Al Jazeera's report suggests that AI is increasingly involved in workplace decision-making. However, let's not pop the champagne just yet. It's time for a cold, hard look at the facts. The claim that AI can become the 'new boss' is still an unproven hypothesis, and hasty judgments from an investment perspective can lead to failure.
AI's decision-making role ultimately means making judgments based on data. The problem is how 'clean' that data is. If the data is biased, AI's decisions will inevitably be biased as well. For example, AI trained on data that historically favored men in promotions may discriminate against female applicants. This is not just an ethical issue, but a serious problem that can lead to reputational damage and legal liabilities for companies.
Furthermore, the responsibility for decisions made by AI remains unclear. Who should be held accountable when AI makes a wrong decision? The developer who designed the AI? The management who introduced the AI? Or the AI itself? Introducing AI decision-making systems without clear regulations and accountability is a risky gamble.
Of course, it is undeniable that AI can contribute to improving work efficiency and reducing costs. However, behind this lies the shadow of changes in the labor market. As AI replaces simple and repetitive tasks, many people may lose their jobs. This can cause anxiety throughout society and create new social problems. The introduction of LLM-based automation systems may threaten a significant portion of office workers.
Looking at the competitive landscape, it is difficult to find companies that have fully adopted AI decision-making systems. Most companies use AI as a supplementary tool, and the final decision is still left to humans. This may be due to limitations of AI technology and concerns about ethical issues.
Looking at past cases of automation system adoption, a similar technology trend, rosy predictions were made at the beginning of the technology's introduction, but in reality, many unexpected side effects occurred. Examples include opposition from workers who lost their jobs due to the introduction of automation systems, production disruptions due to system errors, and security issues. The possibility that AI decision-making systems will follow in these footsteps cannot be ruled out.
In conclusion, the impact of AI on decision-making in the workplace is still highly uncertain. A cautious observation and data analysis is needed rather than a hasty investment. It is a priority to analyze the changes that AI will bring in a calm manner and to clearly define ethical issues and accountability. Now is not the time to jump in smelling the money, but to carefully examine whether there are any traps to escape through the back door. The establishment of a data verification system using RAG technology should take precedence.