AI Ethics

With its high degree of autonomy and learning capability, artificial intelligence can produce outcomes that exceed the range of human prediction and control, so ethical and legal consideration of accountability for those outcomes and of their controllability is strongly required.

In particular, when artificial intelligence performs judgment functions such as evaluation, recommendation, classification, and screening, it can affect the rights and opportunities of stakeholders.

Accordingly, the key is to build a structure of human control, accountable parties, and safeguards from the design stage onward.

AI ethics is not a matter of ‘discussing possibilities’ but something whose importance has been confirmed repeatedly through events that have already occurred.

Accidents in safety-critical areas such as autonomous driving, losses caused by automated decisions in finance and investment, and discriminatory outcomes resulting from data bias show that, when a company neglects ethical principles, the result can be disputes over liability and a collapse of trust.

AI ethics issues tend to intensify and expand as an extension of existing internet ethics concerns, such as personal data leaks, invasion of privacy, and gaps in information access.

Accordingly, if responses remain after-the-fact as in the past, the scale of harm can only grow, and ethics governance centered on advance review and prevention is required.

Discussion of AI ethics is closely connected to the ‘standardization of ethics,’ including the establishment of ethics guidelines, and as global standards take shape, the level of ethics governance a company has in place can affect its market trust and contractual competitiveness.

The top value of AI ethics is Humanity, and the basic principles presented to realize it are the principle of human dignity, the principle of the public good of society, and the principle of the suitability of technology to its purpose.

In corporate practice, these principles should take concrete form as the prevention of safety accidents and human rights violations, the assurance of accessibility for the socially disadvantaged and vulnerable groups, and control over the legitimacy of purpose and the scope of use.

It is also important, on the premise that conflicts among principles may arise, to reach trade-offs suited to the service context and to document them.

The ten core requirements consist of protection of human rights, protection of privacy, respect for diversity, prohibition of infringement, publicness, solidarity, data management, accountability, safety, and transparency.

Companies should shift these into operational standards from the perspective of “through what controls are they implemented”.

For example, respect for diversity should connect to checks on data representativeness, bias testing, accessibility design, and policies that minimize discrimination.

Transparency, by contrast, should be made effective through user notification, a defined level of explainability, advance notice of risks and cautions, and the operation of inquiry and objection channels.

Accountability, in turn, should be realized through the definition of roles and authority, the allocation of responsibility when external solutions are used, and response protocols and reporting systems in the event of an accident.

When a lack of representativeness in the training data or assumptions in the design produces outcomes that disadvantage a particular group, the matter can expand into a discrimination issue.

In particular, in sensitive areas such as hiring, evaluation, lending, insurance, and medical care, there is a high likelihood that the legitimacy of and an explanation for the outcome will be demanded, so preliminary verification and the establishment of standards are needed to respond to disputes.

If personal information is collected, combined, or used during AI development and operation, or if it is used beyond the intended purpose, the risks of regulatory violations and damages may follow.

Accordingly, data management controls such as data minimization, access permission control, de-identification, and retention and destruction standards must be designed together with ethical requirements.

Malfunctions, hallucinations, errors, security vulnerabilities, and overconfidence in automated decision-making can lead to incidents.

When an incident occurs, a failure to explain “why that outcome arose and what controls were in place” may affect the assessment of the company’s fault and its credibility.

For this reason, systems for performance monitoring, risk assessment, human oversight, emergency shutdown, and logging and documentation are needed.

A company should first organize where AI is used in its services and internal operations, then identify bias, privacy, safety, and transparency issues based on data flows and automated decision-making points to set priorities.

In particular, it is efficient to distinguish customer-facing services from internal decision-making systems and to apply differing levels of risk and intensity of control.

Daeryun’s Areas of Assistance

To convert ethical principles and core requirements into internal regulations and processes, a company should organize its ethics guidelines, data management standards, model operation standards, notice and explanation policies, objection and remedy procedures, and incident response manuals.

In addition, when using external solutions and APIs, the allocation of responsibility and the scope of verification should be made clear through contracts and operating procedures.

Daeryun’s Areas of Assistance

At the operation phase, effectiveness should be maintained through monitoring of bias and performance degradation, safety inspections, security controls, retention of logs and decision-making records, and regularized training.

In particular, by repeating impact assessments and improvement measures whenever a new service is introduced or a normative change occurs, ethics governance should be advanced into a sustainable form.

Daeryun’s Areas of Assistance

Daeryun Law Firm operates an AI and Data Intelligence Group, and it supports companies in implementing AI ethics not as a ‘declaration’ but as ‘verifiable compliance.’

By organically linking its capabilities in AI compliance review, the establishment of industry-specific strategies, cybersecurity incident response, and digital forensic analysis, it provides a seamless support system spanning from the diagnosis of ethics risks to incident response.

Specifically, it provides practical advisory that diagnoses the bias, privacy, safety, and transparency issues that may arise throughout the entire AI use process in line with the service structure and data flows, and converts them into internal regulations and operational controls.

In addition, by linking its cybersecurity, crisis response, and digital forensic capabilities, it supports the organization of the facts when an incident occurs, the building of an evidentiary structure, and response documentation, thereby assisting companies in converting ethics risks into a manageable area of control.

If you find yourself in a situation that requires related review and practical advisory, please feel free to request assistance from a corporate attorney at any time.