AI Project Cycle & Ethical Frameworks

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These flashcards cover key concepts and definitions from the lecture on AI Project Cycles and Ethical Frameworks, designed to aid in exam preparation.

Last updated 9:06 PM on 4/18/26
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20 Terms

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AI Project Cycle

A step-by-step process to solve problems using proven scientific methods.

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Problem Scoping

The process of understanding and defining the problem that the AI project aims to solve.

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Data Acquisition

The process of collecting accurate and reliable data from various sources.

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Data Exploration

The phase of arranging and analyzing the data to understand underlying patterns.

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Modelling

Creating models from data to achieve the project goals.

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Evaluation

Testing the model on new data to assess its performance.

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Deployment

The transition of the developed solution from a testing environment to real-world application.

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Statistical Data

A domain of AI related to collecting and analyzing vast amounts of data.

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Computer Vision (CV)

A domain of AI that enables machines to interpret and analyze visual information.

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Natural Language Processing (NLP)

A branch of AI that focuses on the interaction between computers and humans using natural language.

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Ethical Frameworks

Guidelines that help ensure the choices made in AI do not cause unintended harm.

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Bioethics

An ethical framework used in healthcare that addresses ethical issues related to health and medicine.

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Non-maleficence

The ethical principle of avoiding causing harm.

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Beneficence

The ethical principle of promoting well-being and maximizing positive outcomes.

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Justice

The ethical principle that benefits and burdens should be distributed fairly among all individuals.

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Rights-based Frameworks

Ethical frameworks prioritizing the protection of human rights and dignity.

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Utility-based Frameworks

Evaluates actions based on maximizing overall good and minimizing harm.

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Virtue-based Frameworks

Focuses on the character and intentions of decision-makers in ethical decision-making.

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Data Exploration Purpose

To discover patterns and insights in the data.

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Four Principles of Bioethics

1) Respect for autonomy, 2) Do not harm, 3) Maximize benefit, 4) Justice.