Policy & Perspectives  | ‘Bias’ in AI Ethics & Current US Policy

August 19, 2025

Bias in AI: Where Traditional Frameworks & 2025 U.S. Policy Diverge

Policy shifts of any kind, no matter who’s in charge, can affect the technologies shaping people’s lives — especially as it relates to the ethical frameworks deployed into AI systems.

This page presents a nonpartisan overview of how today’s U.S. policy on AI bias, under the current administration, differs from long-established AI frameworks.

In sharing the information below, CPI attempts to highlight what’s changing to offer practical context for researchers, developers, and members of the public who want to make sense of the evolving policy environment.

What are traditional AI ethics and fairness frameworks?

Over the past decade, several well-regarded frameworks have shaped global conversations about ‘bias and fairness’ in AI.

These include:

AIFX (AI Fairness Index)

A developing standard used by tech companies and researchers to rate bias across racial, gender, cultural, and linguistic dimensions within large language models.

AIFS (AI Fairness Standards)

Less formalized, but used across industry to benchmark AI models against ethical fairness thresholds (including demographic parity, disparate impact, etc.).

These known frameworks share common ground:

they treat bias as multi-dimensional, emphasizing demographic inclusivity and fairness for historically marginalized groups.

2025 U.S. AI policy offers a tighter definition of ‘bias’

In July 2025, the current administration issued new directives titled:

These directives mark a significant shift in how the federal government now defines and regulates AI bias.

While still championing fairness, the current U.S. policy reframes bias through a different lens — one that prioritizes ideological neutrality and a merit-based, identity-blind approach to algorithmic governance as follows:

  • De-emphasizing race- or gender-based corrective mechanisms in AI systems
  • Promoting “neutral algorithms” over identity-aware design
  • Defining ‘bias’ primarily in terms of ideological imbalance
  • Rejecting policies that seek to remediate systemic or historical inequalities through AI corrections

Why this shift matters

These policy pivots have real implications.

Many developers, technologists, and scholars work within frameworks that ask them to detect and correct for bias — especially as it relates to race, gender, and class.

However, the current U.S. policy deprioritizes or outright excludes many of those same categories from its regulatory focus.

This puts AI builders in a complex position because industry standards and international norms that call for demographic fairness now appear to go against the grain of US national policy that prioritizes ideological neutrality and non-identity-based governance.

To help the public and technologists alike better understand this tension and shift, we’ve compiled the following table to carefully delineate how the latest iteration of US AI policy now differs from common norms and frameworks.

Comparative Table: Bias in AI Ethics vs. Current U.S. Policy

Bias Type in AI Ethics Common in AI Fairness Frameworks (AIFX, NIST, AIFS) Position of Current U.S. AI Policy (2025)
Racial bias ✔ Yes — foundational issue in fairness conversations ❌ Rejects race-based mitigation; promotes race-blind AI design
Gender bias ✔ Yes — central to discussions on equity and inclusion ❌ Eliminates gender-based corrections; emphasizes neutrality
Sexual orientation bias ✔ Often addressed within LGBTQ+ fairness models ❌ Not recognized as needing special handling in algorithm design
Religious bias ✔ Noted in anti-stereotyping and fairness contexts ⚠️ Retained in some national security applications
Political/ideological bias (anti-conservative) ✔ Occasionally acknowledged, not a primary focus ✅ Central concern under current U.S. policy
Geographic/regional bias ✔ Sometimes addressed (e.g., urban vs. rural access) ✅ Recognized and targeted in rural fairness narratives
Economic class bias ✔ Included in growing concerns about class inequity in automated systems ❓ Not explicitly addressed
Disability bias ✔ Found in accessibility discussions and compliance standards ❓ No clear position stated
Accent/language bias ✔ Included in linguistic inclusivity and dialect fairness ❓ Not prioritized
Cultural bias ✔ Central to global fairness narratives ✅ Supports U.S.-centric cultural framing
Historical/systemic bias ✔ Addressed via correction of legacy injustices (e.g., redlining) ❌ Not considered a valid basis for correction
Age bias ✔ Noted in healthcare, hiring, and education models ❓ Not emphasized
Name bias ✔ Documented in resume filtering and data labeling studies ❌ Considered irrelevant or overly “woke”
Educational elitism ✔ Gaining traction as a bias metric (e.g., Ivy League favoritism) ✅ Replaced by skills-first, credential-free merit emphasis

Final notes

At CPI, we’re focused on clear, credible, and constructive insights — not partisan agendas.

We spotlight information and timely contexts so that technologists, policymakers, workforces, and the public at large can stay abreast of AI trends, policies, and impacts across AI technologies as well as their layered impacts across many aspects of our lives.

This includes recognizing when public policy iterates in new directions. The evolving interpretation of ‘bias’ in AI governance under the current U.S. policy is one such example of a noteworthy pivot.

We remain committed to our tracking of these developments and to provide transparent, cyberpsychology-informed contexts to help technologists, workforces, and the broad public stay alert and make informed choices.

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Policy & Perspectives
Current AI Policy: CPI Statement
‘Bias’ in AI Ethics & Current US Policy