I still want to do a project aiming to enhance market sentiment analysis using Artificial Intelligence (AI) technology to provide stock and investment advice. After the panel discussion, I will change my topic and shift the focus of my discussion to the ethical side of things, below are the key elements that may be involved in the project:
- Data Privacy: Ensuring legal, transparent, and ethical data collection and processing is crucial to protect user’s privacy when gathering market-related data, which may include personal data from sources like social media posts or transaction history.
- Algorithmic Bias: The sentiment analysis model may be influenced by data bias, resulting in unfair recommendations. Ensuring diverse training data and proper data preprocessing is essential to mitigate bias.
- Legal Compliance: Ensuring that the operation of the AI system complies with financial regulations and legal requirements, including securities regulations and data privacy laws. Violating legal regulations can lead to legal liabilities and regulatory issues.
- Responsibility and Accountability: Determining who is responsible for the recommendations made by the AI system and how accountability is traced in case of errors or losses. This relates to investor rights and legal responsibility.
- Risk Disclosure: Clearly disclosing the risks associated with using AI recommendations to investors. Investors need to understand that AI recommendations still carry risks and cannot guarantee investment success.
- Social Impact: AI recommendations can have a significant impact on the market, potentially causing stock price fluctuations. The project team should consider these impacts and take measures to mitigate potential market disruptions.
3 most important challenges or risks
- Limited Data Access: Obtaining data relevant to sentiment analysis in the stock market may be restricted by data providers. Some financial data sources may require subscription or purchase, and other data may be difficult to acquire.
- Ethical Review and Permissions: When studying ethical and moral issues, you need to consider potential ethical review and permissions, especially when dealing with sensitive data or conducting field research.
- Technical Challenges: Handling large-scale data and complex sentiment analysis models may demand advanced technical skills and computational capabilities, presenting a technical challenge for researchers.
Addressing these challenges typically involves careful planning and approaches. This may include seeking alternative data sources, collaboration with data providers, optimizing data quality, utilizing publicly available machine learning libraries, adhering to ethical and compliance standards, and appropriately allocating technical resources. Understanding these challenges, finding solutions, and clearly describing them in my research is essential for ensuring the feasibility and credibility of my study.
