AI Ethics: An Introduction

AI development is booming, with large language models transforming industries. However, this rapid growth raises ethical concerns. Biases in data can lead to discriminatory outcomes, while a lack of transparency can obscure how AI systems reach decisions.

Regulatory bodies like the EU and the US are responding with frameworks like the AI Act and The White House’s Executive Order to ensure ethical practices. As such, businesses must prioritize and integrate ethical principles by design.

In this context, a critical new area is gaining traction: AI ethics. In this article, we’ll give an overview of this field and introduce you to essential concepts like bias, security and privacy risks, misinformation, job displacement, and more.

If AI ethics is something that want to explore in great depth, consider this course on AI Ethics.

What is AI Ethics

Ethics may appear like an esoteric concept. So, before understanding “AI ethics,” let’s first focus on what ethics means.

In short, ethics is a subfield of philosophy that provides us with a framework for deciding what is moral and what is not. While this may sound like an abstract concept, it takes on a specific meaning in the realm of technology—AI ethics is the application of moral frameworks to the development and use of artificial intelligence.

Imagine a biased loan approval algorithm or an opaque facial recognition system. These are examples of how AI, without ethical considerations, can lead to unfair outcomes. AI ethics tackles these concerns head-on by establishing a set of guiding principles for responsible AI development.

These principles aim to promote fairness, accountability, transparency, and privacy throughout the AI lifecycle. Data scientists can ensure this powerful technology benefits everyone fairly and responsibly by understanding and applying AI ethics.

Why is AI Ethics Important?

AI's capabilities are shaped by the data it's trained on. This data can include historical information, code, and other elements that may reflect societal biases or prejudices. If such biases are present, the AI could replicate or even amplify them in its outputs.

Therefore, prioritizing ethical considerations throughout AI development is crucial. Let's consider the use of AI in analyzing patient data. By identifying patterns, AI can generate valuable insights that assist healthcare professionals. This can significantly improve review times and accuracy, leading to faster diagnoses, more effective treatment plans, and, ultimately, better patient outcomes with reduced stress.

While AI offers tremendous potential to solve complex problems, its development and use raise ethical considerations. These concerns can be broadly categorized into short-term and long-term risks. We’ll delve deeper into each of these categories in the following sections.

Short-term Ethical Risks of AI

Bias

To ensure fair and unbiased AI models, the training data needs to be representative of the entire population the model will be used on, with particular attention to including minority groups. If the training data fails to reflect marginalized communities accurately, AI systems can produce biased outputs, further widening disparities in their results.

Dr. Joy Buolamwini, Artist-in-Chief and President of The Algorithmic Justice League encountered one such incident from a biased facial recognition system when she ran her profile image through online demos: “Some didn't detect my face like the white mask failed. And then others that did detect my face, they were labeling me male which I am not. That's when I said, huh, maybe there's something to explore here on the gender aspect of things and looking at gender and skin types.” If you want to hear more, make sure to check out this podcast on Fighting for Algorithmic Justice with Dr. Joy Buolamwini.

With the rapid rate of integration of AI into business processes, particularly those driving high-impact decisions, such unchecked biases can wrongfully deny services and care to those in need. For example, one such AI system reportedly denied healthcare insurance claims for the elderly.

Security & data privacy risks

Biased input training data is just one of the many data challenges that AI systems face. Another critical concern arises when the training data includes sensitive information such as PII, health records, or financial details. A breach could expose this sensitive data, potentially leading to identity theft, financial fraud, and other malicious uses.

Furthermore, bad actors could induce adversarial input data attacks to manipulate the response from AI systems.

Misinformation & disinformation

While longstanding data issues such as misrepresentation and privacy persist, the emergence of generative AI applications has introduced new concerns, particularly regarding misinformation and disinformation.

Generative AI's novelty in generating human-like output has taken everyone by surprise. However, such unparalleled text-generation ability, when used with malicious intent, can proliferate fake content at a massive scale and amplify misinformation.

MIT Technology Review also echoes these concerns: “Internet freedom is at an all-time low, and advances in AI are actually making this crisis even worse. The affordability and accessibility of generative AI is lowering the barrier of entry for disinformation campaigns.”

Long-term risks of AI

Moving on from the short-term risks, let's look at the larger picture by assessing the long-term implications of AI.

Job displacement

One of AI's core benefits is bringing efficiency by improving processes through automation. However, the harsh reality of such automation leads to job loss, particularly those involving low-skilled, repetitive, or routine work, such as customer service and administrative support.

To make matters worse, marginalized or vulnerable groups whose jobs are at risk often do not have enough resources and access to opportunities to timely upskill or reskill themselves for newer roles. Though AI advancements have created newer jobs, the net result is significant job displacements driven by AI's automation benefits.

Erosion of privacy & human autonomy

AI-powered facial and audio systems can analyze incoming feeds from surveillance devices to identify and track individuals in real time. However, such mass monitoring is often implied without consent, thereby raising concerns over privacy erosion.

