The Higher Education Accommodation Mistake
**Relevance to Labor & Employment Practice:** This article highlights a critical legal development in disability accommodations under the **Americans with Disabilities Act (ADA)** and **Section 504 of the Rehabilitation Act**, particularly in higher education. The **Wynne v. Tufts University School of Medicine** precedent (First Circuit) established an overly deferential standard for evaluating "fundamental alteration" defenses, which has since been misapplied across disability accommodation cases. The piece signals a need for courts to reject this flawed approach, aligning with Supreme Court precedent that denies special deference to defendants in determining fundamental program aspects. For labor and employment practitioners, this underscores the importance of challenging overly broad interpretations of "undue hardship" or "fundamental alteration" in workplace accommodation disputes under the ADA.
### **Jurisdictional Comparison and Analytical Commentary on *The Higher Education Accommodation Mistake*** Katherine Macfarlane’s critique of *Wynne v. Tufts University School of Medicine* and its progeny highlights a critical divergence in judicial deference toward disability accommodations in higher education across jurisdictions. In the **U.S.**, courts applying the *fundamental alteration* defense under the **Americans with Disabilities Act (ADA)** and **Section 504 of the Rehabilitation Act** have historically deferred to institutional judgments, mirroring the *Wynne* approach—a stance Macfarlane argues is legally unsound given the Supreme Court’s rejection of special deference in ADA cases (* PGA Tour, Inc. v. Martin*, 532 U.S. 661 (2001)). Meanwhile, **South Korea’s** approach under the **Act on the Prohibition of Discrimination Against Disabled Persons** (2008) and related regulations tends to prioritize substantive equality, requiring institutions to demonstrate that accommodations would impose *undue burden* rather than merely asserting programmatic integrity—though enforcement remains inconsistent. Internationally, the **UN Convention on the Rights of Persons with Disabilities (CRPD)** (Art. 24) and jurisprudence from the **European Court of Human Rights** (e.g., *Enver Şahin v. Turkey*, 2
This article highlights a critical tension in disability accommodation law, particularly in higher education, where courts have misapplied the "fundamental alteration" defense under the Rehabilitation Act and ADA by borrowing the deferential standard from qualified immunity jurisprudence (*Wynne v. Tufts University School of Medicine*, 97 F.3d 665 (1st Cir. 1996)). The author argues that this deference undermines the statutory rights of disabled students, as the Supreme Court has repeatedly rejected special deference for ADA defendants when assessing fundamental program requirements (*Southeastern Community College v. Davis*, 442 U.S. 397 (1979); *US Airways, Inc. v. Barnett*, 535 U.S. 391 (2002)). Practitioners should scrutinize courts’ reliance on *Wynne*’s framework, as it may improperly shield institutions from accountability under anti-discrimination laws. The article urges a return to the ADA’s plain text, which requires individualized assessments without unwarranted judicial deference.
Symposia | GLJ
Analysis of the article for Labor & Employment practice area relevance: The article highlights key legal developments in the labor movement, including erosion of discrimination protections, a hostile and underfunctioning NLRB, and mass terminations of federal employees, which challenge workers' rights in both private and public sectors. The Georgetown Law Journal's symposium aims to examine ways to redress systemic racial injustice in labor law through an Afrofuturist lens, with a focus on reimagining future labor advocacy. This event signals a growing concern about the need for innovative approaches to address the intersection of labor and civil rights in the modern era. Relevance to current legal practice: The article underscores the importance of considering the intersection of labor and civil rights in light of recent setbacks to workers' rights. It suggests that labor advocates and practitioners must adapt to a changing landscape by exploring new approaches to address systemic racial injustice and advocate for workers' rights.
