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  • Artificial Intelligence Sparks the Interest of Federal Policymakers – CUPA-HR

    Artificial Intelligence Sparks the Interest of Federal Policymakers – CUPA-HR

    by CUPA-HR | November 15, 2023

    A growing interest in artificial intelligence and its potential impact on the workforce has sparked action by policymakers at the federal level. As employers increasingly turn to AI to fill workforce gaps, as well as improve hiring and overall job quality, policymakers are seeking federal policies to better understand the use and development of the technology. Recent policies include an executive order from the Biden administration and a Senate committee hearing on AI, both of which are detailed below.

    Executive Order on AI Use and Deployment

    On October 30, the Biden Administration released an executive order delineating the “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.” The order urges responsible AI deployment that satisfies workforce development needs and ethical considerations.

    The executive order directs several agency heads to issue guidance and regulations to address the use and deployment of AI and other technologies in several policy areas. Some orders of particular interest to higher education HR include:

    • The secretary of labor is directed to submit a report analyzing ways agencies can support workers who may be displaced by AI.
    • The secretaries of labor, education and commerce are directed to expand education and training opportunities to provide pathways to careers related to AI.
    • The secretary of labor is ordered to publish principles and best practices for employers to help mitigate harmful impacts and maximize potential benefits of AI as it relates to employees’ well-being.
    • The secretary of labor is directed to issue guidance clarifying that employers using AI to monitor employees’ work are required to comply with protections that ensure workers are compensated for hours worked as defined under the Fair Labor Standards Act.
    • The secretary of labor is directed to publish guidance for federal contractors on nondiscrimination in hiring practices that involve the use of AI and other technology.
    • The director of the National Science Foundation is directed to “prioritize available resources to support AI-related education and AI-related workforce development through existing programs.”
    • The secretary of education is ordered to develop resources and guidance regarding AI, including resources addressing “safe, responsible and nondiscriminatory uses of AI in education.”
    • The secretary of state is ordered to establish a program to “identify and attract top talent in AI and other critical and emerging technologies at universities [and] research institutions” and “to increase connections with that talent to educate them on opportunities and resources for research and employment in the United States.”
    • The secretary of homeland security is directed to continue its rulemaking process to modernize the H-1B program and to consider a rulemaking that would ease the process of adjusting noncitizens’ status to lawful permanent resident status if they are experts in AI and other emerging technologies.

    The executive order directs the agency heads to produce their respective guidance and resources within the next year. As these policies and resources begin to roll out, CUPA-HR will keep members updated on any new obligations or requirements related to AI.

    Senate HELP Committee Hearing on AI and the Future of Work

    On October 31, 2023 the Senate Employment and Workplace Safety Subcommittee held a hearing titled “AI and the Future of Work: Moving Forward Together.” The hearing provided policymakers and witnesses the opportunity to discuss the use of AI as a complementary tool in the workforce to skill and reskill American workers and help them remain a valuable asset to the labor market.

    Democrats and Republicans on the committee agreed that AI has the potential to alter the workforce in positive ways but that the growth of the use of the technology needs to be supported by a framework of regulations that do not smother its potential. According to witnesses, employers using AI currently face a patchwork of state and local laws that complicate the responsible use and growth of AI technologies. They argued that a federal framework to address the safe, responsible use of AI could help employers avoid such complications and allow AI use to continue to grow.

    Democrats on the committee also asked whether education opportunities and skills-based training on AI can help provide an employment pathway for workers. Witnesses argued that AI education is needed at the elementary and secondary level to ensure future workers are equipped with the skills needed to work with AI, and that skills-based training models to reskill workers have proven successful.

    CUPA-HR will continue to track any developments in federal AI regulations and programs and will inform members of updates.



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  • Senate Finance Committee Holds Hearing on Paid Leave – CUPA-HR

    Senate Finance Committee Holds Hearing on Paid Leave – CUPA-HR

    by CUPA-HR | November 14, 2023

    On October 25, the Senate Finance Committee held a hearing on federal paid leave. This comes as congressional Democrats and Republicans have shown interest in finding bipartisan consensus for a federal paid leave program. The hearing also provided policymakers and witnesses the opportunity to discuss the promise and drawbacks of paid leave proposals.

    Increasing employee access to paid leave was a primary focus of the hearing. Both sides of the aisle agreed that all workers will need to take leave during their careers without the obligation to juggle work requirements. Policymakers highlighted that 70 percent of Americans want national paid leave and that 72 percent of Americans who are not currently working cite caregiving and family responsibilities as the main reason. To address these issues, Democrats argued for a federally mandated paid leave program, while Republicans worried that a one-size-fits-all program could limit employer-provided paid leave options and be difficult to implement on a wide scale.

    Witnesses Describe Potential Benefits of Federal Paid Leave

    Some of the witnesses discussed the benefits of a federal paid leave program, concluding that better access to paid leave would benefit workers, employers and the economy. Jocelyn Frye, president of the National Partnership for Women & Families, stated that offering paid leave tends to benefit both workers and employers through increased labor force participation (both for women and generally), worker retention, and wage growth. Ben Verhoeven, president of Peoria Gardens Inc., added that investing in paid leave gave him better return on investment than his capital investments, as implementing paid leave increased business growth and employee retention and promotions.

