The Biggest Missteps of 2025: Putting an End to Data and AI Disasters

2025 was supposed to be the year artificial intelligence and data-driven systems finally delivered on their promise: efficiency, inclusion, and innovation. Instead, it became a year of hard lessons. Across governments, corporations, and platforms, repeated data and AI failures exposed a familiar truth.  Technology is only as ethical as the systems of power that shape it.

For women, marginalized communities, and digital rights defenders, these missteps were not abstract “tech problems.” They had real consequences: surveillance without consent, automated exclusion, silencing of voices, and deepened inequalities. As we move forward, ending data and AI disasters must start with naming what went wrong.

1. Treating Data as a Resource, Not a Right

One of the biggest missteps of 2025 was the continued framing of personal data as a commodity rather than a human rights issue. Governments and companies rushed to collect, share, and monetize data without meaningful consent, transparency, or accountability.

From biometric registration systems to AI-powered public services, people, especially women, were often unaware of how their data was being used, stored, or weaponized. Survivors of gender-based violence, activists, and informal workers faced heightened risks when sensitive data was exposed or misused.

A feminist approach to data governance insists that data protection is not a luxury. It is foundational to dignity, safety, and autonomy.

2. Deploying AI Without Context or Care

In 2025, AI systems were rolled out faster than the safeguards meant to protect people from harm. Automated decision-making tools were used in hiring, lending, welfare distribution, and content moderation, often without considering local contexts or gendered impacts.

The result? Women were disproportionately misclassified, excluded, or penalized. AI systems trained on biased datasets replicated historical discrimination, while opaque algorithms made it nearly impossible to challenge unfair outcomes.

When AI is deployed without accountability, it doesn’t eliminate inequality, it automates it.

3. Ignoring Gendered Harms in AI and Digital Systems

Another critical failure was the persistent refusal to take gendered harms seriously. Online abuse, deepfakes, impersonation, and disinformation targeting women increased, yet responses remained slow and inadequate.

AI-generated content made it easier to spread false narratives and harass women in public life, while platform policies lagged behind the scale and sophistication of these attacks. Too often, women were told to “report and move on,” placing the burden of safety on users rather than on systems.

Ending AI disasters means recognizing that safety is not gender-neutral, and neither is harm.

4. Weak Regulation, Strong Surveillance

While regulators struggled to keep up with AI innovation, surveillance technologies expanded rapidly. Under the guise of security, efficiency, or service delivery, digital monitoring increased with little oversight.

For women human rights defenders, journalists, and organizers, this created a chilling effect. Knowing that communications could be tracked or analyzed discouraged participation and silenced dissent. In many cases, data protection laws existed on paper but were poorly enforced in practice.

Technology that watches more than it protects is not progress but control.

5. Excluding Women From AI Decision-Making

Perhaps the most damaging misstep of 2025 was who was missing from the table. Women, particularly those from the Global South, were largely excluded from AI governance, policy design, and technical leadership.

Decisions about data and AI were made without the voices of those most affected by their consequences. This exclusion reinforced systems that prioritize speed and profit over care, inclusion, and justice.

A feminist future for AI demands participation, not tokenism.

Putting an End to Data and AI Disasters

If 2025 taught us anything, it is that technical fixes alone are not enough. Ending data and AI disasters requires a shift in values .

• Human rights-first data governance
• Gender-responsive AI impact assessments
• Strong enforcement of data protection laws
• Platform accountability for AI-driven harms
• Meaningful inclusion of women in tech and policy spaces

At Shetechtive, we believe that women are not just victims of data and AI failures. We are critical to building better systems. Feminist approaches to technology center care, accountability, and justice. They ask not only what can technology do, but who does it serve, and at what cost?

The future of AI does not have to be disastrous. It will only be transformative if we choose people over profit, rights over speed, and inclusion over convenience.