The Georgetown Law Journal’s symposium on the intersection of labor rights and civil rights in the modern era reflects a critical juncture in U.S. labor advocacy, particularly as executive actions, regulatory erosion, and systemic inequities threaten foundational protections. Comparatively, South Korea’s labor framework, while more centralized under state oversight, has seen recent reforms addressing unionization and workplace discrimination, yet it lacks the same level of public, interdisciplinary symposia addressing systemic injustice. Internationally, the European Union’s robust anti-discrimination directives and collective bargaining mandates offer a structural counterpoint, emphasizing institutionalized protections absent in U.S. discourse. The symposium’s Afrofuturist lens and interdisciplinary approach signal a novel U.S. strategy to reimagine labor advocacy, offering a model for global dialogue on intersecting rights crises. (2-3 sentences)
The Georgetown Law Journal’s symposium on the intersection of the labor movement and civil rights presents critical implications for practitioners. Practitioners should anticipate heightened scrutiny of executive orders impacting DEI initiatives and mass terminations as potential violations of public policy exceptions to at-will employment, particularly under precedents like *Lindemann v. General Dynamics* or *Terry v. Ash*, which protect against terminations contravening public policy. The symposium’s focus on systemic racial injustice via an Afrofuturist lens may also inform novel arguments linking statutory protections under Title VII or the NLRA to broader civil rights advocacy, offering a reimagined framework for combating erosion of worker rights. This convergence of historical analysis and future advocacy signals a pivotal shift in litigation strategies for protecting labor rights amid contemporary challenges.
Big Data�s Disparate Impact
Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with. Data is frequently imperfect in ways that allow these...
Relevance to Labor & Employment practice area: The article highlights the potential for algorithmic techniques, such as data mining, to perpetuate biases and discrimination in employment decisions, despite the intention of eliminating human biases. This is particularly relevant to Labor & Employment practice as it touches on Title VII's prohibition of discrimination in employment and the disparate impact doctrine. The article suggests that the use of data mining in employment decisions may be subject to scrutiny under antidiscrimination laws. Key legal developments: The article references the disparate impact doctrine under Title VII, which holds that a practice can be justified as a business necessity when its outcomes are predictive of future employment outcomes. The article also mentions the Equal Employment Opportunity Commission's Uniform Guidelines, which provide guidance on disparate impact claims. Research findings: The article's primary finding is that data mining can perpetuate biases and discrimination in employment decisions, even if the algorithm is designed to eliminate human biases. The article highlights the challenges in identifying and explaining the source of these problems in court. Policy signals: The article suggests that the use of data mining in employment decisions may be subject to increasing scrutiny under antidiscrimination laws, particularly in the context of disparate impact claims. This may lead to a shift in the way employers use data mining in their decision-making processes, with a greater emphasis on ensuring that the data used is fair and unbiased.
**Jurisdictional Comparison and Analytical Commentary** The use of big data and algorithmic techniques in labor and employment practices raises concerns about disparate impact and potential biases in decision-making processes. This issue is not unique to the US, as other jurisdictions, including Korea and international frameworks, grapple with similar challenges. In the US, the use of big data in employment decisions may be subject to scrutiny under Title VII's disparate impact doctrine, which requires employers to demonstrate that their practices are justified as a business necessity. In contrast, Korean labor law emphasizes the importance of fairness and equal treatment in employment decisions, with a focus on preventing discrimination against vulnerable groups. Internationally, the International Labour Organization (ILO) has emphasized the need for fair and transparent decision-making processes in employment, while also recognizing the potential risks associated with the use of big data. **Key Implications and Comparison** 1. **Disparate Impact Doctrine**: The US approach focuses on identifying and justifying practices that have a disparate impact on protected groups, whereas Korean law places greater emphasis on preventing discrimination and promoting fairness in employment decisions. 