    Objection to a One-Size-Fits-All Leave Program

    Despite these benefits, Elizabeth Milito, executive director of the National Federation of Independent Business’s Small Business Legal Center, said that employers would face trade-offs under a federal paid leave program. Milito argued that employers operating on the same amount of funds but under new federal benefit requirements would be obliged to provide paid leave as a benefit, leading to some employers being unable to provide higher compensation or other benefits like health insurance. Rachel Greszler, senior research fellow at The Heritage Foundation, said that in response to state paid leave programs, some companies choose to send workers to the state program first and then supplement the paid leave benefit to provide 100 percent wage replacement. This creates an administrative burden for the employee, who receives full wage replacement only if they participate in both paid leave programs.

    Republicans and their witnesses also said that a federal program would require flexibility and simplicity to be most effective. Milito and Greszler concurred that most small businesses do not have a qualified HR professional to deal with additional compliance needs. Greszler also stated that the biggest unintended consequence of a one-size-fits-all approach would be a rigid structure that does not work for most employees and businesses. She specified that a carve-out for small businesses or the ability to opt in to a federal program would be most appropriate.

    CUPA-HR continues to monitor for any updates on federal paid leave programs and will keep members apprised of any new developments.



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  • Proposed Changes to the H-1B Visa Program – CUPA-HR

    Proposed Changes to the H-1B Visa Program – CUPA-HR

    by CUPA-HR | November 9, 2023

    On October 23, 2023, U.S. Citizenship and Immigration Services (USCIS) issued a proposed rule that aims to improve the H-1B program by simplifying the application process, increasing the program’s efficiency, offering more advantages and flexibilities to both petitioners and beneficiaries, and strengthening the program’s integrity measures.

    Background

    The H-1B visa program is pivotal for many sectors, particularly higher education. It permits U.S. employers to employ foreign professionals in specialty occupations requiring specialized knowledge and a bachelor’s degree or higher or its equivalent. The program is subject to an annual limit of 65,000 visas, with an additional allocation of 20,000 visas reserved for foreign nationals who have earned a U.S. master’s degree or higher. Certain workers are exempt from this cap, including those at higher education institutions or affiliated nonprofit entities and nonprofit or governmental research organizations.

    Highlights of the Proposed Rule

    Prompted by challenges with the H-1B visa lottery, USCIS has prioritized a proposed rule to address the system’s integrity. The move comes after a surge in demand for H-1B visas led to the adoption of a lottery for fair distribution. However, with the fiscal year 2024 seeing a historic 758,994 registrations and over half of the candidates being entered multiple times, there was concern over potential exploitation to skew selection chances. This proposed rule is a direct response to strengthen the registration process and prevent fraud.

    Beyond addressing lottery concerns, the proposal makes critical revisions to underlying H-1B regulations. It seeks to formalize policies currently in place through guidance and tweak specific regulatory aspects.

    Amending the Definition of a “Specialty Occupation.” At present, a “specialty occupation” is identified as a job that requires unique, specialized knowledge in fields like engineering, medicine, education, business specialties, the arts, etc., and it typically mandates a bachelor’s degree or higher in a specific area or its equivalent. USCIS is proposing to refine the definition of a “specialty occupation” to ensure that the required degree for such positions is directly related to the job duties. The proposal specifies that general degrees without specialized knowledge do not meet the criteria, and petitioners must prove the connection between the degree field(s) and the occupation’s duties. The rule would allow for different specific degrees to qualify for a position if each degree directly relates to the occupation’s responsibilities. For example, a bachelor’s degree in either education or chemistry could be suitable for a chemistry teacher’s position if both are relevant to the job. The changes emphasize that the mere possibility of qualifying for a position with an unrelated degree is insufficient, and specific degrees must impart highly specialized knowledge pertinent to the role.

    Amending the Criteria for Specialty Occupation Positions. USCIS is proposing updates to the criteria defining a “specialty occupation” under the Immigration and Nationality Act. This proposal includes a clarification of the term “normally,” which, in the context of a specialty occupation, indicates that a bachelor’s degree is typically, but not always, necessary for the profession. USCIS is aiming to standardize this term to reflect a type, standard, or regular pattern, reinforcing that the term “normally” does not equate to “always.”

    Extending F-1 Cap-Gap Protection. USCIS is proposing to revise the Cap-Gap provisions, which currently extend employment authorization for F-1 students awaiting H-1B visa approval until October 1 of the fiscal year for which H–1B visa classification has been requested. The Cap-Gap refers to the period between the end of an F-1 student’s Optional Practical Training (OPT) and the start of their H-1B status, which can lead to a gap in lawful status or employment authorization. The new proposal seeks to extend this period until April 1 of the fiscal year for which the H-1B visa is filed, or until the visa is approved, to better address processing delays and reduce the risk of employment authorization interruption. To be eligible, the H-1B petition must be legitimate and filed on time. This change is intended to support the U.S. in attracting and maintaining skilled international workers by providing a more reliable transition from student to professional status.

