Ensuring Trustworthy AI: Probabilistic Reasoning and Learning

Table of Contents

1. Introduction
2. Probabilistic Reasoning and Learning for Trustworthy AI
3. Examples of Trustworthy AI Issues
   1. Bias in AI-Based Decisions
   2. Fairness Issues
   3. Robustness of AI-Based Decisions
4. Capturing Underlying Distributions with Probabilistic Models
5. Probabilistic Circuits
   1. Overview of Probabilistic Circuits
   2. Efficiently Computing Probabilistic Queries with Probabilistic Circuits
6. Addressing Algorithmic Fairness Issues with Probabilistic Circuits
   1. Learning a Classifier with Biased Labels
   2. Encoding Group Fairness in Probabilistic Circuits
   3. Inferring Hidden Fair Labels with Probabilistic Inference
7. Pushing the Limits of Probabilistic Inference
   1. Characterizing Efficient Operations for Answering Queries
   2. Composing Efficient Operations to Answer Complex Queries
8. Conclusion

Probabilistic Reasoning and Learning for Trustworthy AI

In recent years, the use of artificial intelligence (AI) has become increasingly prevalent in various domains, from healthcare to finance to education. However, as AI-based systems become more complex and integrated into our daily lives, concerns about their trustworthiness and reliability have also grown. In this article, we will explore how probabilistic reasoning and learning can be used to address these issues and ensure that AI-based systems are trustworthy.

Examples of Trustworthy AI Issues

Before we dive into the details of probabilistic reasoning and learning, let's first take a look at some examples of trustworthy AI issues that we might encounter.

Bias in AI-Based Decisions

One of the most significant concerns with AI-based systems is that they can exacerbate bias that already exists in the data. For example, if an AI system is used to make hiring decisions, it might inadvertently discriminate against certain groups of people if the data used to train the system is biased. This can lead to unfair outcomes and perpetuate existing inequalities.

Fairness Issues

Related to the issue of bias is the concept of fairness. How do we ensure that AI-based systems are fair and unbiased? One way to approach this is to compare the decisions made by an AI model across different demographic groups. If there is a discrepancy in the average outcome, we might want to investigate why this is the case and take steps to address it.

Robustness of AI-Based Decisions

Another concern with AI-based systems is their robustness. How do we know that the decisions made by an AI model are reliable and accurate, especially when dealing with incomplete or noisy data? This is particularly important in domains such as healthcare, where decisions made by AI systems can have life-or-death consequences.

Capturing Underlying Distributions with Probabilistic Models

To address these trustworthy AI issues, we need to be able to capture the underlying distribution of the data and reason probabilistically about it. This is where probabilistic models come in. By modeling the data as a probability distribution, we can reason about the uncertainty and variability inherent in the data and make more informed decisions.

Probabilistic Circuits

One Type of probabilistic model that has gained popularity in recent years is the probabilistic circuit. Probabilistic circuits are essentially computational graphs that recursively define probability distributions. By combining distributions in various ways, we can capture more and more complex distributions.

Overview of Probabilistic Circuits

At a high level, probabilistic circuits are just computational graphs that define probability distributions. At the base level, we have a simple distribution, such as a Gaussian or a Bernoulli distribution. We can then combine these distributions recursively to define more complex distributions. For example, we might take a weighted sum of two distributions or multiply two distributions together to obtain a new distribution over a larger set of variables.

Efficiently Computing Probabilistic Queries with Probabilistic Circuits

One of the key advantages of probabilistic circuits is that we can efficiently compute various probabilistic queries with them. For example, we might want to compute the marginal probability of a particular variable or the conditional probability of one variable given another. By enforcing certain structural constraints on the circuit, we can ensure that these queries can be computed efficiently in polynomial time.

Addressing Algorithmic Fairness Issues with Probabilistic Circuits

Now that we have a basic understanding of probabilistic circuits, let's see how we can use them to address algorithmic fairness issues.

Learning a Classifier with Biased Labels

Suppose We Are trying to learn a classifier, but the labels that we are given are not the true target that we want to predict. This might occur, for example, if we are trying to make hiring decisions based on historical employee reviews, but we want to predict job performance, which is a latent variable that is inherently hard to represent. In this case, we can explicitly encode the fact that the labels we are seeing are biased versions of the true target in our probabilistic model.

Encoding Group Fairness in Probabilistic Circuits

To ensure that our model is fair, we can enforce some group fairness on the latent variable that represents the true target. This can be done by introducing some independence assumptions about the distribution and fixing the structure of the circuit in a clever way. By doing so, we can ensure that the independence assumptions hold and that the model is fair.

Inferring Hidden Fair Labels with Probabilistic Inference

Once we have learned the joint distribution over all the variables, including the latent variable that represents the true target, we can use probabilistic inference to infer the hidden fair labels. This can be done efficiently with probabilistic circuits, allowing us to make predictions about the hidden label and clean up the data by inferring what the hidden label should have been for each data sample.

Pushing the Limits of Probabilistic Inference

While probabilistic circuits are a powerful tool for addressing trustworthy AI issues, there is still much work to be done in pushing the limits of probabilistic inference. One approach is to focus on characterizing efficient operations for answering queries and composing these operations to answer more complex queries.

Characterizing Efficient Operations for Answering Queries

By characterizing the efficient operations that go into answering a particular query, we can reuse these operations as building blocks for answering other queries. This can be thought of as composing Lego blocks to answer more complex queries.

Composing Efficient Operations to Answer Complex Queries

By composing these efficient operations, we can answer more complex queries in a systematic way without having to start from scratch each time. This can lead to more efficient and effective probabilistic inference algorithms.

Conclusion

In conclusion, probabilistic reasoning and learning are powerful tools for addressing trustworthy AI issues. By modeling the data as a probability distribution and reasoning probabilistically about it, we can ensure that AI-based systems are fair, unbiased, and reliable. Probabilistic circuits are a particularly useful tool for this, as they allow us to efficiently compute various probabilistic queries and reason about complex distributions. By pushing the limits of probabilistic inference, we can Continue to improve the trustworthiness and reliability of AI-based systems.