AI and Privacy Risks in Artificial Intelligence: Ethics and Governance

Artificial intelligence (AI) has emerged as a mainstream trend over many years. And when AI-powered tools crossed over from sci-fi stories into mainstream consciousness a decade ago, consumer and business technology users alike were generally enthusiastic.

As artificial intelligence adoption accelerated, organizations began collecting vast amounts of data, increasing exposure to AI and privacy risks tied to data collection, machine learning, and automated systems.

Despite all their quirks, virtual assistants such as Amazon’s Alexa, Apple’s Siri, and Google Assistant are considered useful ‘helpers’. These AI-driven technologies generally aren’t considered menacing threats, not by mainstream users, at least.

However, the widespread use of AI technologies has amplified AI security and privacy risks, particularly as AI systems process sensitive data and personal information at scale.

But consumers are becoming more aware of – and increasingly vocal about – the pernicious use of AI behind the scenes to influence, direct or impact their interactions with businesses.

This shift has intensified scrutiny around AI and privacy risks, AI ethics, and responsible AI development, especially as ubiquitous data collection becomes embedded in everyday digital experiences.

The European Union (EU) Commission’s regulatory framework proposal on AI acknowledges “certain AI systems create risks we must address to avoid undesirable outcomes” and states: “The regulation ensures Europeans can trust what AI has to offer.”

Similarly, a press release accompanying the United Kingdom’s (UK) AI Regulation declared: “As AI continues developing rapidly, questions have been raised about the future risks it could pose to people’s privacy, their human rights or their safety.”

The need for stricter rules for responsible AI in the US was highlighted in an article published October 22, 2021, by the White House Office of Science and Technology director Dr. Eric Lander and deputy director Dr. Alondra Nelson:

“In the United States, some of the failings of AI may be unintentional, but they are serious, and they disproportionately affect already marginalized individuals and communities.”

As AI adoption continues to accelerate, organizations are facing a growing concentration of AI and privacy risks driven by automated systems, ubiquitous data collection, and the increasing use of machine learning algorithms. These risks extend beyond technical failures to include AI security and privacy risks such as data breaches, identity theft, misuse of sensitive data, and unintended bias in automated decision making. Addressing these challenges requires a structured approach to AI risk management that integrates data protection, ethical considerations, and governance into every stage of AI development and deployment.

Major AI and privacy risks identified

The top risks identified by businesses when AI intersects with privacy concerns were highlighted in the Privacy and AI Governance Report available to members of the International Association of Privacy Professionals (IAPP), a global information privacy community headquartered in New Hampshire (note: download requires subscription), The report highlights growing AI and privacy risks, underscoring the need for stronger AI risk management and ethical governance as AI systems increasingly handle personal data and sensitive information.

Key risks include:

• Harmful bias – bias in AI resulting in harm to individuals and/or violation of their privacy rights, such as discriminatory decisions about housing, finance, education, and insurance
• Bad governance – lack of clear strategies to manage and mitigate risks from processing personal data in AI systems, or weak application of privacy principles, such as data minimization and specified purposes for collecting and managing personal information
• Lack of legal clarity – businesses are struggling to keep up with the changing regulatory environment and can’t be sure they are implementing the right methods and rules to satisfy due diligence obligations in multiple jurisdictions.

Many of the most significant AI and privacy risks stem from how AI systems are trained and deployed using vast amounts of input data. Training AI systems often requires access to personal data, sensitive information, biometric data, and existing datasets that were not originally collected for AI use. Without strong data minimization practices and governance controls, these practices increase AI security and privacy risks, expose organizations to regulatory scrutiny, and raise serious AI ethics concerns, particularly when AI models are used in high-risk AI systems affecting housing, employment, healthcare, or access to services.

The report also identifies several related risks, including:

• Lack of skills and resources in organizations to tackle new AI and privacy challenges, regulations, and governance
• Failure to apply privacy best practices, which may result in data systems used to train AI systems, including non-consensual use of personal data or secondary uses of data
• Security risks posed by AI systems on a connected network including insider threats, model exploitation, and data breaches.