    Cap-Exempt Organizations. USCIS is redefining which employers are exempt from the H-1B visa cap. The proposed changes involve revising the definition of “nonprofit research organization” and “governmental research organization” from being “primarily engaged” in research to conducting research as a “fundamental activity.” This proposed change would enable organizations that might not focus primarily on research, but still fundamentally engage in such activities, to qualify for the exemption. Additionally, USCIS aims to accommodate beneficiaries not directly employed by a qualifying organization but who still perform essential, mission-critical work.

    Deference. USCIS is proposing to codify a policy of deference to prior adjudications of Form I-129 petitions, as delineated in the USCIS Policy Manual, mandating that officers give precedence to earlier decisions when the same parties and material facts recur. This proposal, however, includes stipulations that such deference is not required if there were material errors in the initial approval, if substantial changes in circumstances or eligibility have occurred, or if new and pertinent information emerges that could negatively influence the eligibility assessment.

    Next Steps

    While this summary captures key elements of the proposed changes, our members should be aware that the rule contains other important provisions that warrant careful review. These additional provisions could also significantly impact the H-1B visa program and its beneficiaries, and it is crucial for all interested parties to examine the proposed rule in its entirety to understand its full implications.

    USCIS is accepting public comment on its proposal through December 22, 2023. CUPA-HR is evaluating the proposed revisions and will be working with other higher education associations to submit comprehensive comments for the agency’s consideration. As USCIS moves towards finalizing the proposals within this rulemaking, potentially through one or more final rules depending on the availability of agency resources, CUPA-HR will keep its members informed of all significant updates and outcomes.



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  • CUPA-HR Submits Comments in Response to DOL’s Overtime Rulemaking – CUPA-HR

    CUPA-HR Submits Comments in Response to DOL’s Overtime Rulemaking – CUPA-HR

    by CUPA-HR | November 8, 2023

    On November 7, CUPA-HR, joined by 49 other higher education associations, submitted comments in response to the Department of Labor (DOL) Notice of Proposed Rulemaking (NPRM) to update the Fair Labor Standards Act (FLSA) overtime regulations. In the NPRM, the DOL proposes to update the salary threshold for the “white collar” exemptions to the FLSA overtime pay requirements from its current level of $35,568 annually to $60,209 per year — a nearly 70% increase.* Additionally, the department proposes to automatically increase the salary level every three years.

    CUPA-HR’s comments highlight the concerns from institutions across the country and ask that the DOL consider four recommendations:

    1) The DOL Should Not Update the Salary Threshold at This Time

    The DOL most recently updated the minimum salary threshold in 2020. CUPA-HR welcomed updates at the time, given the minimum threshold had not been successfully updated since 2004 and the level proposed in 2019 was appropriate at the time. With the most recent update becoming effective in 2020, we believe it is too soon for the DOL to move forward with another update to the minimum salary threshold.

    2) The DOL Should Lower the Proposed Minimum Salary Threshold and Account for Room and Board

    If the DOL does choose to move forward with an increase to the threshold, we believe that the proposed minimum salary threshold is too high. Updating the salary level from $684 per week ($35,568 per year) to $1,158 per week ($60,209 per year) leads to a nearly 70% increase, which will result in a large number of employees being reclassified to nonexempt status. To avoid having to reclassify certain employees to nonexempt status, we ask that the DOL consider room and board as part of an employee’s total salary when considering if such employees meet the minimum salary threshold.

    3) The DOL Should Not Implement Automatic Updates to the Salary Threshold

    In the NPRM, the DOL proposes to implement automatic updates to the salary threshold that would occur every three years. CUPA-HR believes that the DOL does not have the authority to implement automatic updates under the FLSA and that automatic increases will negatively impact institutions’ budgets, their ability to provide merit-based increases, and employee morale.

    4) The DOL Should Extend the Effective Date of Any Final Rule Implementing a Higher Salary Threshold

    According to the NPRM, the DOL anticipates providing 60 days for compliance with a final rule once it is published by the agency. CUPA-HR believes 60 days is too short a timeframe to assess the impact, plan, and implement appropriate changes on campus. Instead, we ask for an effective date that is at least 180 days after any final rule is published.

    CUPA-HR’s president and chief executive officer, Andy Brantley, shared the following: “To say campuses are extremely concerned with the Department of Labor’s proposed rule increasing the minimum salary threshold to the FLSA overtime pay requirements by almost 70% would be an understatement. Employees in positions that clearly meet the three criteria to qualify as white-collar employees who are exempt from the federal overtime pay requirement will be forced into nonexempt positions.”

    In addition to submitting these comments, CUPA-HR also joined the Partnership to Protect Workplace Opportunity’s comment letter addressing concerns with the proposed rule. CUPA-HR will keep members apprised of any updates relating to this proposed rule and our advocacy efforts as the department moves toward finalizing these regulations.


