Understanding the dangers of ungoverned AI

• Artificial intelligence (AI) is one of the greatest technological advancements of the last decade. But the rapid development and expansive global adoption has left headlines, governments, and everyday people asking—is AI dangerous?

• The truth is that artificial intelligence can be a powerful tool, but without the appropriate AI governance structures and human oversight, it can present significant risks, including cyber breaches, job displacement, and biased decision-making. 

• This post will examine the potential harms of artificial intelligence, including the current landscape of AI risks, underlying concerns during AI development, three real-life case studies, and more. 

• Defining the dangers of AI systems

• Underlying concerns with AI development

• 3 case studies of real-life AI risks

• The future of AI safety and risk

Ungoverned AI poses several risks to society, both seen and unseen. Automated decision-making is one example of a visible AI risk with clear potential for negative outcomes.

These systems, which include everything from social media algorithms to autonomous vehicles, are designed to make decisions based on user data. And while they can increase efficiency and personalization, without human intervention and the right AI governance structures, they can also be privacy invasive and biased.

Less visible, though equally concerning, is the danger presented by lack of transparency in AI decision-making, often referred to as 'black box' AI. If a system’s decision-making processes are not understandable by humans, trusting it with critical decisions or holding it accountable for adverse outcomes becomes a challenge.

As artificial intelligence has proliferated, so have concerns surrounding the technology. In the mid-20th century, concerns around AI were mainly focused on the potential for machines to outpace human intelligence. 

But as the technology was in its infancy and the necessary computing power wasn’t yet available, these concerns were largely theoretical. 

However, recent advancements in AI and other emerging technologies have brought these concerns to the fore. The advent of machine learning (ML) and deep learning techniques has enabled AI systems to learn and make decisions on their own, raising concrete issues around privacy, fairness, transparency, and security.

In the digital age, data is a valuable commodity—and artificial intelligence systems, with their ability to process vast volumes of data, pose profound risks to personal privacy. 

From social media platforms to smart devices, AI technologies routinely collect, analyze, and store user data. This data, which can range from online shopping habits to health records, can be used to create personalized experiences. However, without stringent checks on the way this data is collected, processed, and stored, it can also be exploited. 

Perhaps more troubling, is that without a clear understanding of the way data moves into and out of LLMs, companies may inadvertently share personal or sensitive data with an AI—effectively losing control of the data from that point forward.

AI automation has sparked fears about job displacement, as more traditional roles could be rendered obsolete. Though automation isn't a new phenomenon, AI has accelerated the process—enabling machines to perform not just manual tasks, but intellectual ones too.

While some argue artificial intelligence will create more jobs to replace those it displaces, the economic implications of AI-induced job losses could be substantial, with increased unemployment and income inequality emerging as the most pressing concerns.

One of the major challenges with AI development is these systems' inherent complexity, particularly those based on deep learning algorithms. Often referred to as "black box" AI, these models are characterized by their deep complexity and lack of interpretability. 

They ingest vast amounts of data, churn through hundreds of layers of computations and then produce an output. But the actual decision-making process—the path from input to output—remains opaque even to AI experts. 

This makes it incredibly challenging to diagnose, understand, and correct issues when they arise.

AI systems are trained using huge amounts of data, including historical records, user interactions, and digital societal trends. When the data used to train an AI model is biased, the model will often reflect and perpetuate these biases, leading to discriminatory outcomes. 

For example, AI recruitment tools trained on historical hiring data may inadvertently favor candidates who reflect past hiring trends, potentially excluding qualified candidates from underrepresented groups. 

For example, in 2018 Amazon shut down an AI recruitment tool after it was found to have significant bias against women. 

Mitigating AI bias, especially in tools that influence human behavior, requires a conscious effort to diversify model training data, as well as the teams building the model itself. Unbiased artificial intelligence is not a naturally occurring outcome, but rather a deliberate act of inclusion and oversight.

AI is increasingly integrated into our daily lives, exposing us to various risks. 

