But many advanced uses of AI can check the limits of existing laws and, under existing legal frameworks, gain some productivity for new patterns of responsibility. Companies seeking to assess the legal risk posed by the implementation of AI must adopt a holistic approach to accountability and responsibility for the degree to which the AI system intends and the performance of the algorithm.
Liability for Outcomes and Intentions
Companies should be happy that their use of AI is socially forgivable and legally acceptable. They need to be clear about what the AI is trying to handle. Hence, ensuring that their program is working according to plan. For example, unlawful discrimination against someone is just as illegal if the discriminatory call is made by an intelligent system rather than a human being.
Further, companies should also ensure that their proposed AI is in accordance with the social norms. That is, it should be consistent with the work ethics and standards. An intelligent algorithm that makes a discriminatory decision affecting the individuals involved might also hurt the company’s reputation even if legal authorities are not involved. Therefore, it is crucial that your AI system is not only working smoothly but also following the standard social laws.
Liability for Action
Usually, proving how your AI algorithm reached a particular decision is a complex process. In fact, you may consider it as something beyond human explanation. However, while defending a certain outcome, you have to prove that your program worked within the allowed parameters. This requires a deep understanding of the code itself, along with the training and testing data.
A well-known example of an algorithmic error is an Uber self-driving car that did not recognize a female cyclist. Hence, the brakes were not applied on time. This led to a collision and consequently fatality. You can put the blame on the testing data, which was not inclusive of bicycles. Hence, leading to several incorrect decisions made by the car.
The incident is a clear example of the cross-over between the law and AI. To avoid any major clashes with the legal system, it is important that your program is working perfectly and not interpreting the data incorrectly.
A careful auditing method is important to establish the reliability associated with the quality of the AI system. The workplace of the United Kingdom Information Commissioner is to identify five risk areas for analysis and reflection in AI decisions:
According to UK Information Commissioner’s Office, currently, there are five major risk areas for analysis in an AI-based decision:
- Significant human reviews in non-machine-driven AI systems.
- Accuracy of the system output and performance.
- Security risks associated with AI.
- Explanation of AI-based decisions.
- Bias and discrimination in the AI.
Liability for Data
Lastly, you are also answerable for the data you use in your AI model. Incorrect or incomplete data may lead to inaccurate decision-making. Further, similar data can also lead to biases in the system. For smart algorithms to run according to expectation, it is important that you provide extensive information covering different scenarios. Moreover, your algorithms should also be in accordance with privacy laws. Therefore, AI systems should not only be working properly but also avoiding any security breaches.
The issue of AI’s liability is as comprehensive as its use cases. In many cases, AI is straightforward and does not cross the set liability thresholds. However, complex systems will require careful thinking and legal analysis. Corporations should also be concerned with the various policies that are present in order to identify appropriate parameters for AI use.
It is pretty clear that AI and ML are the main fields of tomorrow. The way AI is taking over the world is impressive. And, it is expected to follow the same growth trajectory in the future as well. However, with automation, intelligent systems become responsible. Every action is monitored, and companies are answerable to the court of law in case there is an issue. It is important that your AI infrastructure is working within limits. Hence, ensuring a smooth workflow.