    * The discrepancy between our figure of $60,209 and the DOL’s preamble figure of $55,068 arises from DOL’s own projections based on anticipated wage growth. The DOL’s proposed rule is rooted in 2022 data (yielding the $55,068 figure), but a footnote in the NPRM confirms that the salary threshold will definitely change by the time the final rule is issued to reflect the most recent data. Our comments, aiming to respond to the most probable salary threshold at the time a final rule is released, references the DOL’s projected figure for Q1 2024, which is $60,209. We do not believe DOL will be able to issue a final rule before Q1 2024, so we are incorporating this projected figure into our response to the NPRM. In essence, our goal is to provide members with a clearer picture of the likely salary figure when the final rule comes into play.



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  • HR and the Courts — November 2023 – CUPA-HR

    HR and the Courts — November 2023 – CUPA-HR

    by CUPA-HR | November 8, 2023

    Each month, CUPA-HR General Counsel Ira Shepard provides an overview of several labor and employment law cases and regulatory actions with implications for the higher ed workplace. Here’s the latest from Ira.

    California Becomes First State to Mandate Workplace Violence Prevention Plans

    Under a new law, the first broad state law of its kind, most employers in California must now adopt workplace violence prevention plans by next summer. Before now, hospitals in California were the only group of employers required by state law to adopt workplace violence prevention plans. What specifically must be included in the plan is vague under the terms of the statute. The California Division of Occupational Safety and Health (Cal/OSHA) will be responsible for implementation of this statute and stated that it would adopt an appropriate workplace violence set of standards for employers.

    The law will require employers to establish written plans, employee training and tracking of violent acts. Plans must be specific for each workplace and tailored to meet the individual circumstances of each setting. Commentators are looking for further guidance from Cal/OSHA on the specific details that must be covered by employer plans.

    Mandatory Time Off for Reproductive Loss

    California and Illinois are leading the way in the adoption of state laws mandating that employers guarantee time off following a miscarriage or other reproductive loss to ensure leave for grieving. The laws guarantee employees up to five days of paid or unpaid leave following a reproductive loss including miscarriage and still birth, as well as failed adoption, invitro or surrogacy. Utah has adopted a similar policy for state employees, and several cities have adopted similar statutes. Some national employers already voluntarily include reproductive losses in time-off provisions for employees.

    NLRB Lowers the Bar to Prove Joint Employer Status — May Impact Student-Athlete Cases

    The National Labor Relations Board rescinded a Trump-era regulation requiring that an alleged joint employer must have “direct and immediate” control exercised over employees to prove joint employer status. Under the new standard, if an alleged joint employer indirectly controls job terms or conditions of employment, it is a joint employer subject to NLRB jurisdiction. This will have immediate application to the ongoing dispute as to whether the NCAA and athletic conferences are joint employers of student-athletes, as they exercise control over rules that student-athletes must adhere to.

    This also may affect the NLRB’s attempt to exert jurisdiction over student-athletes at public colleges and universities. While the NLRB has no jurisdiction over public entities, its general counsel is asserting jurisdiction over those student-athletes at public institutions based on the legal theory that the NCAA and/or the athletic conferences are joint employers.

    Student-Athlete Unionization Issue May Affect Smaller Institutions and Athletic Programs

    Two additional, separate NLRB cases are winding their way to a decision on whether student-athletes meet the definition of employee under the National Labor Relations Act and are therefore eligible to unionize. A West Coast case involves the NLRB issuing a complaint claiming that the University of Southern California, the NCAA, and the PAC-12 Conference are joint employers of student basketball and football players and have unlawfully refused to bargain with any union.

    An East Coast case involves a union petition filed by the Service Employees International Union to represent Dartmouth College basketball players. Dartmouth has argued that its basketball players are not employees under the NLRA, as they do not receive sports scholarships and the basketball program does not generate money for the institution.

    Commentators at Bloomberg have concluded that decisions allowing unionization of college athletes may have the most serious repercussions for smaller institutions and even small athletic programs that do not generate revenue at large institutions.

    Class Actions Proliferate Related to Washington State’s Pay Transparency Law  

    A series of 40 or so class actions filed against major employers in Washington state — including Adidas, Home Depot and Marriott — will test the reach of the new Washington state job ad and pay transparency law. The Washington state law, like similar statutes in California, Colorado and New York, requires employers to provide pay ranges and benefits information in job ads, with the aim of improving pay equity for women and employees of color.

    The Washington and California laws also provide plaintiff applicants with a private right to sue, with Washington’s statute incentivizing plaintiffs to sue. It grants plaintiffs an award of actual damages proven or $5000, whichever is greater, plus attorney fees upon proving a pay transparency violation.

    Former Women’s Basketball Coach Loses Sex Discrimination Lawsuit

    The former head women’s basketball coach at the University of Montana has lost the sex discrimination lawsuit she filed following her termination after a poor win-loss record and serious culture complaints made by players and parents, including players threatening to leave the university if she remained as coach. The court also granted a positive inference to the university’s stated rationale for termination under the “same actor” doctrine, where in this case the same athletic director that hired the plaintiff was the person who made the decision to fire the plaintiff (Schweyen v. Univ of Montana–Missoula (2023 BL 390525, D. Mont. 9.21-cv-00138, 10/31/23)).