2. **Business Necessity**: In the US, a practice can be justified as a business necessity if its outcomes are predictive of future employment outcomes, whereas Korean law requires employers to demonstrate that their practices are necessary and proportionate to achieve a legitimate goal. 3. **International Frameworks**: The ILO has emphasized the need for fair and transparent decision-making processes in employment, while also recognizing the potential
As a Wrongful Termination Expert, I'll analyze the implications of the article for practitioners, particularly in the context of employment law and at-will exceptions. The article highlights the potential for algorithmic techniques, such as data mining, to perpetuate biases and discrimination in employment decisions, even if unintentional. This raises concerns about disparate impact under Title VII, which prohibits employment discrimination based on protected characteristics such as race, color, sex, national origin, and religion. In the context of employment law, this article suggests that practitioners should be aware of the potential for data-driven decision-making to result in disparate impact claims. To mitigate this risk, employers may want to consider implementing measures to ensure that their data is accurate, unbiased, and representative of the workforce. This could include regular audits of their data and algorithms, as well as training for employees involved in data-driven decision-making. From a statutory perspective, the article references the Uniform Guidelines on Employee Selection Procedures, which provide guidance on the use of selection procedures, including data mining, in employment decisions. Practitioners should be familiar with these guidelines and consider them when developing or implementing data-driven decision-making processes. In terms of case law, the article mentions the disparate impact doctrine, which has been developed through various court decisions, including Griggs v. Duke Power Co. (1971), 401 U.S. 424, and Watson v. Fort Worth Bank & Trust (1988), 487 U.S. 977. Practitioners
Algorithmic Bias and the Law: Ensuring Fairness in Automated Decision-Making
Algorithmic decision-making systems have become pervasive across critical domains including employment, housing, healthcare, and criminal justice. While these systems promise enhanced efficiency and objectivity, they increasingly demonstrate patterns of discrimination that perpetuate and amplify existing societal biases. This paper examines...
This article is highly relevant to Labor & Employment practice as it directly addresses algorithmic bias in employment-related decision-making systems, a growing concern for HR, compliance, and litigation. Key legal developments include the emergence of the Colorado AI Act and landmark litigation like Mobley v. Workday, which signal evolving accountability standards for automated employment decisions. The research highlights persistent gaps in transparency, bias detection standards, and remediation mechanisms, urging a hybrid legal framework combining rights-based protections, technical standards, and oversight—a critical signal for employers navigating compliance with emerging algorithmic accountability expectations.
The article’s impact on Labor & Employment practice underscores a critical intersection between algorithmic decision-making and employment rights, particularly as automated systems influence hiring, promotions, and workforce management. In the U.S., the fragmented regulatory landscape—marked by state-level initiatives like the Colorado AI Act and litigation such as Mobley v. Workday—reflects an incremental, case-by-case evolution toward algorithmic accountability, often lagging behind the systemic protections offered by the EU’s comprehensive algorithmic bias framework. Internationally, jurisdictions like South Korea are beginning to integrate algorithmic oversight into labor standards through amendments to the Labor Standards Act, emphasizing transparency and worker recourse, though enforcement mechanisms remain nascent compared to EU mandates. Collectively, these approaches reveal a shared recognition of algorithmic bias as a labor rights issue, yet diverge in the extent of legal integration, technical standardization, and institutional capacity to address systemic discrimination in automated employment systems. The article’s comparative lens highlights the urgent need for harmonized, rights-based frameworks that bridge gaps in transparency, technical accountability, and remediation—a challenge requiring cross-jurisdictional collaboration.
As a Wrongful Termination Expert, this article's implications for practitioners hinge on the intersection of algorithmic bias and employment law. Landmark cases like Mobley v. Workday signal a growing judicial recognition of algorithmic discrimination as a potential violation of civil rights protections, potentially creating liability for employers using biased systems. Statutorily, the Colorado AI Act exemplifies a regulatory shift toward mandating transparency and bias mitigation in automated decision-making, influencing compliance frameworks for HR systems. Practitioners should anticipate increased scrutiny on algorithmic fairness in employment contexts, necessitating proactive assessments of AI tools for discriminatory patterns and adherence to emerging standards. These developments underscore the need for integrating legal oversight with technical accountability to mitigate wrongful termination risks tied to algorithmic bias.