It addresses key issues in the AI ​​era, including bias in data utilization, privacy, copyright, and deepfakes.
Beyond technological advancement, it presents ethical standards to maintain social trust and fairness.
Artificial Intelligence Encyclopedia.
You can find the artificial intelligence knowledge you need at aiseries.oopy.io.

Data is everything that exists on Earth.
Even if it is a written text, image, voice, or video, it exists as data.
The moment it is digitized, anyone can easily use it for any purpose.
The properties of data vary, including copyright, personal information, trade secrets, and public domain information (facts).
Because the properties are so diverse, the interests are complex and the related laws and protection systems are different.
Representative related laws include the Copyright Act for works, the Personal Information Protection Act for personal information, and the Trade Secret Protection Act for trade secrets.
In addition, various laws are relevant to the data industry as a whole, such as the Framework Act on Promotion of Data Industry and Utilization (hereinafter referred to as the Data Industry Act), and the Public Data Act for data in the public sector.
Despite the existence of various laws, the ethical framework of data cannot be ignored, as it is difficult to properly verify the entire process of data collection, processing, and use.
If data is used without such consideration, future disputes will be self-evident.
Data ethics can be viewed as a system for responding to ethical issues in the process of collecting, processing, and using data, including various problems that may arise from using data.
--- From "01_“Data Ethics”"

Because securing data is not easy unless you are a platform operator or have capital, solving the problem of data monopoly requires a common effort from all of humanity.
Above all, due to the monopolization of platform operators, businesses that utilize data based on user data are seeking data monopoly rather than data sovereignty.
Therefore, it is necessary to consider legislative theory to design a stable and predictable system.
Because algorithmic problems are occurring and can be predicted to occur continuously and repeatedly, regulation is necessary.
--- From "03_“Data Bias”"

Artificial intelligence is called a black box.
Because no one can understand the process, and can only infer the results.
This is the main reason why the transparency of artificial intelligence is emphasized.
If the algorithm is distorted or biased, it is difficult to ensure the fairness of the results.
For this reason, in addition to policies and legal systems to control artificial intelligence, various technological techniques are being proposed.
Disclosure of algorithm sources or introduction of auditing techniques are also being discussed as options.
Since artificial intelligence, as a black box, cannot explain its processes, explainable artificial intelligence (XAI, eXplainable AI) is sometimes proposed as a new solution.
--- From “06_“Profiling and Transparency””

Data ethics, which arose due to the problem of data distortion, originated from the intention to prevent acts of intentionally distorting data during the process of utilizing it.
While it is natural to utilize data in the machine learning process, if intentionality is reflected, the resulting social benefits and discrimination will be reflected in the machine, and users of services utilizing such machines will face a situation where they unconsciously embody discrimination or become the parties or subjects of discrimination.
For example, depending on the nature of the service, intentional discrimination such as economic discrimination, socio-cultural discrimination, educational discrimination, and group stratification may become widespread.
A solution to overcome these biases is the discussion on AI ethics and efforts to ensure fairness in the collection, analysis, processing, and use of data.
Additionally, discussions are needed to ensure the fairness of the algorithms in the learning process using these or in the process of utilizing the learned data.

--- From “09_“Data Ethics Combined with Algorithms””