    The prior coach had a compiled 38-year performance of winning 75% of her games, while the plaintiff had only one winning season in the four years she served as head coach. The court rejected the plaintiff’s attempt to compare herself to a men’s basketball coach who had lost team players to transfer, citing multiple federal cases that have rejected arguments that disparate treatment between men’s and women’s sports teams creates an inference of discriminatory animus under Title VII.



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  • Gender-Inclusive HR Strategies: Are You on the Right Track? – CUPA-HR

    Gender-Inclusive HR Strategies: Are You on the Right Track? – CUPA-HR

    by Julie Burrell | November 6, 2023

    This year’s Transgender Awareness Week (November 13-19) is an opportunity for HR pros to educate themselves about daily steps that make a more inclusive workplace. It’s also a good time to review strategies for inclusion for all employees, including those who are transgender, nonbinary, and agender, among other gender identities.

    Small Steps to a More Gender-Inclusive Workplace

    At this year’s CUPA-HR annual conference, Jon Humiston of Central Michigan University laid out some simple actions everyone can take to make gender inclusiveness a daily practice. They suggest:

    • Use gender-inclusive language when referring to groups of people. For example, use terms like “people” or “individuals” rather than “ladies and gentlemen.”
    • Pay attention to the assumptions you make about someone’s gender identity and expression.
    • Feel free to ask about pronouns or share your own pronouns, but don’t require people to share theirs, as they may be uncomfortable doing so.
    • Use “pronouns” rather than “preferred pronouns,” since it’s not a preference but an identity.
    • Do know that it’s OK if you make a mistake by accidentally misgendering someone. Apologize and move on. If you repeatedly make the same mistake, it might be time to practice. Consider using AI, like ChatGPT, as a conversation tool or asking a friend to be a practice buddy.

    HR’s Role in Creating a Gender-Affirming Culture

    Jon also proposed a framework for higher ed HR to review inclusion policies, focusing on transgender and nonbinary employees, but with potential benefits for all employees. Reviewing the following questions (adapted from sources such as CUPA-HR and Out & Equal toolkits) will help you identify potential gaps in your institution’s efforts to be gender inclusive.

    Policies and Procedures

    • Do you have a non-discrimination policy that includes sexual orientation, sex (or biological sex), gender identity and gender expression?
    • Do you allow employees to identify their gender outside of the gender binary?
    • Do you have a name-in-use policy or chosen-name policy that is easy to access and navigate?
    • Do systems such as software allow for gender pronouns to be included?
    • Does health insurance cover benefits for transgender and nonbinary employees?
    • Is gender-inclusive language used in internal and external materials (marketing, job ads, etc.)?
    • Are gender-inclusive bathroom locations shared with all potential employees during the interview process and all new employees?

    Programmatic Support

    • Do you have an Employee Resource Group for LGBTQIA+ employees?
    • Do you have LGBTQIA+ safe-zone training available for all employees?

    Visibility

    • Does your institution publicly show its support of LGBTQIA+ communities during Pride Month, National Coming Out Day, National Day of Silence, etc.?
    • Does your institution publicly address hate and bias crimes that occur, or have a plan for doing so?
    • Does your institution have a presence at local LGBTQIA+ pride events?

    Making the Case for Gender Inclusion

    Policies that support LGBTQIA+ employees benefit all employees.

    For example, flexible work arrangements are desired by two-thirds of the higher ed workforce, yet most campus staff members must work on-site. For employees experiencing misgendering at work or in public, a day working from home might provide them the respite they need from the exhaustion of being misgendered or experiencing gender dysphoria.

    Both job seekers and current employees want their workplace cultures to be inclusive. According to a recent Workhuman study, 72 percent of employees see this as somewhat or very important to them. Inclusive cultures also promote creativity and innovation and may reduce absenteeism.

    Preventing workplace discrimination and harassment is also a matter of regulatory compliance. The EEOC recently published new proposed guidance on preventing workplace harassment, including several examples of discrimination and harassment on the basis of sexual orientation and gender identity.

    The Future of Gender Inclusion

    The makeup of the higher ed workplace won’t look the same in the years to come. The share of Americans who know someone whose gender differs from the sex they were assigned at birth continues to grow, with 44 percent of Americans saying they know someone who is trans and 20 percent saying they know someone who is nonbinary.

    If a major overhaul of institutional inclusion policies isn’t something you’re in a position to initiate, Jon suggests networking with your colleagues at other institutions to provide support, personally recognizing national days of awareness or remembrance, and encouraging allyship.

    Additional Resources

    Gender Identity and Sexual Orientation in the Workplace (CUPA-HR Toolkit)

    Assessing LGBTQI+ Inclusion in Your Workplace (Out & Equal Toolkit)

    A Guide to Gender Identity Terms

    What’s Your Pronoun? Strategies for Inclusion in the Workplace



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  • How to ChatGPT-proof Analysis Assignments –

    How to ChatGPT-proof Analysis Assignments –

    Let’s assume we live in a world in which students are going to use ChatGPT or similar tools on their assignments. (Because we do.) Let’s also assume that when those students start their jobs, they will continue to use ChatGPT or similar tools to complete their jobs. (Because they will.) Is this the end of teaching as we know it? Is this the end of education as we know it? Will we have to accept that robots will think for everyone in the future?