For example, the personalized recommendations we receive on e-commerce sites or streaming platforms are a result of AI algorithms analyzing our online behavior. While these recommendations may enhance our user experience, they also mean our online activities, preferences, and behavioral patterns are constantly tracked, recorded, and analyzed. 

Similarly, AI-powered digital assistants and smart home devices, while providing convenience, can inadvertently become sources of eavesdropping or data breaches.

Furthermore, AI systems used in social media algorithms may expose users to echo chambers, a form of social manipulation in which users are only fed information that aligns with their existing opinions or beliefs.

In one of the most high-profile instances of AI misuse, political consulting firm Cambridge Analytica harvested personal data from millions of Facebook users without consent. 

Using artificial intelligence and machine learning techniques, the firm analyzed this data to build psychological profiles of voters, which were then used to deliver targeted political ads during the 2016 U.S. Presidential Election. This incident raised serious concerns about data privacy, the ethical use of AI, and the potential for AI-powered manipulation of democratic processes.

Amazon faced backlash when it was revealed their AI-based recruitment tool was showing bias against women. The tool was trained on resumes submitted to the company over a 10-year period, the majority of which came from men. 

This resulted in the AI algorithm penalizing resumes that included the word "women's," such as "women's chess club captain," and ranked graduates of two all-women's colleges lower. Despite attempts to correct the bias, Amazon ultimately scrapped the system.

AI-powered facial recognition systems have come under scrutiny for instances of racial bias and misidentification. In 2020, a man in Detroit became the first known case of an individual being wrongly arrested due to a faulty facial recognition match. 

The AI system misidentified him based on a grainy surveillance video, demonstrating the high stakes of relying on these tools in sensitive contexts like law enforcement.

As AI technology evolves, society will face new challenges related to AI systems. 

One potential issue is the advent of 'deepfakes', hyper-realistic digital forgeries produced by AI algorithms that can manipulate visual and audio content to appear real. These AI-generated images, videos, or voice recordings can be convincing enough to deceive audiences, posing serious threats to our ability to discern truth from fiction. 

Bad actors can also potentially deploy AI for cyberattacks, with AI-powered phishing attempts, data breaches, or autonomous weapons systems posing significant security threats.

With these scenarios as the backdrop, the future of AI necessitates robust ethical, regulatory, and technical safeguards to mitigate these potential risks.

A strong foundation of education and awareness is crucial in promoting responsible AI practices. Education about AI should be integrated into both formal and informal learning platforms, targeting a wide range of demographics. 

Public awareness campaigns and AI literacy programs can play a significant role in informing the wider public about AI and its implications. Through accessible and engaging content, these campaigns can demystify AI, dispelling misconceptions and fears while highlighting the importance of ethical AI practices. 

Continued innovation, dialogue, and responsible development are pivotal in shaping the future of artificial intelligence tools. As the technology evolves, it’s imperative to foster an environment of continuous innovation that not only advances AI capabilities but also addresses its associated risks. 

However, it’s equally critical that AI creators deploy responsible practices in AI development—emphasizing ethical considerations, transparency, and fairness to ensure that AI technology benefits society without causing harm. 

In this way, we can harness the potential of AI while safeguarding potential pitfalls.

Transcend is the governance layer for enterprise data—helping companies automate and future-proof their privacy compliance and implement robust AI governance across an entire tech stack.

Transcend Pathfinder gives your company the technical guardrails to adopt new AI technologies with confidence. While Transcend Data Mapping goes beyond observability to power your privacy program with smart governance suggestions.

Ensure nothing is tracked without user consent using Transcend Consent, automate data subject request workflows with Privacy Requests, and mitigate risk with smarter privacy Assessments.

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• AI governance 101

• Introducing Transcend Pathfinder—the governance layer for enterprise AI

• Transcend joins IAPP’s AI Governance Center as 
first top-tier foundational supporter

• Introducing the AI Risk Assessment template

• 5 steps for choosing the right AI governance software