Who does the housework? by Jini Kim

What I observed that I think could be harmful:
In these images, there is an unbalanced representation of men and women, with women predominantly shown doing household chores.

Why I think this could be harmful, and to whom:
The predominance of women depicted as responsible for housework in images can have harmful effects on various levels:

  1. Children exposed to these images might grow up with skewed perceptions of gender roles, believing that certain tasks are inherently meant for one gender, which can limit their aspirations and understanding of equality from a young age.

  2. Gender Stereotypes: This portrayal reinforces the traditional belief that domestic chores are primarily a woman’s duty, perpetuating gender stereotypes and limiting the roles that women and men can play in both the domestic sphere and the workplace.

  3. Impact on Women: It can contribute to societal expectations that women should prioritize domestic responsibilities, which can limit their opportunities for professional advancement and personal development, potentially affecting their self-esteem and mental health.

  4. Influence on Men: Such imagery can also perpetuate the notion that men should not engage in housework, leading to an unequal distribution of domestic responsibilities and possibly straining family dynamics and relationships.

Overall, the biased depiction of women predominantly doing housework can have deep-rooted and widespread effects, contributing to gender inequality and affecting individuals’ roles and opportunities in society.

How I think this issue could potentially be fixed:
Firstly, we can do diversity-inclusive data collection. I think It is important to collect data that includes diverse images of various genders for different occupations when gathering images or information. Second, we can improve algorithm. We can minimize bias by considering improvements to algorithms responsible for image generation. Third, we can do intentional adjustment on the output. If gender bias is detected in the generated output, it is important to intentionally adjust the output to better reflect diversity. Lastly, we can make it more transparent. We can provide some interfaces to show how the result comes out. By describing the operation of algorithms and disclosing them transparently, users should understand how the output was generated.

Note, this audit report is relevant to poster’s own identity and/or people and communities the poster care about.