Gender Bias in Artificial Intelligence
While listening to some talks from the BigData Europe 2022 conference, I stumbled upon a session by Shalvi Mahajan where she discussed Gender Bias in Artificial Intelligence. I had heard about biases in hiring software but it was only recently when I researched it in-depth.
Artificial Intelligence is meant to be for all. It’s supposed to be neutral, inclusive, and fair. However, you would be surprised to find out that this doesn’t always hold.
Examples of Gender Bias in AI
- Occupational Stereotyping: AI algorithms sometimes reinforce gender stereotypes by linking certain jobs more with one gender than the other. For example, in some translation tools, when translating to languages that use gender-specific terms, “doctor” is often assumed to be male, while “nurse” is assumed to be female. This reflects societal biases.
- Credit Scoring Disparities: AI systems used in finance can show gender bias in credit scoring, leading to unfair treatment based on gender. For instance, women might receive lower credit limits compared to men with exactly similar credit histories.
- Facial Recognition: The Gender Shades research project highlighted that certain commercial facial recognition tools were built on top of datasets that lacked diversity. These tools frequently misclassified images of women, particularly those with darker skin tones, at a staggering error rate of 35%, compared to just 0.8% for lighter-skinned men.
- Voice Recognition: Some voice recognition systems struggle to understand female voices as accurately as male voices.
Understanding Bias Origins
Data Collection
AI algorithms need lots of data to learn and make predictions. Data can be considered a snapshot of the real world. Our smartphones are the biggest data collectors today. However, around 327 million fewer women than men have smartphone and internet access today. In fact, in developing countries, women are 20% less likely to own a smartphone. This depicts how biased the collected data can be.
Algorithm Design Bias
Data is collected, cleaned, labeled, and processed by humans, mostly men in AI and data science fields. According to consensus, the percentage of women within data-science roles is under 22%.
Humans decide how to set up AI algorithms. They have the power to decide which factors should or should not contribute to the predictions. They can even decide the weightage of certain factors. These decisions can embed biases further into the system. For instance, if people designing the algorithm have certain stereotypical beliefs or assumptions, those can affect how the AI behaves.
Impact of Gender Bias
Bias in AI not only undermines the quality of service in tools like voice recognition but also perpetuates harmful stereotypes. By reinforcing societal inequalities, these biases hinder progress towards gender equality and inclusivity.
Strategies to Address Bias
- Diversify Datasets: Ensure representation from diverse demographic groups in datasets used for training AI algorithms.
- Integrate Ethical Considerations: Incorporate ethical considerations into the design and development of AI algorithms to promote fairness and transparency.
- Promote Diversity within AI Teams: Foster diversity to bring varied perspectives to ensure more inclusive decision-making. For example, humans labelling data should come from diverse backgrounds.
Eliminating gender bias in AI is a long road but it for sure offers opportunities for positive change. We can start by raising awareness, advocating for diversity and inclusivity, and implementing proactive measures to address biases. Collective efforts can lead to a future where AI systems promote fairness and equality for all individuals, regardless of gender or background.
