AI Ethics and Accountability: Bias, Inclusivity, and Fairness in Artificial Intelligence

Name: AI Ethics and Accountability: Bias, Inclusivity, and Fairness in Artificial Intelligence
Author: Briana Brownell

Learn why bias and fairness are such important concepts in AI ethics

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Course: On-Demand

(bouncy music) As AI systems begin to impact even more areas of our lives, the public is becoming more and more aware of the dangers that biased systems can have on individuals and on society as a whole. The real risk of bias in AI systems is that it can scale harmful unfairness to a massive degree and in ways that might not have been predicted. When AI functionality is deployed in systems which have significant impact on people's lives, as in credit reporting, employment, or the criminal justice system, they can severely damage public trust in AI systems. For this reason, much attention is being paid to ways that practitioners can detect bias in AI systems as they're being built and prevent biased AI systems from being deployed. Bias is especially important when building machine learning models. There are many domains where our data is collected. This data which is used to train machine learning models embedded into AI systems, might be biased toward or against certain types of individuals based on factors like gender and race. That's because the data collected may systematically include or exclude certain groups. Data labeling can also introduce bias. But technology can provide a partial solution to some of these challenges. There are technical tools that can be used to detect AI bias as well as best practices for data pre-processing, feature selection, and model training. Additionally, bias can exist within AI systems that do not use machine learning models. In these cases, it can be the result of likely unintentional decisions made by practitioners who do not account for the diversity of those using the system. The decision system may reinforce existing beliefs that are biased. For example, a job posting requiring a certain educational level like a bachelor's or master's degree might codify a hiring manager's bias about what makes a good job candidate without seeing whether those factors are relevant to performance. An AI system based on those parameters may then reject good candidates with a less traditional background. In all AI systems, preventing bias must be an important process piece throughout the entire model-building pipeline, design, data collection, model training and testing, decisions about what variables to include and exclude, the algorithm used in the resulting model, and the way in which it is deployed. Ensuring that bias is considered in the whole design process should be a part of the governance framework for the design and implementation of any AI system.

Intermediate Provider : Briana Brownell 4 Lessons · 8m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

These lessons on Bias, Inclusivity, and Fairness in Artificial Intelligence will explore why bias is one of the most discussed concepts involving AI ethics and why preventing bias should be a part of the governance of every AI system. Additionally, these lessons will review the concepts of fairness, representativeness, and the principle of inclusive impact.

AI systems scale harmful unfairness to a massive degree. As a result, preventing bias should be a part of the whole model building pipeline from design to data collection to model training and testing. Representative data is important to overcoming bias in machine learning model building. For data to be representative, it must accurately represent the cases the AI is concerned with. You will learn the ways that representative data can be used to mitigate or eliminate bias in machine learning-based AI systems.

At its essence, fairness means that data subjects should be treated impartially and equitably, regardless of characteristics such as race, gender, age, region, nationality, country of origin, political beliefs, religion, etc. Through these lessons, you’ll also explore the concept of fairness and its relevance to bias, the specific definitions of which have been explored at length in the academic literature.

Finally, you’ll learn about the principle of inclusive impact and how it can be used to empower people and share benefits amongst everyone.

What You'll Learn

Describe the difference between bias and fairness in AI systems
Recognize the risks posed by biased AI systems
Explain why representative data is important to creating fair AI
Apply representative data to mitigate or eliminate bias in machine learning-based AI systems
Understand the principle of inclusive impact and its ability to empower those interacting with AI
Explore fairness, representativeness, and the principle of inclusive impact in AI ethics

Key Takeaways

Bias is one of the most discussed concepts in AI ethics, and preventing it should be part of the governance of every AI system.
Because AI systems scale harmful unfairness to a massive degree, preventing bias should span the whole model-building pipeline from design to data collection to model training and testing.
Representative data accurately represents the cases the AI is concerned with and is important to overcoming bias in machine learning model building.
Fairness means data subjects should be treated impartially and equitably, regardless of characteristics such as race, gender, age, region, nationality, country of origin, political beliefs, or religion.
The principle of inclusive impact can be used to empower people and share benefits amongst everyone.

Frequently Asked Questions

What topics does this course cover?

It covers bias, inclusivity, and fairness in artificial intelligence, including detecting and preventing bias, representative data, fairness in AI, and inclusive impact.

What skills will I gain from this course?

You will build skills in Artificial Intelligence Systems, Artificial Intelligence, Biasing, Computational Intelligence, Data Ethics, and Diversity Equity and Inclusion Policies.

What does fairness mean in the context of AI?

Fairness means that data subjects should be treated impartially and equitably, regardless of characteristics such as race, gender, age, region, nationality, country of origin, political beliefs, or religion.

Why is representative data important?

For data to be representative it must accurately represent the cases the AI is concerned with, and representative data can be used to mitigate or eliminate bias in machine learning-based AI systems.

What is the principle of inclusive impact?

Inclusive impact is a principle that can be used to empower people who interact with AI and to share benefits amongst everyone.