AI & Algorithms: The Double-Edged Sword for 2026 Workplace Equity

AI is embedded in nearly every aspect of life, including the workplace. Consider all the processes from hire-to-retire—including job descriptions, hiring screens, performance evaluations, learning systems, promotions, and even cultural analytics.

To business and HR leaders, AI is being marketed as the great equalizer—a groundbreaking tool that will finally “remove human bias” from mundane HR processes.

Here’s the hard truth leaders need to hear: AI can remove bias… and AI can also amplify it.

The Secret Sauce: Governance and Ethical Use

In 2026, the organizations who excel at utilizing AI understand one critical factor: governance and ethical use. Companies are hungry for efficiency and technology vendors are promising productivity.

Beyond mere compliance, equity is a talent strategy. Organizations that rigorously vet their AI systems for fairness secure a competitive advantage: they unlock access to untapped talent pools, reduce employee turnover by fostering trust, boost innovation through diverse perspectives, and gain a reputation as an equitable employer.

For US-based organizations, the use of AI in HR processes carries significant legal risk under the Americans with Disabilities Act (ADA) and equivalent state laws. When an algorithm, through poor training or design, systematically screens out disabled workers or unfairly lowers their performance scores, the company is directly liable. The lack of proper governance is not just an ethical failing; it is an open door to litigation and regulatory penalties.

Yet Deaf, DeafBlind, and hard of hearing professionals—along with many other disabled communities—are living out the consequences of poorly tested, untrustworthy, and unregulated workplace AI.

The dangers of AI are seemingly predictable:

• Job descriptions rewritten by AI that erase accessibility language
• Generated interview and performance review questions may perpetuate ableism, based off historic data no longer relevant
• Resume screeners penalize employment gaps caused by systemic issues
• Performance ratings skewed because AI “listens” for verbal participation in meetings
• Automatic captioning tools with 10% error rates being used to evaluate communication skills
• Demographic analytics that categorize disability as a “risk variable”

This is the double-edged sword: AI can help systems evolve… or ossify inequity faster than ever.

The Danger of Biased AI at Work

The threat of inequity isn't limited to a single algorithm; it spans various AI applications. This includes Machine Learning (ML) models used for predictive performance scores, Natural Language Processing (NLP) tools that draft and analyze job descriptions, and computer vision systems that monitor meeting participation or factory output.

AI systems learn from the data they’re trained on—and most corporate datasets are built from decades of manual reporting and inequitable practices. On the flip side, AI is being used by nearly everyone, regardless of education level or age.  Moreover, there is a huge turnover in tenured talent as Baby Boomers gear up for retirement and Gen Z enters the workforce. Decades of legacy knowledge are disappearing without proper systems in place to train the AI.

To conceptualize this challenge, consider a real-world example from the healthcare setting.

Recently, I experienced a significant challenge when scheduling and coordinating logistics for a surgical procedure. Even though I informed the scheduling nurse that I needed an in-person interpreter for pre-op, the surgery itself (for my wife, who is also Deaf), and post-op, the nurse ignored my request and only scheduled the interpreter to arrive at the start of the procedure.

It is likely that if this nurse contributed to the development of the hospital’s AI platform on protocols and best practices for scheduling, this flawed approach would have resulted in future errors downstream for other patients requesting interpreters. Beyond the process flaws, this mistake could result in decreased employee efficiency and a poor customer or patient experience.

To rectify this, data must be scrubbed clean of impurities—which is often a momentous task for companies that still use manual reporting. After all, bad data in = bad data out.

The same principle can be applied in the workplace.

Consider, for instance, a number of Deaf employees who historically received fewer customer-facing opportunities because appropriate accommodations weren’t provided. Without critical context, AI will conclude these employees are less “leadership ready” despite the fault of the process - not of the employee. 

Likewise, if HR systems show lower engagement scores from employees denied communication access, AI may label them “low morale” or “poor culture fit.”

If virtual meetings’ automated captioning is not attributed to the meeting speaker, AI-powered meeting tools may record a Deaf employee’s contribution as “silent.”

This isn’t science fiction. It’s a reality for Deaf, DeafBlind, and hard of hearing individuals in the workplace. Because AI is fast, scalable, and integrated into everything, bias doesn’t just creep in - it compounds without the proper checks and balances in place.

As we move into 2026, companies must remember their responsibility to ethical AI: Will this powerful tool be used to reinforce inequity? Or will AI enable others to prioritize human input during The Great Correction?

The Opportunity: AI as an Accessibility Accelerator

AI can do incredible things—if we design and govern it correctly.

When paired with conscientious audits, ethical frameworks, and Deaf-led testing, AI can:

• Identify biased job descriptions at scale
• Flag inaccessible meeting patterns
• Detect accessibility gaps impacting performance data
• Surface advancement disparities early
• Build equitable career paths through personalized learning

The AI itself isn’t the enemy. Unregulated AI is.

What matters most is how intentionally—and how transparently—companies use it. To help partners ensure trustworthy and equitable AI, 2axend offers ethical auditing and policy strategy services, providing Deaf-led validation and expert review to weed out systemic biases and advise on robust accessibility best practices.

What Companies Must Do Going Into 2026

To prevent 2026 from becoming the year AI widens the equity gap, organizations must adopt one non-negotiable standard: Mandate audits for all HR-related AI tools before implementation. This shall include:

• Testing hiring AI for disability bias, not just race/gender bias
• Validating accessibility accuracy with Deaf, DeafBlind, and/or hard of hearing users
• Auditing performance-review algorithms for communication-based discrimination
• Requiring vendors to provide training data transparency and information regarding compliance with accessibility standards
• Implementing “human override paths” for all AI-driven decisions
• Mandating shadow testing of new AI tools against non-AI systems to quantify bias before going live.
• Monitoring equity outcomes at least quarterly
• Inviting Business/Employee Resource Group (B/ERG) leaders to participate as required AI process auditors

Simply put, no AI tool should go live without passing an inclusion audit from the affected users and tenured leaders—and including a formal, documented human override path to prioritize human judgment over algorithmic recommendation.

2026 will be the inflection point: AI can be the tool that accelerates equity - or the tool that systemically contributes to exclusion. Next week, we’ll break down exactly how these trends connect to the biggest shift coming in 2026: The Great Correction.