IAPP’s Privacy and AI Governance Report recommends businesses stay on the right side of consumers – and any existing or upcoming AI regulations – by adopting key principles shared among recently published AI governance guidelines and proposed AI regulations in the EU, UK, and US.

Collectively, these issues represent significant AI and privacy risks that can lead to identity theft, serious privacy breaches, and loss of trust.

The Report notes there is consensus across many jurisdictions on the following AI governance principles:

• Privacy
• Accountability
• Fairness
• Explainability
• Robustness
• Security
• Human oversight.

Effective AI risk management depends on embedding these governance principles into operational processes, not treating them as abstract guidelines. Organizations must assess how AI systems collect, process, and retain personal data, evaluate privacy risks across automated systems, and ensure ongoing oversight of AI models throughout their lifecycle. Without this operational focus, AI and privacy risks can escalate quickly, resulting in data breaches, regulatory enforcement, reputational damage, and erosion of public trust. Strong governance frameworks help align AI security and privacy risks with ethical standards and existing data protection laws.

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AI experts call out privacy and human rights risks

Privacy and ethics experts have certainly warned about the ethical issues of unshackled AI innovation for many years – though few people expected 2023 to be the year AI experts would call time (or at least, time out).

As generative AI models advanced rapidly, concerns around AI and privacy risks, AI ethics, and human rights intensified among researchers and regulatory bodies.

On March 22, 2023, just a week after OpenAI released GPT-4, a major update on its popular ChatGPT chatbot, hundreds of AI experts were joined by tens of thousands of people signing an open letter calling for a pause on giant AI experiments.

The letter highlights severe AI security and privacy risks, including unintended consequences of autonomous AI systems and misuse of sensitive data.

Published by the Future of Life Institute, the letter calls on “all AI labs to immediately pause for at least six months the training of AI systems more powerful than GPT-4.” It warns that “AI systems with human-competitive intelligence pose profound risks to society and humanity.”

The risks posed by AI achieving “smarter-than-human intelligence” are so extreme, according to Eliezer Yudkowsky, lead researcher at the Machine Intelligence Research Institute, that Pausing AI Developments Isn’t Enough. We Need to Shut it All Down.

These warnings underscore why organizations must prioritize AI risk management and ethical standards when deploying AI technologies.

He claimed nightmare scenarios of AI going rogue and causing loss of human life are now technically possible. “Without that precision and preparation, the most likely outcome is AI that does not do what we want and does not care for us nor for sentient life in general.”

Given the hype cycle for AI has visibly swung to fear among some AI experts and consumers, organizations are being urged by consumers and rule makers to adopt safer and fairer practices for developing and using AI immediately – ahead of future legislative requirements.

Addressing AI and privacy risks proactively helps organizations maintain trust, protect personal data, and align AI development with human-centered artificial intelligence principles.

A centralized privacy management platform like TrustArc helps organizations assess AI and privacy risks, support AI risk management programs, and align AI governance with evolving data protection laws.

To manage escalating AI and privacy risks, organizations are increasingly adopting structured approaches that combine policy, technology, and oversight. This includes conducting regular risk assessments, documenting AI use cases, monitoring AI systems for unintended consequences, and aligning AI governance with data protection and security requirements. These practices are essential for mitigating AI security and privacy risks while enabling responsible AI innovation and compliance with emerging regulatory expectations.

FAQs: AI and Privacy Risks

What are AI and privacy risks?

AI and privacy risks refer to threats arising when artificial intelligence systems collect, process, or infer personal data, including data breaches, bias, and misuse of sensitive information.

Why is AI risk management important?

AI risk management helps organizations identify, assess, and mitigate AI security and privacy risks before they cause harm to individuals or violate data protection laws.

How do AI ethics relate to privacy risks?

AI ethics addresses fairness, transparency, and accountability, which are essential to reducing AI and privacy risks and preventing unintended consequences.

What types of AI systems pose the highest privacy risks?

High-risk AI systems include facial recognition, predictive analytics, automated decision-making, and generative AI models trained on vast amounts of personal data.

How can organizations reduce AI and privacy risks?

Organizations can reduce AI and privacy risks by implementing strong governance, minimizing data collection, conducting risk assessments, and using privacy-enhancing technologies.