Glossary of Ethical AI Terms

Algorithmic Bias

Definition: Systematic and repeatable errors in a computer system that create unfair outcomes, such as giving preferential treatment to one group over another. This bias often comes from the data used to train the AI.

Significance: Algorithmic bias can perpetuate and amplify existing societal inequalities, leading to discriminatory outcomes in areas like hiring, lending, and even education.

Example: An AI essay grading system trained mainly on essays from students with privileged backgrounds might unfairly penalize essays from students with less privileged backgrounds due to differences in writing style or access to resources.

Accountability in AI

Definition: Establishing responsibility for the development, deployment, and consequences of AI systems. It means having mechanisms in place to ensure that AI operates ethically and can be held responsible when things go wrong.

Significance: As AI becomes more powerful, it's crucial to have accountability measures. This includes identifying who is responsible for the data used, the design of the algorithms, and the impact of AI systems on individuals and society.

Example: If a self-driving car causes an accident, accountability involves determining who is responsible – the car manufacturer, the software developer, or the user.

Bias Detection and Mitigation

Definition: The process of identifying and reducing bias in AI algorithms and the data they use. It involves techniques and strategies to ensure fairer and more equitable outcomes.

Significance: Since bias can lead to unfair or discriminatory results, actively working to detect and mitigate bias is a critical step in developing ethical AI.

Example: Regularly testing AI systems used for hiring for biases against certain demographic groups and making adjustments to the algorithms or training data to reduce these biases.

Data Security in AI

Definition: The measures taken to protect the data used by AI systems from unauthorized access, theft, damage, or misuse. It involves implementing safeguards to ensure the integrity and confidentiality of data.

Significance: Secure data is essential for maintaining privacy and the reliability of AI systems. Breaches in data security can have serious consequences for individuals and organizations.

Example: Implementing strong encryption methods to protect student data stored in AI learning platforms.

Ethical AI Framework

Definition: A set of principles, guidelines, and standards that aim to guide the development and deployment of AI in a way that is morally sound and beneficial to society.

Significance: Ethical AI frameworks provide a roadmap for creating and using AI responsibly, addressing concerns related to bias, fairness, transparency, and accountability.

Example: The ETHICAL Principles AI Framework for Higher Education provides guidelines for the responsible use of AI in academic settings, emphasizing transparency, a human-centered approach, and accessibility.

Fairness in AI

Definition: The concept of ensuring that AI systems do not discriminate against individuals or groups based on characteristics like race, gender, or socioeconomic status. It aims for AI to treat all people equitably.

Significance: Fairness is a core ethical principle in AI. Biased AI can lead to unfair decisions and perpetuate societal harms. Striving for fairness in AI development and deployment is essential for building trustworthy and just technologies.

Example: AI used in college admissions should not unfairly favor or exclude applicants based on their race or ethnicity.

Human Oversight in AI

Definition: The involvement of humans in monitoring, evaluating, and potentially intervening in the decisions and actions of AI systems. It ensures that AI remains under human control and ethical considerations are maintained.

Significance: While AI can automate many tasks, human oversight is crucial for preventing errors, biases, and unintended consequences. It allows for human judgment and intervention when necessary.

Example: In AI-assisted medical diagnosis, doctors should always review and confirm the AI's findings before making treatment decisions.

Inclusive Design in AI

Definition: Designing AI systems with consideration for the diverse needs and abilities of all potential users, including those from marginalized groups and individuals with disabilities.

Significance: Inclusive design aims to prevent AI from creating or exacerbating existing inequalities. By considering a wide range of users, developers can create AI that is more equitable and accessible.

Example: Designing AI learning tools with features like screen reader compatibility and speech-to-text functionality to ensure accessibility for students with disabilities.

Privacy in AI

Definition: Protecting individuals' personal information when it is collected, used, and stored by AI systems. It involves ensuring that data is handled securely and ethically.

Significance: AI systems often rely on large amounts of data, which can include sensitive personal information. Protecting this data from unauthorized access, misuse, or breaches is a fundamental ethical concern.

Example: AI tools used in education collect student data. Ensuring this data is stored securely and used only for educational purposes, with informed consent, is crucial for protecting student privacy.

Transparency in AI

Definition: The ability to understand how an AI system works, including the data it uses and the decisions it makes. A transparent AI system allows people to see and comprehend its inner workings.

Significance: Transparency is vital for building trust in AI. If we don't understand how AI makes decisions, it's difficult to identify and correct potential biases or errors. Transparency also allows for accountability.

Example: Clearly communicating when and how AI is being used in a course, such as for grading or providing feedback, helps students understand the role of AI in their learning.