    No. In this post, I’m going to show you one easy solution that solves the problem of assuming students will use generative AI by incorporating it into assessments. Keep in mind this is just a sketch using naked ChatGPT. If we add some scaffolding through software code, we can do better. But we can do surprisingly well right now with what we have.

    The case study

    Suppose I’m teaching a college government class. Here are my goals:

    • I want students to be able to apply legal principles correctly.
    • I want to generate assignments that require students to employ critical thinking even if they’re using something like ChatGPT.
    • I want students to learn to use generative AI appropriately.

    Let’s throw in a couple of more goals just to make it interesting:

    • I want students to demonstrate competencies.
    • I want my assignment to support diversity, equity, and inclusion

    Can we do all that?

    Yes.

    The prompt

    I cobbled together a DEI guidelines document from some existing ones I found on the web to create something focused on curricular materials generation. I gave the document to ChatGPT along with the following prompt, which is designed to provide assessment questions that you can’t answer by just copying/pasting them into ChatGPT:

    You are instructed to generate 3 fictional legal scenarios assessing The following competency:

    • Assess whether a case is likely to be heard by the Supreme Court based on an analysis of the Court’s role and selection criteria.

    Evaluating Court functions will be consistently required.

    For each scenario:

    • Involve 2-3 parties and a dispute requiring application of the competency.
    • Make it sufficiently complex so direct questions don’t fully demonstrate understanding.
    • Generate 1-2 written questions requiring analysis using the competency.

    Additionally, construct a rubric table that is appropriate for all generated scenarios to evaluate student responses on:

    • Mastery of the competency above.
    • Mastery of the competencies below.

    Demonstration of the following additional competencies

    Critical Thinking Competencies

    • Ask probing questions to uncover deeper complexities beneath the surface. Questioning skills are inherently required by the assessment structure.
    • Critically evaluate source credibility and potential biases when analyzing information. Assessing the AI responses is crucial.
    • Produce sound arguments and reasoning to justify conclusions.

    Written justifications will necessitate this in all cases.

    AI Competencies:

    • Formulate questions and direct dialogue with an AI assistant to extract relevant insights. Strategic probing of the AI agent will be essential.
    • Efficiently utilize an AI’s knowledge while evaluating its capabilities and limitations. Critically appraising the AI’s responses is key.

    Rubric should include levels of performance for each competency (e.g. novice, developing, proficient). Apply the attached diversity, equity, and inclusion guidelines to the content you generate. Incorporate diverse litigants and equity-relevant issues in the cases you generate.

    [Note: I’ve added some formatting to make it more legible for humans. The actual prompt doesn’t have bullets and has fewer paragraph breaks.]

    The results

    Here’s what ChatGPT gave me back:

    Legal Scenario 1: The Case of Interstate Water Rights

    Background: The states of Aridonia and Riverbend are locked in a legal battle over the rights to water from the Silver River, which flows from the mountains in Aridonia and through the agricultural heartlands of Riverbend. Aridonia, facing an unprecedented drought, has constructed a series of dams to conserve water, significantly reducing the flow to Riverbend. Riverbend’s economy relies heavily on agriculture, which is now under threat due to the reduced water supply.

    Parties Involved:

    1. The State of Aridonia (Plaintiff)
    2. The State of Riverbend (Defendant)
    3. The Indigenous Tribes of the Silver River Basin (Intervenors) – They claim ancestral rights to the river and are impacted by the actions of both states.

    Complexity: The dispute requires an understanding of interstate commerce, environmental law, and indigenous rights.

    Questions:

    1. Analyze the potential arguments each party could make to justify their claims to the Silver River’s water. How might these arguments impact the likelihood of the Supreme Court electing to hear this case?
    2. Considering the Supreme Court’s historical approach to interstate disputes and the involvement of indigenous rights, what precedents might influence the Court’s decision to grant certiorari?

    Next, I will create the second legal scenario.

    Legal Scenario 2: The Case of Digital Privacy and International Data Exchange

    Background: GlobalTech Inc., a multinational tech company, is sued by the State of Freedonia on behalf of its citizens for allegedly breaching privacy laws. GlobalTech’s data centers, located in various countries, process and store data from Freedonian users. The lawsuit asserts that GlobalTech’s practices violate Freedonia’s stringent digital privacy laws. However, GlobalTech contends that they are compliant with international agreements on data protection to which Freedonia is a signatory.

    Parties Involved:

    1. The State of Freedonia (Plaintiff)
    2. GlobalTech Inc. (Defendant)
    3. International Privacy Watch (Amicus Curiae) – A global coalition advocating for citizens’ digital rights and privacy.

    Complexity: This scenario involves understanding international law, digital privacy, and the implications of cross-border data flow.

