Implications of causality in artificial intelligence

Area of Science:

• Computer Science
• Artificial Intelligence
• Ethics in Technology

Background:

• Significant investment in AI has led to advancements but also introduced challenges like the 'black box' problem and inherent biases in AI models.
• Existing approaches such as Responsible AI, Fair AI, and Explainable AI aim to mitigate these issues by focusing on ethics, bias correction, and model transparency, respectively.

Purpose of the Study:

• To evaluate various approaches for addressing challenges in artificial intelligence development.
• To highlight the role of Causal AI in promoting robust, reliable, fair, and transparent AI systems.

Main Methods:

• Review and comparison of Responsible AI, Fair AI, Explainable AI, and Causal AI methodologies.
• Analysis of the strengths and weaknesses of each approach, focusing on their effectiveness in mitigating AI-related challenges.

Main Results:

• Responsible, Fair, and Explainable AI approaches present certain limitations and weaknesses.
• Causal AI emerges as a leading methodology with the fewest criticisms, demonstrating significant potential for ethical AI development.

Conclusions:

• Causal AI provides a robust framework for developing more trustworthy and ethical artificial intelligence systems.
• The emphasis on cause-and-effect relationships in Causal AI is crucial for ensuring fairness and transparency in AI applications.