Machine learning is a branch of artificial intelligence that allows computers to learn from data and make predictions or decisions without being explicitly programmed. For example, machine learning can help us recognize faces, recommend products, diagnose diseases, or drive cars.
Legal consequences of machine learning is a very important and complex topic, because machine learning can have significant impacts on individuals and society, both positive and negative. Some of the legal consequences of machine learning are:
- Invasion of the privacy of individuals. Machine learning often relies on large amounts of personal data, such as biometric information, location data, health records, or online behavior. This data can reveal sensitive or intimate aspects of people’s lives, such as their preferences, opinions, beliefs, or emotions. If this data is collected, processed, or shared without proper consent or protection, it can violate people’s privacy rights and expose them to risks such as identity theft, fraud, or discrimination. According to the European Union’s General Data Protection Regulation (GDPR), which came into effect in 2018, individuals have the right to access, correct, delete, and restrict the processing of their personal data, as well as the right to object to automated decision making based on their data.
- Lack of transparency in automated decision making. Machine learning can also be used to make decisions that affect people’s lives, such as whether they get a loan, a job, a medical treatment, or a social benefit. However, some machine learning models are very complex and opaque, meaning that it is difficult or impossible to understand how they work or why they produce certain outcomes. This can raise issues of accountability, fairness, and explainability. For example, if a machine learning model rejects someone’s loan application, how can we ensure that the decision was based on relevant and accurate criteria and not on hidden biases or errors? How can we provide meaningful feedback or recourse to the person affected by the decision? The GDPR also requires that individuals have the right to receive meaningful information about the logic involved in automated decision making that affects them. Moreover, Microsoft has proposed six key principles for responsible AI: accountability, inclusiveness, reliability and safety, fairness, transparency, and privacy and security.
- Profiling, its lack of regulation and the resulting discriminations and biases. Machine learning can also be used to create profiles of individuals or groups based on their data, such as their demographics, behavior, preferences, or personality traits. These profiles can then be used to target them with personalized offers, advertisements, or messages. However, profiling can also lead to discrimination and bias if it is based on sensitive or protected characteristics such as race, gender, age, religion, or sexual orientation. For example, if a machine learning model predicts that someone is more likely to commit a crime based on their race or ethnicity, how can we prevent this from influencing their treatment by law enforcement or the justice system? The GDPR also prohibits discrimination based on automated processing of personal data that reveals special categories of data such as racial or ethnic origin. Furthermore, researchers have proposed various methods and frameworks for measuring and mitigating bias and unfairness in machine learning models.
These are some of the legal consequences of machine learning that we need to be aware of and address. As a machine learning engineer, I have a responsibility to design and implement machine learning systems that respect the law and the ethical principles of human dignity, autonomy, justice, and solidarity.
I also need to collaborate with other stakeholders such as regulators, policymakers, users, and civil society to ensure that machine learning is used for good and not for evil.