Final answer:
Training generative AI involves awareness of its economic impact as well as ethical considerations, such as labor market effects and algorithm biases. Diversification of the engineering team and ethics certifications for AI designers are fundamental steps. Ensuring transparent, responsible AI development is crucial to addressing these concerns.
Step-by-step explanation:
To train generative AI using your company’s data, it is essential to first be aware of the potential impacts such as significant economic growth, as Goldman Sachs suggests AI could raise global GDP by 7%. However, ethical concerns regarding labor market disruption, data privacy, and biases in algorithms must be taken into account. Ethicist Genevieve Bell emphasizes the need to approach AI development responsibly, with human-centric technology in mind. For effective training, it is crucial to:
- Diversify the engineering team to include various scientific and humanities disciplines for more rounded perspectives.
- Implement ethics certification programs for AI designers to ensure responsible AI development.
- Introduce measures to reduce biases such as utilizing accuracy nudges and allowing researchers access to data with protections like differential privacy.
It is also vital to comprehend the spectrum of thought about the governance of AI and recognize its presence in leading industries. Transparency in AI's legal considerations and an understanding of both the benefits and threats of AI, as raised by industry leaders and philosophers like Bostrom, are paramount.