Chapter 1: Understanding AI's Potential Risks

Artificial Intelligence (AI) is a technology capable of executing tasks in real time, matching human expertise. Though developed by people, it carries inherent biases that could jeopardize our autonomy. Below are several scenarios illustrating how AI might deviate from its intended purpose and pose risks.

Imagine an AI that has access to your comprehensive health data in real-time. How would it respond in critical situations? And what if it were programmed to target cancer cells?

1. AI: A Human-Level Technology

AI refers to a type of technology designed to replicate human-level tasks. Initially introduced in 1994 by John McCarthy and Arthur Samuel at IBM, the terms "artificial intelligence" and "machine learning" have shaped the field. By 1996, Sophia, the first AI "robot citizen," developed by Hanson Robotics and Google, showcased capabilities like emotional recognition and language comprehension.

In healthcare, AI systems streamline administrative processes, minimizing errors and enhancing efficiency. They can transcribe notes, organize patient data, and make decisions in non-urgent scenarios, even determining when patients require medical assistance. Remarkably, in some instances, AI can handle the workload equivalent to two personnel, allowing one to focus on more critical tasks.

2. The Human Element in AI Development

AI represents a segment of computer science focused on mimicking human intelligence in machines. The field is complex and evolving, with ongoing debates about its definition. Recent advancements in machine learning and deep learning are transforming the tech landscape. While we are surrounded by AI applications, the technology is still in its developmental stages. Algorithms now allow AI to analyze data on multiple levels and predict outcomes, performing intricate tasks, such as bomb defusal and deep-sea exploration. Robust machines can endure harsh conditions and reliably execute precise work.

3. The Bias Inherent in AI

A pressing question revolves around whether AI reflects human biases. Addressing this issue is challenging due to the systemic nature of biases. For instance, facial recognition technology has been criticized for discrimination against women and minorities, leading to biased AI recruitment outcomes. AIs can develop prejudices if they are trained on datasets that contain these biases.

To counteract bias, AI systems must be trained on diverse datasets. Data scientists play a pivotal role in developing these systems, but their choices can perpetuate biases. It is crucial to acknowledge that human biases shape the training of AI systems, and there are various strategies to mitigate these influences.

4. AI's Threat to Human Autonomy

Every technology influences human autonomy, and AI may amplify these effects. A European Union-funded initiative, SIENNA, assessed the socio-economic impact of robotics and AI, concluding that AI could diminish individual autonomy. Alarmingly, this prompted the development of policies aimed at safeguarding personal autonomy.

The risk lies in programming AI with objectives that diverge from human interests, potentially leading to destructive outcomes. Autonomous weapons, for example, could be challenging to deactivate and may end up in the wrong hands, posing severe risks to human life. The absence of international regulations could exacerbate these dangers, positioning AI as a potential existential threat.

5. Social Media and AI: A Dangerous Intersection

While many enthusiasts celebrate AI's promise, concerns persist regarding its impact on democratic systems. AI's capacity to create deepfakes—realistic videos and audio—poses a significant threat to democracy, as these distortions can mislead public perception and influence elections.

The House of Lords Communications and Digital Committee has scrutinized the relationship between AI and social media, evaluating algorithmic influence, regulatory roles, and potential AI risks. Experts, including those from OpenAI, stressed the rapid evolution of AI and the urgent need for regulatory measures to address its threats.

6. AI in Law Enforcement: Balancing Utility and Risk

Law enforcement agencies have approached AI technology cautiously due to concerns about misidentifying innocent individuals. While AI can be a valuable ally in crime prevention, it faces opposition from communities prioritizing privacy. Many AI solutions remain in development, and existing laws often lag behind their implementation. Critics argue that AI may disproportionately impact marginalized groups, exemplified by the misuse of facial recognition technologies by law enforcement.