Generative AI is revolutionizing industries by creating realistic content such as images, text, and even music. We recently covered a survey report by Gartner that summarizes the benefits, challenges, and use cases of Generative AI.
Its rapid evolution has raised legitimate concerns among companies. In this article, we look into some of these concerns and potential frameworks for establishing effective governance and policies.
1. Lack of Real-World Source Data: Acquiring diverse data for training Generative AI models can be challenging, especially in niche sectors. This can limit the model's ability to generate accurate content.
2. Inherited Bias in the Output: Generative AI models may carry inherent biases from the training data, leading to inappropriate or discriminatory outputs.
3. Inaccurate Results from Noisy Data: Noisy data can lead to misleading outputs, degrading the model's performance.
4. Data Security: Handling sensitive data raises concerns about data breaches and unauthorized access.
5. Vulnerabilities and Inaccuracy of Generated Results: Generative AI models can be susceptible to adversarial attacks, leading to manipulated content. Prompt injection attacks are becoming more common.
6. Lack of Corporate Governance Policies: Without comprehensive governance policies, the use of Generative AI may be unregulated and potentially harmful.
To mitigate the concerns surrounding Generative AI, companies need robust governance and well-defined policies, which cover the following;
1. Data Acquisition and Curation: Policies emphasize sourcing high-quality, diverse data for model training.
2. Bias Mitigation: Policies mandate identifying and addressing bias in training data.
3. Data Preprocessing and Noise Reduction: Rigorous data preprocessing eliminates noise and outliers.
4. Secure Data Management: Policies prioritize data security, implementing encryption, access controls, and secure data storage practices.
5. Adversarial Defense Mechanisms: Policies encourage the integration of defense mechanisms in Generative AI models to safeguard against attacks.
6. Corporate Governance and Compliance: Policies outline guidelines for the responsible and compliant use of Generative AI.
We learned and distilled how companies such as Adobe and Microsoft establish their data governance and AI policies, and created the framework below that companies could potentially use.
1. Define Objectives and Scope: Identify the specific objectives for using Generative AI. This ensures a targeted and effective implementation.
2. Multidisciplinary Collaboration: Form a team comprising data scientists, ethicists, legal experts, and business stakeholders to address all considerations.
3. Risk Assessment and Mitigation: Conduct a comprehensive risk assessment to identify potential pitfalls and outline strategies.
4. Ethical Guidelines: Establish clear ethical guidelines that prioritize transparency, fairness, and privacy. A great example is Adobe’s AI ethics principles.
5. Data Governance: Develop policies that emphasize data quality, privacy, and security.
6. Continuous Evaluation and Improvement: Regularly review and update the policies to adapt to changing landscapes.
Learn more about data labeling use cases in Generative AI.
Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.
I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.