Cognitive Bias and Its Impact on Women’s Advancement in Tech #3: The Ambiguity effect
A Real-World Example
Sarah, a software engineer with four years of experience at a technology firm, watches as project assignments are distributed during a team meeting. The lead architect announces two upcoming initiatives: upgrading the existing payment system (a well-documented project with clear requirements) and developing a new machine learning feature for customer recommendations (an innovative project with uncertain scope and evolving requirements). Despite Sarah’s recent completion of machine learning coursework and expressed interest, the ML project goes to her male colleague, who has similar experience. The payment system upgrade, safer and more predictable, lands on Sarah’s desk.
This scenario exemplifies the ambiguity effect in action. When HR policies do not rely on objective criteria and the context for evaluation is ambiguous, organizational decision-makers will draw on gender (and other) stereotypes to fill in the blanks when evaluating candidates [1]. The decision maker, faced with uncertainty about the ML project’s requirements and Sarah’s capabilities in this new domain, defaults to the “safer” choice of assigning her to familiar territory.
Research Evidence: How Ambiguity Blocks Women’s Advancement
The ambiguity effect is a cognitive bias where people make more conservative decisions when information is unclear or incomplete, defaulting to stereotypes and assumptions to fill in the gaps. In computing careers, this bias systematically disadvantages women because when managers lack concrete information about an employee’s capabilities for a new technical challenge, they unconsciously fall back on stereotypes suggesting men are more naturally suited for technical risk-taking and innovation. The result is a pattern where women need significantly more proof of competence than men to access the same opportunities, particularly for the stretch assignments and innovative projects that accelerate careers in technology.
This higher burden of proof for women appears consistently across multiple research settings. Handley and colleagues tested this phenomenon with over four thousand participants evaluating job candidates [2]. The study revealed that vague performance descriptors like “shows promise” or “has potential” triggered gender bias, with evaluators rating male candidates as more competent than equally qualified female candidates. But when the researchers replaced ambiguous language with concrete achievements like “increased sales by twenty-three percent” or “completed AWS certification,” the gender gap vanished entirely. The bias was strongest among participants who believed workplace discrimination no longer exists, suggesting that people most confident in their fairness are actually most susceptible to letting unconscious assumptions guide their decisions when information is unclear. Real technology companies show these same patterns playing out in career-defining decisions. The Stanford VMware Women’s Leadership Innovation Lab documented how nearly eighteen hundred engineers across five companies experienced drastically different career trajectories based on gender, despite identical performance ratings [3]. Women consistently received well-defined maintenance and upgrade projects while men got the innovative, undefined projects that led to promotions.
The encouraging news is that organizations can eliminate these disparities through structural changes that remove ambiguity from decision-making. Bohnet and colleagues proved this through a field experiment at a multinational technology firm where women were receiving less than a third of innovation assignments despite representing forty percent of the engineering talent pool [4]. The company implemented a system requiring managers to evaluate all team members against specific criteria before making project assignments. This simple change, forcing concrete rather than impressionistic evaluations, increased women’s share of innovation assignments to match their representation. More remarkably, the shift revealed that many women managers who had been considered “not quite ready” actually met all the stated requirements once those requirements were explicitly defined. Women’s promotion rates increased substantially, and the positive effects persisted for years.
Practical Countermeasures That Work
Organizations can implement specific interventions that directly counter the ambiguity effect and create more equitable pathways for women in computing:
1. Post all project opportunities on internal platforms with explicit selection criteria. Currently, only fifteen percent of companies track who receives high-visibility assignments, allowing bias to operate invisibly [5]. Publishing opportunities with clear requirements remove the guesswork that triggers biased assumptions about who is “ready.” Scotiabank’s transparent advancement process, where employees self-assess against published criteria, led to more objective evaluations and increased women’s representation in leadership roles [6].
2. Replace subjective “readiness” assessments with competency frameworks that measure specific skills. Meta-analysis shows bias decreases dramatically when evaluators have clear competency information rather than vague impressions [7]. Instead of asking, “Is she ready for this challenge?” managers should evaluate “Does she meet these five specific technical requirements?” This prevents the common pattern where women must be overqualified while men get opportunities to grow into roles.
3. Create pilot programs that let women demonstrate capabilities in emerging technical areas. Small-scale test projects reduce the perceived risk of assigning uncertain work to women. Research shows that challenging assignments early in careers are particularly crucial, marking women as potential leaders while building confidence that compounds over time [8]. Seventy-one percent of senior leaders identify stretch assignments as their biggest career enabler, yet without deliberate programs ensuring equal access, these opportunities flow disproportionately to men [9].
