Managing AI Risk in Generative AI

What is Generative AI

Generative artificial intelligence (AI) refers to a type of AI that is able to generate new content or data based on a given input. This can include generating text, images, audio, or other types of data.

There are several types of generative AI systems, including:

  1. Generative models: These are machine learning models that are trained on a dataset and can generate new data that is similar to the training data.
  2. Generative adversarial networks (GANs): These are a type of neural network that consists of two models: a generator and a discriminator. The generator generates new data, while the discriminator determines whether the data is real or fake. The two models are trained together in a competitive process, with the goal of the generator creating data that is indistinguishable from real data to the discriminator.
  3. Evolutionary algorithms: These are AI techniques that use a process of natural selection to generate new data or solutions to a problem.

Examples of Popular Applications of Generative AI

Generative AI systems have many potential applications, including creating realistic synthesized images or audio, generating personalized content or recommendations, and designing new molecules or materials. Some of the popular applications of generative AI includes:

Text generation: Generative AI can be used to generate text, such as news articles, product descriptions, or social media posts.

  • ChatGPT (Chat Generative Pre-training Transformer): This is a popular chatbot developed by OpenAI. It is built on-top of GPT-3.5, a natural language processing (NLP) model developed by OpenAI that can generate text in a wide range of styles and formats, including news articles, stories, and poems.

Image and video generation: Generative AI can be used to synthesize realistic images or videos, such as for use in advertising or entertainment.

  • OpenAI Dall-E: This is a generative AI tool that can generate original images based on a given text description.

Audio generation: Generative AI can be used to synthesize realistic audio, such as for use in music or podcasting.

  • Amper Music: This is a generative AI tool that can create original music tracks based on user-specified parameters such as genre, length, and instrumentation.

Personalization: Generative AI can be used to generate personalized content or recommendations, such as personalized product recommendations or customized news feeds.

  • Adobe Sensei: This is a suite of AI and machine learning technologies developed by Adobe that includes generative capabilities for tasks such as image and video generation, content creation, and personalization.

Molecular design: Generative AI can be used to design new molecules or materials, such as for use in pharmaceuticals or manufacturing.

  • AtomNet: This is a machine learning-based approach for predicting the properties of small molecules and designing new molecules with desired properties.

Data augmentation: Generative AI can be used to generate additional data to supplement existing datasets, which can be useful for training machine learning models.

  • DataAugment: This is a Python library that uses a variety of techniques to augment image and text data, including image rotation, scaling, and color augmentation, as well as text generation and translation.

Risk of Generative AI

There are many more other potential applications of generative AI, and the technology is constantly evolving and being applied in new and innovative ways. However, they also present certain risks that need to be carefully managed.  

Here are some examples:

  1. Misuse: Generative AI systems could be used to produce malicious content, such as spam, phishing attacks, or fake news.
  2. Misrepresentation: Generative AI systems might produce content that is not clearly identified as being generated by a machine, potentially leading to confusion or deception.
  3. Bias: Generative AI systems can perpetuate and amplify biases present in their training data, leading to discriminatory or unfair outcomes. This MIT Technology Review article examined the bias in the viral AI avatar app Lensa.
  4. Lack of accountability: It can be difficult to determine who is responsible for the content generated by a generative AI system, leading to a lack of accountability for any negative consequences.
  5. Intellectual property issues: Generative AI systems may be used to produce content that infringes on the intellectual property rights of others.
  6. Economic disruption: Generative AI systems may be able to produce content or perform tasks that could replace human labor, leading to job displacement and economic disruption.

 

Managing Risk in Generative AI

Knowing what some of the risks are, there are several steps that organizations can take to manage risk associated with generative artificial intelligence (AI) systems:

  1. Ensure responsible design and training: When designing and training generative AI systems, it is important to use diverse and representative data sets to reduce the risk of bias, and to carefully evaluate the quality and accuracy of the generated output.
  2. Establish guidelines and protocols: Organizations should establish clear guidelines and protocols for the use of generative AI, including identifying appropriate use cases and setting limits on the types of content that can be generated.
  3. Educate stakeholders: It is important to educate stakeholders about the capabilities and limitations of generative AI, and to build trust and transparency around the use of these systems. This can involve communicating clearly about how the system works and what it is designed to do, and being open and responsive to any questions or concerns.
  4. Implement safeguards: Organizations should implement safeguards to prevent generative AI systems from being used for nefarious or malicious purposes, such as by implementing appropriate oversight and review processes.
  5. Monitor and evaluate: It is important to regularly monitor and evaluate the performance of generative AI systems to ensure that they are functioning as intended and that any potential risks are being adequately managed.

 

The speed of AI innovation is outpacing risk and compliance. By understanding and mitigating risk early on, generative AI can become a powerful force of transformation. That transformation has already begun. 

 

About FAIRLY

FAIRLY is an award-winning on-demand AI Audit platform on a mission to accelerate the broad use of fair and responsible AI by helping organizations bring safer AI models to market. FAIRLY bridges the gap in AI oversight by making it easy to apply policies and controls early in the development process and adhere to them throughout the entire model lifecycle. Our automation platform decreases subjectivity, giving technical and non-technical users the tools they need to meet and audit policy requirements while providing all stakeholders with confidence in model performance.

Visit us at https://www.fairly.ai or follow us on Twitter and LinkedIn.

Post generated by ChatGPT, edited by humans at FAIRLY ;-) 

Recent Posts