Responsible AI Practices

Question 1

What is responsible AI?

Answer 1

Responsible AI refers to practices and principles that ensure that AI systems are transparent and trustworthy while mitigating potential risks and negative outcomes.

Question 2

What proactive measures should companies take to ensure responsible AI in their systems?

Answer 2

• Transparency and Accountability: It is transparent and accountable, with monitoring and oversight mechanisms in place.
• Leadership Accountability: It is managed by a leadership team accountable for responsible AI strategies.
• Expertise in Development: It is developed by teams or consultants with expertise in responsible AI principles and practices.
• Guideline Compliance: It is built following responsible AI guidelines.

Question 3

What type of AI requires responsible AI?

Answer 3

Responsible AI is not exclusive to any one form of AI. It should be considered when you are building traditional or generative AI systems.

Question 4

What are the basic differences between traditional AI and generative AI?

Answer 4

Traditional AI vs. Generative AI #flashcard
- Traditional machine learning models perform tasks based on the data you provide.
- They can make predictions such as ranking, sentiment analysis, image classification, and more.
- Each model can perform only one task and needs to be carefully trained on the data.
- As they train, they analyze the data and look for patterns to make predictions based on these patterns.
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Generative AI Characteristics #flashcard
- Generative AI runs on foundation models (FMs) that are pre-trained on massive amounts of general domain data.
- These models can perform multiple tasks and generate content based on user input, usually in the form of a prompt.
- The generated content comes from learning patterns and relationships, enabling the model to predict the desired outcome.
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Question 5

What potential business values can companies benefit from due to the exciting innovations and diverse strengths of foundation models (FMs) and their anticipated new architectures?

Answer 5

Business Values of Foundation Models #flashcard
- Creativity: Create new content and ideas, including conversations, stories, images, videos, and music.
- Productivity: Radically improve productivity across all lines of business, use cases, and industries.
- Connectivity: Connect and engage with customers and across organizations in new ways.
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Question 6

How many types of bias is there and what are they?

Answer 6

__Data Bias__
Definition:
If the training data used to train an AI model is biased or underrepresents certain groups, the resulting model may exhibit biases in its predictions or decisions.

Example:
If an AI system for hiring is trained on historical data that reflects past adverse decisions towards an individual or a group based on their characteristics, it may perpetuate those biases in its recommendations.

__Algorithm Bias__
Definition:
The algorithms and models used in AI systems can introduce biases, even if the training data is unbiased. This can happen due to inherent assumptions or simplifications made by the algorithms, particularly for underrepresented groups.

Key Point:
Machine learning models often optimize for performance, not necessarily for fairness.

__Interaction Bias__
Definition:
Biases can arise from the way humans interact with AI systems or the context in which the AI is deployed.

Example:
If an AI system for facial recognition is primarily tested on a certain demographic group, it may perform poorly on other groups.

__Bias Amplification__
Definition:
AI systems can amplify and perpetuate existing societal biases if not properly designed and monitored.

Key Point:
This can lead to unfair treatment or discrimination against certain groups, even if unintentional. With increased adoption of AI, especially through social media platforms, there is a heightened risk of bias amplifying further.

Question 7

What practices are aimed at mitigating bias throughout the development and operation of an AI system?

Answer 7

• Ensuring diverse and representative data is used for training AI models.
• Carefully auditing algorithms and models for potential biases.
• Incorporating fairness metrics and constraints into the AI development process.
• Promoting transparency and explainability in AI systems to understand their decision-making processes.
• Involving diverse stakeholders and communities in the design and deployment of AI systems.
• Continuously monitoring and updating AI systems to address emerging biases.

Question 8

What are some of the challenges related to generative AI?

Answer 8

__Toxicity__
Definition:
Toxicity is the possibility of generating content (whether it be text, images, or other modalities) that is offensive, disturbing, or otherwise inappropriate. This is a primary concern with generative AI. It is hard to even define and scope toxicity. The subjectivity involved in determining what constitutes toxic content is an additional challenge, and the boundary between restricting toxic content and censorship can be murky and dependent on context and culture.

Examples of Controversy:
- Should quotations that would be considered offensive out of context be suppressed if they are clearly labeled as quotations?
- What about opinions that might be offensive to some users but are clearly labeled as opinions?

Technical Challenges:
Offensive content might be worded in a very subtle or indirect fashion, without the use of obviously inflammatory language.

__Hallucinations__
Definition:
Hallucinations are assertions or claims that sound plausible but are verifiably incorrect. Considering the next-word distribution sampling employed by large language models (LLMs), it is perhaps not surprising that in more objective or factual use cases, LLMs are susceptible to hallucinations.

Example:
A common phenomenon with current LLMs is creating nonexistent scientific citations. Suppose that an LLMs is prompted with the request, “Tell me about some papers by” a particular author. The model is not actually searching for legitimate citations but generating ones from the distribution of words associated with that author. The result might include realistic titles and topics in the area of the author. However, these might not be real articles, and they might include plausible coauthors but not actual ones.

__Intellectual Property__
Definition:
Protecting intellectual property was a problem with early LLMs. This was because the LLMs had a tendency to occasionally produce text or code passages that were verbatim of parts of their training data, resulting in privacy and other concerns. But even improvements in this regard have not prevented reproductions of training content that are more ambiguous and nuanced.

Example of Controversy:
Consider the following prompt for a generative image model: “Create a painting of a skateboarding cat in the style of [name of a famous artist].” If the model is able to do so in a convincing yet original manner because it was trained on images of the specific artist, objections to such mimicry might arise.

__Plagiarism and Cheating__
Definition:
The creative capabilities of generative AI give rise to worries that it will be used to write college essays, writing samples for job applications, and other forms of cheating or illicit copying. Debates on this topic are happening at universities and many other institutions, and attitudes vary widely.

Example of Debate:
Some are in favor of explicitly forbidding any use of generative AI in settings where content is being graded or evaluated, while others argue that educational practices must adapt to, and even embrace, the new technology. But the underlying challenge of verifying that a given piece of content was authored by a person is likely to present concerns in many contexts.

__Disruption of the Nature of Work__
Definition:
The proficiency with which generative AI is able to create compelling text and images, perform well on standardized tests, write entire articles on given topics, and successfully summarize or improve the grammar of provided articles has created some anxiety. There is a concern that some professions might be replaced or seriously disrupted by the technology.

Key Point:
Although this might be premature, it does seem that generative AI will have a transformative effect on many aspects of work. It is possible that many tasks previously beyond automation could be delegated to machines.