women and non-binary individuals (Shrestha and Das, 2022). In criminal justice, biased AI affects female prisoners unfairly, with biased algorithms skewing recidivism predictions (Karimi-Haghighi and Castillo, 2021).

More relevant to our analysis are the implications of gender bias in AI to democracy, for which the verdict is still out. Advancements in AI can enhance the government's understanding of public needs and citizen participation in policy making. AI can analyze large datasets to detect corruption, boosting accountability and trust. Automated tools for summarizing public feedback can improve officials' ability to process inputs, while grievance systems enhance transparency and provide data for AI models to predict policy impacts (Rahim, Mahony, and Bandyopadhyay, 2024).

AI enhances government communication, particularly in multilingual settings. In India, the Jugalbandi platform uses AI chatbots to deliver services in multiple languages across 171 government programs, while the Bangsamoro region in the Philippines employs AI to analyze social media for development insights (Rahim, Mahony, and Bandyopadhyay, 2024). AI platforms also improve marginalized communities' access to essential services, promoting inclusivity. For example, Chile's national human rights action plan incorporated diverse perspectives from indigenous peoples and incarcerated individuals, using large language models to analyze their input and ensure fair representation (Fajardo-Hayward and Cuesta, 2024).

<mark>However, the benefits of AI in citizen engagement depend on the accuracy and fairness of the systems to avoid reinforcing sociohistorical inequalities and marginalizing specific communities. Ensuring AI systems provide accurate, accessible information requires rigorous testing, highquality data, and human oversight. For example, AI bots may respond differently based on a user's location or struggle with various accents, disadvantaging marginalized communities (Panditharatne, Weiner, and Kriner, 2023). Such biases have led to issues like increased surveillance of marginalized groups for immigration decisions (Eubanks, 2018) and manipulation of public comments on net neutrality rules (Panditharatne, Weiner, and Kriner, 2023). Studies have shown similar rates of bias detection in AI-generated content compared to human responses, risking neglect of genuine needs (Kreps and Kriner, 2023).</mark>

# 2.4. **_Detection and mitigation of gender bias_**

Detecting biases in AI requires systematic objective methods. Biases are typically identified by comparing AI outcomes with human-coded benchmark datasets. For instance, Booth et al. (2021) use psychometrics to assess gender bias in video recruitment. Census data is often used to evaluate income prediction biases (Feldman and Peake, 2021). More advanced methods include Winograd schemas, which involve ambiguous sentence pairs to test AI's language comprehension and contextual understanding, a method typically used in sentiment analysis (Sarraf et al., 2021). Beyond those methods, fairness metrics are also used for detecting AI bias. Fairness through unawareness ensures models ignore attributes like gender in predictions, while counterfactual fairness checks if altering someone’s gender affects outcomes. Equal opportunity fairness compares true positive rates across groups for equity, and average odds fairness assesses equal rates of true and false positives. Disparate impact and statistical parity fairness focus on 5