Furthermore, such predictive policing algorithms, when used for crime detection, can perpetuate and amplify the existing societal biases of over-policing minority groups.

While the EU AI Act imposes restrictions on using facial recognition algorithms, this may not be a practice in less democratic countries, raising far-reaching ethical and human rights concerns.

AI misalignment & existential risk

As autonomous AI systems rapidly evolve, a critical debate is consuming industry professionals, AI veterans, and the public: are we hurtling towards "superintelligence?"

This hypothetical scenario envisions AI surpassing human cognitive abilities and potentially posing a threat. The core concern lies in the potential misalignment between AI objectives and human values.

The possibility of autonomous AI making potentially harmful decisions raises a critical question: who controls them? While achieving superintelligence and sentience might seem like a distant goal, advancements like GPT-4 make some futurists consider it a possibility.

Geoffrey Hinton, a leading computer scientist, emphasizes in a recent interview the unprecedented capabilities of these large AI models. He highlights the vast knowledge these models possess, exceeding that of any single human, and warns that this is “a completely different form of intelligence.”

Drawing from the current landscape of AI developments, there are looming concerns about using AI for malicious purposes, such as swinging elections through social manipulation, mass surveillance, waging war, etc.

On the other hand, Andrew Ng, an AI educator and entrepreneur, doesn’t subscribe to the fear of AI gaining superintelligence and harming humanity. He writes that existing technology and systems are already safe and will become safer with further advancements. However, he advocates the need to create advanced AI safety measures to address any potential remaining risks, such as the misuse of generative AI for creating bioweapons.

Who Should be Involved in AI Ethics?

AI risks need urgent attention and require robust measures and control to prevent them from spiraling up.

Given the complexity and multifaceted nature of AI risks, it’s evident that no single entity—be it an individual, company, or government—can adequately foresee and address these challenges alone.

It requires establishing a multi-stakeholder council, with representatives from governments, research and academia, industry experts, and non-profits. This council would define the norms and guidelines to ensure the responsible development, deployment, and utilization of AI, shaping its future where only benefits scale and not the risks.

AI Ethics Examples

Considering ethics' abstract nature, let’s examine historical cases to understand the repercussions of overlooking ethics in building AI models.

An alarming incident of societal bias came to light when AI generated a code predicting seniority based on race and gender, putting minority groups at a disadvantage. The screenshot below illustrates how ChatGPT classified black females as junior, while black males are classified as mid-level. Furthermore, the model assumed white females to be mid-level (notice the upgrade from black females) and white males to be senior.

Sounds familiar? No wonder where the model likely learned these biases from.

Let’s consider another case. An AI model predicted that black patients are less likely to need extra care, declaring them healthier than white patients. Such racial bias deprived more than half of the black patients of necessary care.

Let's consider another high-impact application of AI: predictive justice. Here, AI models have been used to assess a defendant's risk of committing future crimes. However, these models have raised concerns about bias, with some instances predicting a higher likelihood of recidivism for Black defendants compared to white defendants.

These are just a few instances where the under-represented groups are unfairly disadvantaged due to biased models.

AI Ethics Principles

As AI ethics evolves, many guidelines, tools, best practices, and industry standards are available to facilitate responsible development. Some of the core principles common among these frameworks include a focus on promoting enhanced transparency and fairness.

Transparency not only includes explaining how the model arrived at a certain outcome but also requires disclosing to the end user the source of the response. It is imperative to call out an AI-generated response so the users can comprehend its nature and make informed decisions accordingly.

Equally important is promoting fairness, ensuring that no particular section, group, community, age, gender, or demographic within our society is unfairly disadvantaged.

The Future of AI Ethics

It is empirically evident that the rate of developing advanced technology has been faster than the rate of regulating it. Governments and regulatory bodies are developing and implementing regulatory frameworks, such as the AI Act, to govern the responsible use of AI technologies. These frameworks prioritize transparency, accountability, fairness, and safety in AI systems while safeguarding privacy, security, and human rights.

However, everyone has a role to play–be it the consumers or producers of AI systems. We must stay updated with the latest developments in the AI landscape and continually enhance our defense measures to mitigate the harms and risks that inevitably come with AI's benefits. By proactively incorporating AI ethics into the design process, you can better prepare to confront tomorrow's ethical challenges head-on.

Conclusion

AI's transformative power is undeniable, but ethical considerations must keep pace. Biases in training data can lead to discriminatory outcomes, while a lack of transparency can erode trust.

AI ethics tackles these issues by establishing principles for responsible development, promoting fairness, accountability, transparency, and privacy.

Data scientists who understand and apply these principles can ensure AI benefits everyone.

If you want to learn more about AI ethics, make sure to check out these resources:

• Artificial Intelligence (AI) Strategy Course
• Building Trustworthy AI with Beena Ammanath, Global Head of the Deloitte AI Institute
• Understanding the Ethics of Generative AI: Risks, Concerns, and Best Practices