Imagine this. Anjali, a 27-year-old marketing professional in Delhi, scrolls through a job portal on her phone. She notices that her male colleagues keep getting ads for high-paying roles in tech and finance, while she’s shown openings in customer service or HR support. She doesn’t know it yet, but the algorithm has quietly pigeon-holed her. In another instance, Rani, a mother in Bihar applies for government benefits online. A glitch in the facial-recognition system rejects her photo twice. The error isn’t her fault, but the rejection leaves her shaken. Both women have been sidelined by a machine that is supposed to be neutral.
Algorithmic and AI bias is not only about skewed data or unfair code. It’s about the psychological toll on women who must navigate systems that sometimes don’t ‘see’ them properly. In India, now that digital platforms are fast becoming the gateways to jobs, loans, healthcare, and government services, the stakes couldn’t be higher.
What Is AI Bias?
In simple terms, AI bias occurs when a system that’s supposed to be objective ends up being unfair. Perhaps because the data it was trained on didn’t include enough women, or because the engineers who built it never thought about rural voices, darker skin tones, or regional dialects. In India, AI is already woven into everyday life, It is used by job-matching sites to filter resumes. Banks use algorithms to score loan eligibility and health apps use it to predict risks. Aadhaar-linked systems verify identity for welfare schemes.
The Psychology Of Being Filtered Out
When women sense that a system doesn’t expect them to succeed, their performance and confidence can dip. It’s like carrying an invisible backpack full of doubt. Studies show people often trust computer outputs more than human judgement, even when the computer is wrong. So, if an app tells you you’re less eligible, it can hit harder than a rejection letter from a human recruiter. Small failures such as being locked out of apps, denied access, or shown irrelevant ads can make people feel powerless. Over time, women may stop applying, stop exploring, or shrink their aspirations. In India, these effects are magnified by other realities like gender gaps in digital literacy, cultural norms that already discourage women from pushing too hard, and limited social safety nets.
The Indian Context
AI bias against women is not uniquely Indian, but here, it collides with specific realities. A GSMA study found Indian women are significantly less likely than men to own smartphones or access mobile internet. If fewer women are online, algorithms are literally learning less about them. Also, if women are historically shown in certain job roles like teachers or nurses, algorithms trained on past data may keep reinforcing that pattern. There is also the problem of intersectionality. A Dalit woman in a rural area may face more layers of exclusion than an upper-caste, urban, English-speaking woman. Algorithms don’t always see those nuances. Many Indians are raised to trust systems, so questioning fairness doesn’t always come naturally.
The Mental Safeguarding Toolkit
Think of this as a set of tools you can carry, so that even if the system isn’t perfect, you don’t lose your balance.
1. Build Digital Self-Awareness
When something goes wrong online, women often blame themselves. If the instinct to do that arises, check the system, not just yourself. Algorithms fail too. If you’re locked out or mis-scored, remind yourself that this might be bias or error, not your fault. Document issues by keeping screenshots or notes. This is evidence if you need to challenge a decision later. Talk it out. Sharing with friends or peers can reveal that others are facing the same glitches and bring down feelings of isolation. Algorithms can feel like magic. But remember, they’re just probability engines. Notice patterns. If you’re repeatedly shown lower-paying ads or irrelevant jobs, it may not reflect your capability. Learn the basics. Even a short online course on how algorithms work can make you feel more in control.
2. Resist Internalising AI Bias
The biggest mental trap is believing biased outcomes reflect your worth. Research on stereotype threat shows that self-affirmation reduces the psychological impact of discrimination. This includes reminding yourself of your values, skills, and achievements. Keep a ‘strengths journal’ or a small notebook where you jot down accomplishments, compliments, or progress. When an algorithm misjudges you, your own record becomes a counterweight.
3. Use Community As A Shield
Online and offline women’s groups, WhatsApp communities, or workplace circles can act as mental armour, when women swap stories of algorithmic glitches. Knowing you’re not alone is powerful. Find a mentorship network. Experienced women can help younger ones spot systemic bias and not take it personally.
4. Mindfulness And Emotional Regulation
This isn’t about ignoring injustice. It’s about not letting it consume you. Simple practices like mindfulness, meditation, or journalling help manage frustration when the system feels stacked. If an app rejects you today, you’ll still have the emotional steadiness to try again tomorrow.
5. Learn To Challenge Respectfully
In India, pushing back against the system can feel intimidating. But challenge doesn’t always mean confrontation. Be persistent in using proper grievance channels in apps and government services. Politely escalate issues to helplines or social media. Companies often respond faster when problems are public. The key is, don’t assume the system is always right.
6. Guard Your Mental Energy Online
Bias is tiring. Scrolling job ads that don’t fit, re-uploading ID documents, and fighting glitches often drains you. Protecting mental energy is as important as protecting privacy. Set time boundaries for stressful apps. Curate your feeds to include positive, empowering content and unplug consciously when you feel trapped in digital loops.
What Needs to Change
It’s unfair to put all the responsibility on women’s shoulders. Mental safeguards are important, but the real fix is systemic. We need better data practices to represent women, along with bias audits and regulation. The system needs to be designed for inclusivity from the start. Until these changes fully arrive, mental safeguarding is a survival skill.