AI Ethics and Accountability: Unique Challenges of AI and Machine Learning

Name: AI Ethics and Accountability: Unique Challenges of AI and Machine Learning
Author: Briana Brownell

Learn the ways machine learning provides different ethical challenges to other forms of software development.

Preview the first lesson free — get full access to all 5 lessons.

Course: On-Demand

(gentle music) Machine learning poses some unique challenges in terms of ethics and accountability. In many ways, decision systems that use machine learning are more challenging to address than other forms of software development. We'll start with the most common type of machine learning. Supervised learning is almost certainly the most common style of machine learning currently used. There are many use cases for this type of learning, such as predicting which factors most influence customer experience, predicting and preventing customer churn, which refers to the loss of customers, forecasting the success of a marketing strategy to optimize channel spend, projecting system usage or foot traffic, or predicting user behavior and providing a nudge to change it. Notice that all these cases have some kind of predicted outcome. That's the differentiator between supervised machine learning and other types we'll review. These predictions and their subsequent actions are based on collected data and many of the ethical considerations concern this data. Yet others relate to the impact of the prediction, which is unique to supervised machine learning. There are both technical and non-technical considerations around ethics and accountability that are especially applicable to supervised learning models. In terms of technical considerations, some methods of supervised learning are especially susceptible to the influence of outliers in the dataset. So when data is collected that contains these errors, the overall model might perform well in training, but then fail upon deployment. Sometimes we know this ahead of time when the predictive model performs poorly with a test set, but often we don't know until deployment. This adds several additional risks around the impact of the system in terms of bias and fairness. Supervised learning depends on having labeled data. Often this labeling is done by humans based on a set of qualitative criteria. It's difficult not only for each person to be consistent in their labeling, but also for there to be consistency across multiple raters. The raters themselves bring their own biases to the labeling problem, which has the possibility of accidentally introducing issues into the resulting supervised learning model. For example, imagine that raters are providing a score of how strong a resume is, and this data is then used to create an automated system that will score resumes automatically. These raters may themselves be biased toward or against certain schools, previous companies, or extracurricular activities. As they score the resumes, they may replicate their own bias into the supervised machine learning model by way of providing biased labeling. More complicated models, like those built using deep neural networks, may be difficult to explain and thus have issues with transparency. Plus, due to their complexity, it may be more difficult to verify the security and safety of the resulting machine learning model. All these factors mean that supervised learning has a large list of ethical considerations that should be addressed.

Intermediate Provider : Briana Brownell 5 Lessons · 16m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

In these lessons on AI and Machine Learning’s Unique Challenges, you’ll explore the types of machine learning, including supervised, which uses labeled data to make predictions; unsupervised, a series of techniques to understand the structure of data without an outcome variable; and reinforcement learning, one of the oldest styles of machine learning that uses a goal-based method of learning. You’ll also understand ethical considerations around neural networks and generative AI.

Machine learning underlies many AI systems built today. Artificial neural networks are among the most successful machine learning methods. They solve many of the field's hardest problems, including those that spent decades unsolved. But using neural networks has also added some new challenges to AI, including how to test for edge cases and for robustness of the outputs. The complexity of the testing systems might make them particularly difficult to analyze. We will review the specific considerations that neural networks have in terms of ethics.

Generative AI is a newer and emerging area relevant to AI ethics and accountability.

We’ll discuss the Lovelace test that aims to test whether a computer could generate something surprising and original.

What You'll Learn

Differentiate between the main types of machine learning: supervised, unsupervised, and reinforcement learning
Recognize the unique ethical challenges posed by artificial neural networks
Explain the ethical challenges around generative AI
Examine why neural networks make testing for edge cases and output robustness difficult
Describe the Lovelace test for whether a computer can generate something surprising and original

Key Takeaways

Supervised learning uses labeled data to make predictions, unsupervised learning uses techniques to understand the structure of data without an outcome variable, and reinforcement learning is a goal-based method of learning.
Artificial neural networks are among the most successful machine learning methods and have solved problems that remained unsolved for decades.
Neural networks introduce new challenges to AI, including how to test for edge cases and for robustness of outputs, and their testing systems can be particularly difficult to analyze.
Generative AI is a newer, emerging area relevant to AI ethics and accountability.
The Lovelace test aims to assess whether a computer could generate something surprising and original.

Frequently Asked Questions

What does this course cover?

It covers the types of machine learning (supervised, unsupervised, and reinforcement learning), the ethical considerations around artificial neural networks, and the ethical challenges of generative AI, including a discussion of the Lovelace test.

What are the main learning objectives?

To differentiate between the main types of machine learning, recognize the unique challenges with artificial neural networks, and explain the ethical challenges around generative AI.

Which lessons are included?

The course includes lessons on Supervised Learning, Unsupervised Learning, Reinforcement Learning, Neural Networks, and Generative AI.

What skills will I build?

You will build skills in Artificial Intelligence, Automated Machine Learning, Machine Learning, Machine Learning Methods, Supervised Learning, and Unsupervised Learning.

Why are neural networks given special ethical attention in this course?

Because, while among the most successful machine learning methods, neural networks add new challenges such as testing for edge cases and output robustness, and their complexity can make the testing systems particularly difficult to analyze.