accelerates, AI systems are becoming better and better at performing tasks that were once thought to be exclusive to humans. This has led to widespread anxiety about the potential for AI to cause job widespread displacement as more and more industries reap its benefits. OpenAI, the creator of the explosive chatbot ChatGPT, has itself warned that its technology could expose up to 80 per cent of jobs globally and see more than a tenth of workplace tasks completed by AI models in their current state. Workers in manufacturing assembly lines, data entry, or customer support roles are particularly at risk as their jobs ate already being automated by A. But white-collar jobs are also at risk too. AI algorithms can process vast datasets and perform complex calculations with remarkable speed and precision, potentially rendering some specialized roles redundant.

The ethical consequences of widespread job displacement could be devastating. Economic disparities may widen as displaced workers struggle to find new employment opportunities, particularly if their skills do not align with emerging AI-driven industries. To prevent AI-induced job displacement, governments and businesses must unite in creating provocative reskilling and upskilling initiatives to equip workers with the skills needed for the AI-driven job market. It’s crucial they also invest in policies and practices that facilitate workforce transitions and provide support for affected employees.

With AI systems becoming more and more sophisticated, the rise of AI-generated deepfakes has made it increasingly difficult to discern between real and fake content. These convincing, AI-crafted videos and audio recordings can seamlessly graft one person's likeness and voice onto another, allowing people to deceive others, manipulate opinions, and seriously damage someone’s reputation by essentially stealing their digital identity. 

The massive popularity of Social media platforms made deepfakes and misinformation more dangerous than ever before. Deepfakes can easily go viral, causing confusion and spreading information rapidly. One example of this was when an AI-generated image of the pope wearing a puffer jacket went viral in March, with many users being unable to discern if the image was in fact real or false. While this image was innocent, the potential for people to create convincing deepfakes with malicious intent is real, and there is so far no regulation to stop this sort of behaviour online. 

In recent years, more and more governments have been investing in autonomous weapons that can select and engage targets without human intervention. While these AI-driven systems are built to reduce casualties by minimising human involvement in armed warfare, critics are concerned that their presence in conflict raises serious ethical and moral concerns.

Autonomous weapons, by definition, can operate independently, making their own decisions about when and how to use lethal force. This means that they can engage with no human involvement, raising profound ethical questions about accountability and responsibility for the consequences of their actions. Experts are also concerned that the system's inability to fully understand complex moral factors could escalate conflicts and lower the threshold for initiating warfare going forward. 

AI is already revolutionising the health industry, enhancing diagnostics to improve and reduce the burden on medical staff. However, there are also a number of challenges that need to be addressed before AI can be ethically adopted in healthcare. One of the biggest challenges is data privacy and security. Healthcare systems contain highly sensitive patient information, and AI's reliance on vast datasets for training and decision-making amplifies the risk of data breaches and unauthorised access from criminals.

The healthcare industry also needs to adapt to AI before it can use it. Healthcare professionals will need to be trained to understand, interpret, and trust AI recommendations, which can require a fundamentally significant shift in clinical practice and the role of medical staff. And training medical professionals across the healthcare industry won't be easy. Healthcare systems around the world rely on different, fragmented software and data formats, making the integration of AI across global healthcare services unique to every hospital. Global healthcare providers will need to agree on a series of interoperable standards, data-sharing protocols, and a commitment to open architectures to maximize the effectiveness of AI tools.

As AI systems become more sophisticated, they may eventually become capable of making their own decisions without human intervention. This raises questions about the ethics of giving machines autonomy, and about how developers can ensure that they make decisions that are aligned with human values. For example, an AI system that is designed to maximize efficiency may make decisions that are harmful to humans, such as deciding to release a dangerous product or pollute the environment.

Another concern is that machines may not be able to learn from their mistakes in the same way that humans can. This means that they may be more likely to make the same mistakes over and over again and commit the same errors until humans stop them from doing so. It’s important that governments carefully consider the ethical implications of giving AI the ability to make their own decisions, especially in ethically challenged sectors such as law enforcement, healthcare and the military. 

Unlike traditional tools or machines, AI often operates independently, making decisions based on complex algorithms and vast datasets. This raises the question of who would be held accountable when things go wrong. It can be difficult to pinpoint responsibility between developers, operators, and the technology itself, leading to a lack of accountability and the affected individuals without recourse or compensation.

The opacity of AI decision-making processes only complicates accountability further. Most AI algorithms are "black boxes," meaning that even their creators may not fully understand how they arrive at specific decisions. This opacity makes it challenging to trace the root causes of errors or discriminatory outcomes, hindering efforts to hold responsible parties accountable. The ethical implications extend beyond individual cases of error or harm. Autonomous AI systems, such as self-driving cars, can make life-or-death decisions in critical situations. Determining how AI should prioritize human safety versus other factors, such as property preservation or avoiding legal liability, is likely to become a major development as AI infiltrates society. 

As AI becomes increasingly integrated into critical domains such as finance, healthcare, and infrastructure, the risks associated with its security are escalating. One of the biggest concerns is AI’s vulnerability to adversarial attacks. These attacks involve manipulating AI models through subtle alterations in input data, forcing them to make incorrect or even harmful decisions. This could be exploited in multiple different ways, such as making autonomous vehicles incorrect decisions and causing accidents, or forcing AI-powered financial systems to make flawed predictions, resulting in financial fraud. This vulnerability endangers lives and raises significant privacy concerns, as AI systems can be manipulated to reveal sensitive information about individuals or organisations.

As well as being exploited in adversary attacks, AI also can be weaponised to amplify already existing cybersecurity threats. Hackers can use AI to generate highly convincing phishing emails, automate probing and exploiting vulnerabilities in systems, and rapidly adapt to defence systems. While many AI companies have restrictions to prevent their tools from being used for these purposes, researchers from Check Point recently discovered that hackers have already found ways to bypass these measures. The Researchers found that hackers were exploiting OpenAI’s API to create malicious content generated by ChatGPT, and were generating Python scripts to launch malware attacks and create convincing phishing attacks in seconds.