Final answer:
Explainability and interpretability in AI systems are crucial for building trust, improving AI performance, and communicating results to non-technical stakeholders, encompassing reasons (A), (B), and (C), therefore option (D) is correct. It also ties into broader concerns of legal transparency and the ethical integration of AI in society.
Step-by-step explanation:
Explainability and interpretability are important in AI systems for several vital reasons. Firstly, it ensures trust in the decisions that the AI makes by allowing us to understand and justify those decisions (Option A). Secondly, it provides an opportunity to improve AI performance by allowing us to fine-tune and correct the system as necessary (Option B). Finally, having interpretable results allows for effective communication to stakeholders, such as managers and executives, who may not have a technical background but need to understand AI decisions (Option C). In essence, all of the above points (Option D) are essential for responsible AI usage.
The importance of AI transparency is highlighted not only in its application but also within legal contexts. Ensuring that AI acts in an understandable and predictable manner aligns with public concerns such as cybercrime, privacy infringement, and ethical governance. In addition, as artificial intelligence becomes more ingrained in industries like autonomous vehicles and various service sectors, the ability to explain AI behavior and decisions becomes crucial for both safety and ethical considerations. Therefore, embracing approaches that enhance the transparency and clarity of AI systems is paramount.