Assignment two

[Yutong2002]

What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)

I completed the tasks including Fixing NULL values with COALESCE; Implementing ROW_NUMBER(), DENSE_RANK(), and windowed COUNT(); Writing CASE, SUBSTR, INSTR, and REGEXP queries; Creating CTEs and using UNION to identify best/worst sales days; Performing CROSS JOIN calculations; Creating a new table, inserting sample data, and applying DELETE and UPDATE logic.

What did you learn from the changes you have made?

Learned how window functions work, how to handle NULLs with COALESCE, how to extract substrings using INSTR + SUBSTR, and how to use CTEs and correlated subqueries to update tables safely.

Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?

N/A

Were there any challenges? If so, what issue(s) did you face? How did you overcome it?

Main challenge was identifying the most recent inventory record per product. Solved it using ROW_NUMBER() ordered by inventory date.

How were these changes tested?

Executed query in the SQL environment and verified correct outputs

A reference to a related issue in your repository (if applicable)

N/A

Checklist

• I can confirm that my changes are working as intended

[Yutong2002]

Section 1_Logic_Table

[Yutong2002]

Section_4:

The essay reveals that AI systems are deeply human in origin, structure, and labour. The ethics of neural networks are therefore the ethics of:

1. Human workers behind the data, for instance, the date labelers, content moderators, annotation contractors etc. The performance of neural networks reflects the labour of these workers. Recognizing this labour is essential for ethical AI development;
2. The societal biases embedded in annotations. The neural nets always mirrors human judgments (including flawed ones), and Cultural differences affect labels (e.g., political opinions, moral norms). Thus, bias is not an accident, it is baked into the system through human labour and societal structures ;
3. Corporate power shaping AI deployment. Only a few corporations (OpenAI, Google, Meta) have the compute and data to train frontier models, which may create centralized control over knowledge production, limited transparency and asymmetric power dynamics;
4. Global inequalities defining who benefits and who pays the cost. Boykis highlights two extremes: High-salary engineers in Silicon Valley and Low-wage annotators in developing countries. This mirrored structure resembles other extractive global industries. This imbalance questions who benefits from AI and at what cost;
5. Transparency around how models are built and maintained. When LLMs enforce one version of “truth,” they can unintentionally suppress diverse viewpoints.

Boykis’s key opinion is that AI is not alien intelligence. it is indeed a mirror of the people who build it. Ethical AI must acknowledge the human labour, subjectivity, and societal structures that shape its outputs, rather than pretending the system is neutral or autonomous.

[khsergvl]

Thank you for submitting assignment 2.
The best practice is to isolate changes related to assignment within Pull request.
Your current Pull request shows 35 files modified, instead of expected 3-4.
Mark: 70/70