Academic
  • Introduction
  • Artificial Intelligence
    • Introduction
    • AI Concepts, Terminology, and Application Areas
    • AI: Issues, Concerns and Ethical Considerations
  • Biology
    • Scientific Method
    • Chemistry of Life
    • Water, Acids, Bases
    • Properties of carbon
    • Macromolecules
    • Energy and Enzymes
    • Structure of a cell
    • Membranes and transport
    • Cellular respiration
    • Cell Signaling
    • Cell Division
    • Classical and molecular genetics
    • DNA as the genetic material
    • Central dogma
    • Gene regulation
  • Bioinformatics
    • Bioinformatics Overview
  • Deep Learning
    • Neural Networks and Deep Learning
      • Introduction
      • Logistic Regression as a Neural Network
      • Python and Vectorization
      • Shallow Neural Network
      • Deep Neural Network
    • Improving Deep Neural Networks
      • Setting up your Machine Learning Application
      • Regularizing your Neural Network
      • Setting up your Optimization Problem
      • Optimization algorithms
      • Hyperparameter, Batch Normalization, Softmax
    • Structuring Machine Learning Projects
    • Convolutional Neural Networks
      • Introduction
    • Sequence Models
      • Recurrent Neural Networks
      • Natural Language Processing & Word Embeddings
      • Sequence models & Attention mechanism
  • Linear Algebra
    • Vectors and Spaces
      • Vectors
      • Linear combinations and spans
      • Linear dependence and independence
      • Subspaces and the basis for a subspace
      • Vector dot and cross products
      • Matrices for solving systems by elimination
      • Null space and column space
    • Matrix transformations
      • Functions and linear transformations
      • Linear transformation examples
      • Transformations and matrix multiplication
      • Inverse functions and transformations
      • Finding inverses and determinants
      • More Determinant Depth
  • Machine Learning
    • Introduction
    • Linear Regression
      • Model and Cost Function
      • Parameter Learning
      • Multivariate Linear Regression
      • Computing Parameters Analytically
      • Octave
    • Logistic Regression
      • Classification and Representation
      • Logistic Regression Model
    • Regularization
      • Solving the Problem of Overfitting
    • Neural Networks
      • Introduction of Neural Networks
      • Neural Networks - Learning
    • Improve Learning Algorithm
      • Advice for Applying Machine Learning
      • Machine Learning System Design
    • Support Vector Machine
      • Large Margin Classification
      • Kernels
      • SVM in Practice
  • NCKU - Artificial Intelligence
    • Introduction
    • Intelligent Agents
    • Solving Problems by Searching
    • Beyond Classical Search
    • Learning from Examples
  • NCKU - Computer Architecture
    • First Week
  • NCKU - Data Mining
    • Introduction
    • Association Analysis
    • FP-growth
    • Other Association Rules
    • Sequence Pattern
    • Classification
    • Evaluation
    • Clustering
    • Link Analysis
  • NCKU - Machine Learning
    • Probability
    • Inference
    • Bayesian Inference
    • Introduction
  • NCKU - Robotic Navigation and Exploration
    • Kinetic Model & Vehicle Control
    • Motion Planning
    • SLAM Back-end (I)
    • SLAM Back-end (II)
    • Computer Vision / Multi-view Geometry
    • Lie group & Lie algebra
    • SLAM Front-end
  • Python
    • Numpy
    • Pandas
    • Scikit-learn
      • Introduction
      • Statistic Learning
  • Statstics
    • Quantitative Data
    • Modeling Data Distribution
    • Bivariate Numerical Data
    • Probability
    • Random Variables
    • Sampling Distribution
    • Confidence Intervals
    • Significance tests
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On this page
  • Issues and Concerns around AI
  • Ethical Concerns
  • Bias
  • Four key aspects
  • Transparency
  • Accountability
  • Privacy
  • Lack of Bias
  • Jobs and Employment

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  1. Artificial Intelligence

AI: Issues, Concerns and Ethical Considerations

Issues and Concerns around AI

  • Privacy is the number one key challenge of AI.

  • Who would own the new music which is created by an AI algorithm based on the style of great composer.

Ethical Concerns

Ethics is not a technological problem, ethics is a human problem.

Bias

Humans subconsciously apply certain kinds of bias that becomes obvious in large scale data which can then impact Machine Learning algorithm outcomes if those data sets are used for training.

Four key aspects

Transparency

使用者應有得知互動者為 AI 的權力，並知道該次互動所期望得到的結果為何。

Accountability

開發者應建立責任機制於 AI 系統上，在發生非期望的結果是能夠追溯原因。

Privacy

使用者的個人資料應被保護。

Lack of Bias

開發者應使用客觀的 training data 來防止產生 bias，並且在平常時就應該隨時檢查並防範 bias 的產生。

Jobs and Employment

  • Many jobs will be lost to AI, and that the most vulnerable jobs will be those with rules-based, repeatable tasks, like call center workers and drivers.

  • AI will generate new jobs, and new types of work.

  • AI is already being used to benefit humankind in many fields, including healthcare, crime prevention, agriculture, and power generation, among others.

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Last updated 5 years ago

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