4. Implement quarterly audits of project assignments broken down by gender and project type. Track which managers consistently assign women to maintenance work while giving men innovative projects. Tying evaluations to this metrics and holding managers accountable for patterns reduces stereotyping in assignment decisions [10]. These audits also prevent stretch opportunities for going only to confident self-promoters while overlooking technically excellent women who may downplay their readiness [5].
5. Require managers to document specific evidence for all performance evaluations and promotion decisions. Vague statements like “not quite ready” or “shows potential” should trigger requests for concrete examples. When forced to articulate specific reasons, managers often discover their impressions don’t match actual performance data. This simple requirement can reveal when assumptions rather than evidence are driving decisions.
6. Establish rotation programs that systematically expose all high-potential employees to different technical domains. Rather than allowing informal networks and comfort levels to determine who gets diverse experiences, create structured pathways that ensure women build the varied portfolios needed for senior roles. This counters the tendency to keep women in “safe,” familiar territories while men get opportunities to explore emerging technologies.
The common thread across these interventions is replacing ambiguity with clarity. When organizations force themselves to articulate criteria, track patterns, and justify decisions with evidence, they eliminate the uncertainty that allows bias to flourish.
The Path Forward
The ambiguity effect is not an immutable barrier but a solvable challenge undermining women’s advancement in computing. Research consistently shows that when organizations remove ambiguity through clear criteria, structured processes, and concrete information, gender disparities in project assignments and promotions decrease or disappear entirely. The solution lies not in changing women but in changing systems to eliminate the uncertainty that triggers biased assumptions.
References
[1] M. E. Heilman and S. Caleo, “Combatting gender discrimination: A lack of fit framework,” Group Processes & Intergroup Relations, vol. 21, no. 5, pp. 725-744, 2018. DOI: 10.1177/1368430218761587
[2] W. M. Handley et al., “In some professions, women have become well represented, yet gender bias persists—Perpetuated by those who think it is not happening,” Science Advances, vol. 6, no. 7, eaba7814, 2020. DOI: 10.1126/sciadv.aba7814
[3] S. Correll and C. Simard, “Research: Vague Feedback Is Holding Women Back,” Harvard Business Review, April 2016. [Online]. Available: https://hbr.org/2016/04/research-vague-feedback-is-holding-women-back
[4] I. Bohnet, A. van Geen, and M. Bazerman, “When Performance Trumps Gender Bias: Joint vs. Separate Evaluation,” Management Science, vol. 62, no. 5, pp. 1225-1234, 2016. DOI: 10.1287/mnsc.2015.2186
[5] M. E. Heilman and M. C. Haynes, “No credit where credit is due: Attributional rationalization of women’s success in male-female teams,” Journal of Applied Psychology, vol. 90, no. 5, pp. 905-916, 2005. DOI: 10.1037/0021-9010.90.5.905
[6] R. Mohr, “Why companies should give women more stretch assignments,” The Ladders, 2021. [Online]. Available: https://www.theladders.com/career-advice/why-companies-should-give-women-more-stretch-assignments
[7] International Labour Organization, “Breaking barriers: Unconscious gender bias in the workplace,” ILO ACT/EMP Research Note, August 2017. [Online]. Available: https://www.ilo.org/wcmsp5/groups/public/—ed_dialogue/—act_emp/documents/publication/wcms_601276.pdf
[8] S. J. Ceci, S. Kahn, and W. M. Williams, “Exploring Gender Bias in Six Key Domains of Academic Science: An Adversarial Collaboration,” Psychological Science in the Public Interest, vol. 24, no. 1, pp. 15-73, 2023. DOI: 10.1177/15291006231163179
[9] CSQ Editorial Team, “5 Ways Stretch Assignments Elevate Women to Leadership,” C-Suite Quarterly, December 2024. [Online]. Available: https://csq.com/2024/12/5-ways-stretch-assignments-elevate-women-to-leadership/
[10] P. Cecchi-Dimeglio, “How Gender Bias Corrupts Performance Reviews, and What to Do About It,” Harvard Business Review, April 2017. [Online]. Available: https://hbr.org/2017/04/how-gender-bias-corrupts-performance-reviews-and-what-to-do-about-it