    Questions:

    1. Evaluate the competing interests of national privacy law and international data agreements in the context of this case. How might these factors affect the Supreme Court’s decision to grant a writ of certiorari?
    2. What are the broader implications for digital privacy and international commerce if the Supreme Court decides to hear this case? Consider historical precedents and current legal trends in your analysis.

    Now, I’ll proceed to create the third legal scenario.

    Legal Scenario 3: The Case of Health Care Access and Religious Exemptions

    Background: A new federal regulation mandates that all employers, regardless of their religious beliefs, must include comprehensive reproductive health services in their employee health insurance plans. FaithFirst, a nationwide chain of craft stores owned by a family with strong religious convictions, files a lawsuit claiming that this mandate violates their religious freedom. The case is further complicated by the intervention of HealAll, a non-profit organization that provides health services to underprivileged populations and argues that exemptions undermine public health efforts.

    Parties Involved:

    1. FaithFirst (Plaintiff)
    2. The Federal Government (Defendant)
    3. HealAll (Intervenor) – Advocating for universal access to health care.

    Complexity: This scenario encompasses the interpretation of religious freedom, federal regulatory power, and the right to health care.

    Questions:

    1. Assess the potential constitutional conflicts at play in this case, particularly the tension between religious liberty and federal health care mandates. How could these conflicts influence the Supreme Court’s inclination to review the case?
    2. Examine how the Supreme Court’s previous decisions on similar health care and religious freedom cases might be used as precedents in this scenario. What factors might the Court consider in deciding whether to hear this case?

    With the three legal scenarios outlined, it’s time to generate a universal rubric that can be applied to assess student responses for all three scenarios. I will incorporate the DEI guidelines into this rubric to ensure the content is sensitive to diversity, equity, and inclusion.

    Universal Rubric for Assessing Legal Scenario Responses

    The following rubric applies to responses for all three legal scenarios, ensuring that students are assessed on the mastery of core competencies along with critical thinking and AI engagement. The DEI guidelines are integrated to promote inclusive and diverse perspectives in the analysis.

    Competency Novice (1-2) Developing (3-4) Proficient (5-6) Mastery (7)
    Mastery of Supreme Court Case Assessment Displays limited insight into the Supreme Court’s selection criteria. Overlooks DEI considerations. Recognizes basic aspects of the Court’s role and criteria. Considers DEI aspects superficially. Analyzes the Court’s criteria proficiently, integrating DEI considerations into the analysis. Provides comprehensive analysis aligned with the Court’s criteria and DEI principles, demonstrating nuanced understanding.
    Critical Thinking: Questioning Questions lack depth and do not reflect DEI considerations. Questions show an emerging understanding of complexities with some DEI awareness. Probes complex issues effectively with questions that incorporate DEI awareness. Asks deep, insightful questions that fully embrace DEI complexities within the legal context.
    Critical Thinking: Source Evaluation Shows limited ability to evaluate credibility and biases, with minimal DEI consideration. Begins to critique sources for credibility and bias, showing awareness of DEI. Evaluates sources critically for credibility and biases with a consistent DEI lens. Expertly discerns source credibility and biases, thoroughly applying a DEI perspective.
    Critical Thinking: Argumentation Constructs basic arguments, lacking DEI perspective and strong justification. Develops arguments that begin to incorporate DEI considerations with moderate justification. Presents well-reasoned arguments that effectively integrate DEI considerations. Articulates complex arguments, richly informed by DEI considerations and robust justification.
    AI Competency: Question Formulation Struggles to craft questions that effectively leverage AI insights, with little DEI focus. Formulates questions that occasionally lead to relevant AI insights and consider DEI. Consistently crafts questions that elicit useful AI insights, mindful of DEI implications. Strategically formulates questions for AI that yield comprehensive insights, fully integrating DEI.
    AI Competency: AI Utilization Demonstrates limited understanding of AI capabilities and DEI considerations. Shows basic awareness of AI’s capabilities and DEI considerations in utilization. Utilizes AI effectively, with a clear understanding of its capabilities and DEI considerations. Masters AI utilization, skillfully balancing its capabilities with DEI awareness in analysis.

    The performance levels for each competency are:

    • Novice: Limited application and understanding
    • Developing: Basic to moderate application and understanding with some inconsistencies
    • Proficient: Consistent application and understanding with minor gaps
    • Mastery: Exceptional application and understanding with insightful integration

    These are challenging assignments. You’d need to precede them with lower-level formative assessments to ensure they’re getting the foundational concepts and know what kinds of questions to ask the generative AI.

    Which is exactly what you should be doing before any challenging competency application assessment anyway.

    I asked ChatGPT to generate three examples, but I just as easily could have asked it to generate 300. In other words, you can create a test bank if that’s what you need. Or just generate them on demand every term. You’d want a human expert to tweak the rubric and review each assignment; it’s a bit more complex and error-prone than algorithmic math problem generators.

