Artificial Intelligence (AI) has been a hot topic in the news lately. According to 2021 Statista data, the AI market generated $327.5 billion in global revenue and is estimated to grow further in the coming years.
With the rapid advancement of AI technology, it’s no wonder that people are both excited and concerned about its future. One of the most pressing concerns is the ethical implications of artificial intelligence.
Why are Ethics in AI Crucial?
The subject of ethical AI comes from the fact that AI is developed to imitate human intelligence and behavior. That means AI systems are capable of making decisions on behalf of humans. And as AI begins to play a more prominent role in everyone’s lives, the concern over its capabilities and potential biases grows. This part is where data ethics comes in.
The AI code of ethics is a set of guidelines that aim to ensure that artificial intelligence gets developed responsibly and ethically. The goal is to allow the creation and use of AI while minimizing risks to individuals, society and the environment.
Because AI must benefit the common good, many experts have called for the need to establish ethical principles to ensure that it will get used for what they deem acceptable to society. Without these principles, AI could be used in ways that are harmful to certain persons or society as a whole.
What are the 3 Big Ethical Concerns of AI?
The AI ethics framework addresses three broad concerns. And to navigate applied ethics, it’s best to understand these three ethical considerations:
1. Privacy and Surveillance
Technology and data security experts have long warned about the potential for abuse when it comes to data collection.
The Cambridge Analytica scandal is a prime example of how personal data can be mishandled. With AI, the concern is that data collected by corporations and governments could be used to unfairly manipulate or interfere with individuals’ lives.
2. Manipulation of Behavior
AI technologies are highly-based on human intelligence, which makes them good at understanding and modeling human behavior. That also means it can be a weapon to manipulate people’s behavior for ulterior motives. AI tools can be used to target ads, content and even fake news to people to influence their opinions or actions. As a result, there is a heightened concern over how AI can manipulate people on a mass scale.
3. Opacity of AI Systems
Human society is based on the rule of law. But when it comes to an AI system, there are no such rules. Because algorithms power AI, they can be opaque and inscrutable. That means it can be challenging to understand how an AI system came to a particular decision. This lack of transparency is a significant concern because it could lead to errors and bias in AI decision-making.
What are Other Ethical Issues to Consider?
Ethical challenges do not stop at the three significant concerns. As AI adoption grows, there are other ethical issues to keep an eye out.
Here are some of the most pressing ethical AI issues:
Bias in Decision Making
Machine learning models focus on predictive analytics. That usually means they are trained on historical data, which can be biased. As a result, these AI models can inherit the biases of the dataset, which can lead to discriminatory decisions.
For example, AI researchers can inadvertently introduce bias when they collect and label data for training machine learning models. Studies have shown that a model can be biased if the training data is unrepresentative of the real world.
Human-Robot Interaction
Moral principles are not just for humans. With the increasing adoption of robots in society, there is a need to consider the ethical implications of human-robot interaction. For example, should a robot be designed to protect its own existence? What if a robot harms a human by accident? These types of questions need to be considered when it comes to ethics and robots.
Loss of Control
Computer systems are becoming increasingly autonomous. That means they can make decisions without human intervention. As AI gets more advanced, there is a concern that humans will lose control over AI systems.
This concern could significantly affect society, especially if AI systems control critical infrastructures like power grids or transportation networks. So, data scientists and ethicists need to consider the implications of autonomous systems and how to ensure that humans retain control over these systems.
Automation and Employment
The job market is constantly evolving. But with the rise of AI, some jobs are at risk of being automated. With the competitive advantage of machine learning algorithms, there is a concern that intelligent systems will replace humans.
This is already happening in some industries. For example, self-checkout kiosks are replacing cashiers in many stores. And there are now autonomous vehicles that can drive without a human driver. This poses ethical questions about the future of employment and how to protect workers from being replaced by physical machines.
How Do You Ensure AI is Ethical?
Ethical questions concerning what is considered trustworthy AI behavior will continue to increase as technology advances. However, data scientists and developers can take some steps to ensure that AI is ethical.
