Practices for using Generative AI

Navigating the Landscape of Generative AI at Work: Preferred Practices for Success

December, 2023

In the dynamic realm of workplace technology, the integration of generative AI applications has become pivotal for numerous companies. As these advanced systems gain prominence, it's imperative for businesses to establish preferred practices to ensure ethical, secure, and efficient usage. With the surge in public AI programs, larger corporations are increasingly developing their own private, company-based interfaces.

This article delves into preferred practices for unlocking the potential of generative AI at work and at home, emphasizing risk mitigation and productivity optimization. And remember that generative AI produces more than text; generative AI applications can also produce images, video, audio, tables and charts, programming, and other manifestations. 

1. Guarding Your Trade Secrets:

Practice: Avoid divulging sensitive business information to public AI programs, as they may store inputted conversations, posing a risk of sensitive data resurfacing during external searches.

Example: A company discussing proprietary technologies in an AI-generated conversation might inadvertently expose crucial business secrets. Private, enterprise-grade AI models mitigate this risk.

2. Train and Educate Yourself:

Practice: Seek comprehensive training for generative AI tools to understand their limitations, potential biases, and ethical considerations. Enhance productivity through improved prompt management.

Example: Google and Amazon offer free AI training for individuals with a technical inclination, fostering a workforce well-versed in responsible AI usage.

3. Implement Redundancy Checks:

Practice: Integrate redundancy checks and verification mechanisms into your workflow, ensuring human review in critical decision-making scenarios.

Example: Human review adds an extra layer of assurance, reducing the reliance on AI-generated results and enhancing overall accuracy.

4. Choose Wisely:

Practice: Exercise discernment in selecting generative AI platforms, opting for enterprise-grade models through paid subscriptions for enhanced security measures.

Example: Investing in a reputable AI service with a proven track record ensures responsible and confidential handling of organizational information.

5. Mind the Bias:

Practice: Remain vigilant about potential biases in generative AI models, addressing concerns arising from publicly available datasets to maintain fairness and inclusivity.

Example: Regular audits and updates to training data mitigate inadvertent perpetuation of regional biases, reflecting changes in societal norms responsibly.

6. Verify, Don’t Trust:

Practice: Exercise caution and cross-verify AI-generated content, avoiding blind trust in the accuracy of every output to mitigate the risk of "hallucinations."

Example: An AI Hallucination is an instance where a generated output contains incorrect information such as an image of a human with six fingers. Knowing that hallucinations exist underscores the importance of cross-verification to ensure accuracy.

7. Steer Clear of Copyright Concerns:

Practice: Exercise caution to avoid copyright infringement issues related to AI-generated content.

Example: There are current lawsuits in motion that will help define the data modeling of the future.

8. Utilize Explainable AI:

Practice: Opt for generative AI models that prioritize transparency and explainability, fostering user trust and facilitating the identification and resolution of biases.

Example: Understanding how AI arrives at conclusions builds confidence among users, promoting responsible and accountable AI usage.

By incorporating these preferred practices, companies can leverage the benefits of generative AI while upholding ethical standards. A proactive approach to responsible AI usage will play a pivotal role in shaping an innovative and accountable future for the workplace.

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