

How large language models map risks, benefits, and options for girls and women addressing non-sexual harassment, and support safer, informed help-seeking.

This is Article No.3a under the Human-Tech Partnership Series
Costs and Benefits Before Girls and Women Addressing Non-sexual Harassment: LLMs Provide a Realistic Assessment explores how large language models can become practical allies for girls and women facing non-sexual harassment by clarifying the risks and benefits of seeking help and offering self-empowering strategies. It juxtaposes two AI-generated strands: a Copilot-led risk–benefit analysis and historical examples, and a ChatGPT-led toolkit that focuses on resilience, safety, and informed decision-making in real-world contexts
The previous article in the Human-Tech Partnership Series (click here to read the previous article) invited LLMs to characterize nonsexual harassment of girls and women. The significant challenge posed to policy makers, in this regard, requires contributions at the individual, societal and institutional levels. However, it appears that a new and arguably equal partner in the effort to address non-sexual harassment may be emerging in the form of LLMs.
LLMs have been conceived of as a repository of the collective information and shared history of humankind. LLMs could, additionally, help users become more aware of biases, such as the Confirmation Bias, drawing attention to evidence that is not consistent with the user’s hypothesis, or to approaches that reinterpret contradictory evidence, thereby forcing an alignment with their hypothesis. A selective search for data that bolster a particular belief and a refusal to incorporate data that refute it, are tendencies that can be offset by the active participation of LLMs which can provide well-rounded, multifaceted points of view.
While various members of society work tirelessly to create usable policy recommendations and assistive programs towards mitigation of non-sexual harassment of girls and women, LLMs could help increase chances of successful implementation, providing both practical assistance as well as a sensitive reading of the externalities associated with the addressal of non-sexual harassment.
In the following prompted articles Microsoft’s Copilot delineates the risks and benefits associated with the addressing of non-sexual harassment of girls and women, while ChatGPT puts together a toolkit for those experiencing non-sexual harassment, emphasising an approach oriented towards self-empowerment.
How might the ChatGPT Toolkit incorporate Copilot’s risk-benefit analysis to refine its solutions?
How might Copilot and ChatGPT together support girls and women in their effort to remain safe and lead normal lives in the face of non-sexual harassment?
Seeking help for non-sexual harassment offers both empowerment and protection, but it also carries risks such as retaliation, stigma, or institutional inaction. Academic research highlights these trade-offs, and real-world cases illustrate both the dangers and the potential gains.
Academic literature underscores that help-seeking is a double-edged sword: while it can empower women, improve mental health, and drive systemic change, it also exposes them to retaliation, stigma, and institutional shortcomings. The balance between risk and benefit depends heavily on organizational culture, legal frameworks, and the presence of supportive networks. Strengthening institutional responses and reducing stigma are critical to ensuring that women can seek help without fear.
Sources: journals.plos.org, BMJ Public Health, ijmrtjournal.com
Women historically faced both serious risks and meaningful benefits when seeking help for non-sexual harassment. Two documented risks include retaliation and marginalization, while two benefits include institutional reform and empowerment through collective action.
| Category | Example | Outcome |
|---|---|---|
| Risk | Civil Rights Movement retaliation | Women sidelined, gender concerns ignored Library of Congress |
| Risk | Workplace marginalization | Victims labeled “troublemakers,” careers stalled knowledge.unwomen.org |
| Benefit | Indian legal reforms | Stronger constitutional protections, grievance mechanisms Centre for Law & Policy Research |
| Benefit | Grassroots | Collective support, economic and social empowerment in Kerala IJRAR |
From a historical perspective, risks often involved retaliation and stigma, while benefits included systemic reforms and collective empowerment. These examples show that helpseeking has always been a double-edged sword: it could expose women to further harm, but it also laid the groundwork for broader social and legal change.
Sources: PLOS One review journals.plos.org, UN Women handbook knowledge.unwomen.org, CLPR report on Indian court decisions Centre for Law & Policy Research, IJRAR study on empowerment in Indian history IJRAR, Library of Congress Civil Rights History Project Library of Congress.
Dr Monika Krishan is a Senior Fellow (Cognitive Science and Artificial Intelligence) at the Centre for Public Policy Research (CPPR), Kochi, Kerala, India.
Views expressed by the authors are personal and need not reflect or represent the views of the Centre for Public Policy Research (CPPR).
Dr Monika Krishan's academic background includes a Master’s in Electrical Engineering from the Indian Institute of Science, Bangalore, India and a Ph.D. in Cognitive Psychology from Rutgers University, New Jersey, USA. Her research interests include image processing, psychovisual perception of textures, perception of animacy, goal based inference, perception of uncertainty and invariance detection in visual and non-visual domains.
Dr Krishan's areas of study also include the impact of artificial intelligence devices on human cognition from the developmental stages of the human brain, through adulthood, all the way through the aging process, and the resulting impact on the socio-cognitive health of society. She has worked on several projects on the cognitive aspects of the use and misuse of technology in social and antisocial contexts at SERC, IISc as well as the development of interactive graphics for Magnetic Resonance Imaging systems at Siemens.