Below are some factors guarding the responsible use of AI:
Awareness
First, it is crucial to be aware of the ethical issues surrounding AI. That includes understanding the three significant concerns of AI and other ethical problems. With this awareness, you can develop a set of ethical principles for AI.
There are also continuous opportunities to learn about ethical AI. For example, you can attend conferences or read papers on the topic. Many online courses also cover ethical AI.
Principles
One way to ensure ethical AI is to develop principles that guide AI decision-making. These principles can help to ensure that AI systems are transparent, accountable, and fair.
Various frameworks and models can help to develop these principles. For example, the Asilomar AI Principles provide guidelines for the responsible development and use of AI. However, it is important to tailor these principles to the specific context and application of AI. And although these principles are a good starting point, they need to be constantly updated as AI evolves.
Government Regulation
Government regulation can also help to ensure the ethical use of AI. For instance, the European Union has released rules concerning the use of AI. These regulations focus on areas such as data protection, privacy, and safety.
When it comes to regulation, it is important to strike a balance between protecting people and promoting innovation. Too much regulation could stifle AI development. But too little regulation could allow unethical practices to flourish.
Human Intervention
Human interaction is still needed to ensure ethical AI. That’s because AI systems are only as good as the data they are given. By providing the proper oversight, humans can help to ensure that AI systems are used responsibly. This can be in the form of regular check-ins, audits, or testing.
It is also important to have humans in the loop when it comes to decision-making processes. AI systems can make suggestions, but humans should ultimately make the final decision. This ability provides control and prevents AI systems from making unethical decisions.
Public Engagement
There should also be public engagement when it comes to ethical AI. This includes educating the public about AI and its implications. It is also essential to get feedback from the public on proposed AI applications.
There are many ways to engage the public. For example, you can hold workshops, create public awareness campaigns, or develop user-friendly tools. Either way, it is important to ensure that the public has a voice in the development of AI.
What are the 10 Principles of Ethical Artificial Intelligence?
Ethical standards for artificial intelligence (AI) are still being developed. However, there are some general principles that can guide the development and use of AI applications.
Below are ten principles of ethical AI:
Interpretability and explainability
AI systems should be interpretable and explainable. That means users should be able to understand how the system works and why it makes certain decisions. This is important for ensuring that AI systems are accountable and transparent. It also helps to build trust between users and AI systems.
Reliability and robustness
Machine learning techniques are constantly improving. But they are not perfect. It is crucial to ensure that AI systems are reliable and robust and can handle different types of data and situations. It should also operate within its design specifications.
Privacy and data security
AI systems should respect the privacy of individuals and protect their data. Data security is a crucial concern with AI, and guarding against data breaches is essential. This includes cyber threats as well as unauthorized access to data.
Accountability
Tech companies must be accountable for the AI systems they develop and sell. Being transparent about how the system works and providing users with a way to appeal decisions made by AI is essential.
Beneficial
AI systems should be designed to do good and benefit individuals, society, and the environment. They should also avoid causing harm. For example, autonomous weapons should not be designed to kill people.
Privacy
Individuals should be made aware of how their data will be used by AI systems. Informed consent is essential when it comes to data collection. And individuals should have the right to opt-out of data collection if they choose.
Human agency
AI systems should promote the human spirit and creativity. They should not be used to replace humans but to augment them. For example, AI can be used to assist humans in their work, such as by providing them with recommendations or suggestions.
Lawfulness
AI systems should be used in a lawful manner. That includes complying with all applicable laws and regulations. If AI is used to make decisions that could have legal implications, such as in employment or finance, then they should be fair and unbiased.
Fairness
AI systems should be fair and unbiased. They should not discriminate against individuals based on race, gender, age, or other protected characteristics. Cognitive biases can creep into AI systems if they are not carefully monitored, so it is important to be aware of these biases and take steps to avoid them.
Safety
AI systems should be safe. That means they should not cause physical or psychological harm to humans. For example, autonomous vehicles should be designed to avoid accidents. Otherwise, they could pose a severe safety risk.
The presence of AI in today’s world is undeniable. And by fostering a dialogue about ethical AI, the transition toward a modern, digital future can be a smooth one.
