Ethical Frameworks for AI and AI Project Cycle Review

These vocabulary flashcards cover the primary concepts of the AI Project Cycle, AI domains (Statistical Data, CV, NLP), and the various types and principles of Ethical Frameworks, including Bioethics.

AI Project Cycle

The cyclical process followed to complete an AI project, consisting of 6 stages.

Problem Scoping

The first stage of the AI Project Cycle where the goal is set and the problem to be solved is clearly defined by looking at various parameters.

Data Acquisition

The second stage of the AI Project Cycle which involves collecting data from reliable and authentic sources to understand related parameters.

Data Exploration

The stage where collected data is given visual representations like graphs, databases, flow charts, or maps to interpret patterns.

Modelling

The stage where a user researches, selects, and tests appropriate models to find the most efficient one to serve as the base for the algorithm.

Evaluation

The stage where the model is tested on newly fetched data to interpret results, assess performance, and make improvements.

Deployment

The final stage of the AI Project Cycle ensuring the successful integration and operation of AI solutions in real-world environments.

Statistical Data (Domain)

A domain of AI related to data systems and processes where the system collects numerous data, maintains data sets, and derives meaning to make decisions.

Computer Vision (CV)

A domain of AI depicting the capability of a machine to get and analyse visual information, such as photographs or videos, and predict decisions about it.

Natural Language Processing (NLP)

A branch of AI that deals with the interaction between computers and humans using natural language (spoken and written words) to extract valuable information.

Frameworks

A set of steps and a structured approach that provides a step-by-step guide for solving problems in an organized manner.

Ethical Frameworks

Step-by-step guidance used to ensure that choices made during AI development do not cause unintended harm and align with moral values.

Sector-based Frameworks

Ethical frameworks tailored to specific industries, such as Bioethics for healthcare or frameworks for finance, education, and transportation.

Value-based Frameworks

Frameworks focusing on fundamental ethical principles and moral philosophies that inform ethical reasoning and assess the moral worth of actions.

Rights-based Framework

A value-based framework that prioritizes the protection of human rights, dignity, individual autonomy, and freedoms over other considerations.

Utility-based Framework

A framework that evaluates actions based on maximizing overall good or benefit for the greatest number of people while minimizing harm.

Virtue-based Framework

A framework focusing on whether the character and intentions of individuals or organizations align with principles like honesty, compassion, and integrity.

Bioethics

A sector-based ethical framework used in healthcare and life sciences to address ethical issues related to medicine, patient privacy, and biological sciences.

Non-maleficence

The ethical principle of avoiding causing harm or negative consequences, emphasizing the obligation to minimize harm to individuals and the environment.

Maleficence

The concept of intentionally causing harm or wrongdoing.

Beneficence

The ethical principle of promoting and maximizing the well-being and welfare of individuals and society to produce positive outcomes.

Respect for Autonomy

A principle of bioethics that involves respecting the self-governing authority and individual choices of stakeholders.

Justice

A primary principle of bioethics alongside autonomy, non-maleficence, and beneficence, ensuring fairness in the application of AI.