    Grading the assignment

    The key here is that the assignment students turn in is the ChatGPT transcript. (You can optionally have them submit their final analysis work product separately.) The students are, in effect, showing their work. They can’t use ChatGPT to “cheat” because (1) ChatGPT is part of the assignment, and (2) the assignment is designed such that students can’t just plug in the questions and have the AI give them the answer. Their ability to analyze the problem using the new tool is what you are evaluating.

    You could use your generative AI here too as a TA. Give it the assignment and the rubric. Write a prompt asking it to suggest scores and cite evidence from the student’s work. You can decide how heavily you want to lean on the software’s advice, but at least you can get it.

    Learning to think like a lawyer (or whatever)

    Generative AI does not have to kill critical thinking skills. Quite the opposite. These assignments are much farther up on Bloom’s taxonomy than multiple-choice questions and such. Plus, they get students to show their thought work.

    In fact, these scenarios are highly reminiscent of how I use generative AI every day. Here is a sampling of tasks I’ve performed over the last several months using ChatGPT and other generative AI that I probably couldn’t have—and definitely wouldn’t have—performed without them:

    • Analyzed the five-year performance of a business based on its tax returns and developed benchmarks to evaluate the quality of its growth
    • Cloned a Github source code repository, installed Docker and other needed tools on my laptop, and ran the Docker image locally
    • Analyzed and hedged the risk to my retirement fund portfolio based on technical and economic indicators
    • Wrote the generative AI prompt that is the centerpiece of this post

    None of these scenarios were “one and done,” where I asked the question and got the answer I wanted. In all cases, I had to think of the right question, test different variations, ask follow-up questions, and tease out implications using generative AI as a partner. I didn’t have to learn accounting and business analyst but I did have to know enough about how both think to ask the right question, draw inferences from the answer, and then formulate follow-up questions.

    To score well on these assessments, I have to demonstrate both an understanding of the legal principles and the ability to think through complex problems.

    Critical thinking competencies

    Ethan Mollick, a professor at the Wharton School of Business who writes prolifically and insightfully about generative AI, wrote an excellent analogy for how to think about these tools:

    The thing about LLMs that make them unintuitive is that analogizing them to having a science fiction AI is less useful than thinking of them as infinite copies of some guy named Steve, a first year grad student who is great at coding & art and is widely-read, but makes up stuff based on what he remembers when he is pressed.

    Asking AI to do things an incredibly fast Steve couldn’t do is going to lead to disappointment, but there is a lot of value in Steve-on-demand.

    Ethan Mollick’s LinkedIn post

    This is a great analogy. When I was analyzing the tax returns of the business, I didn’t have to understand all the line items. But I did have to know how to ask Steve for the important information. Steve doesn’t understand all the intricacies of this business, its context, or my purpose. I could explain these things to him, but he’d still just be Steve. He has limits. I had to ask him the right questions. I had to provide relevant information that wasn’t on the internet and that Steve couldn’t know about. I used Steve the way I would use a good accountant whose help I need to analyze the overall quality of a business.

    Coming up with benchmarks to measure the business against its industry was even more challenging because the macroeconomic data I needed was not readily available. I had to gather it from various sources, evaluate the quality of these sources, come up with a relevant metric we could estimate, and apply it to the business in question.

    In other words, I had to understand accounting and economics enough to ask an accountant and an economist the right questions and apply their answers to my complex problem. I also had to use critical thinking skills. Steve could help me with these challenges, but I ultimately had to think through the problem to ask Steve for the kind of help he could give me.

    When you’re teaching students using a generative AI like ChatGPT, you should be teaching them how to work with Steve. And as bright as Steve may be, your student still has much she can contribute to the team.

    Generative AI competencies

    Suppose you have a circle of intelligent friends. Steve is brilliant. He has a mind like an engineer, which can be good or bad. Sometimes, he assumes you know more than you do or gives you too short an answer to be helpful. Also, he’s been focused night and day on his dissertation for the last two years and doesn’t know what’s been happening in the real world lately. He’ll do a quick internet search for you if it helps the conversation, but he’s not tuned in.

    Your friend Claude thinks like a Classics graduate student. He’s philosophical. He pays close attention to the nuances of your question and tends to give longer answers. He also has a longer attention span. He’s the kind of friend you talk with late into the night about things. He’s somewhat more aware of current events but is also a bit tuned out of the latest happenings. He can be analytical, but he’s more of a word guy than Steve.

    Then there’s your friend Anna. Anna Bard. She’s not quite as sharp as either Steve or Claude, but, as an international finance graduate student, she reads everything that’s happening now. If you need to have an in-depth conversation on anything that’s happened in the last two years, Anna is often the person to go to.

    Also, all of these friends being young academics in training, they’re not very good at saying “I don’t know” or “I’m not sure.” They’re supposed to be the smartest people in the room, and they very often are. So, they’re not very self-aware of their limitations sometimes. All three of my friends have “remembered” studies or other citations that don’t exist.

    And each has their quirks. Claude has a strong sense of ethics, which can be good and bad. I once asked him to modify a chapter of an OER book for me. I gave him the front matter so that he could see the Creative Commons licensing was there. He told me he couldn’t do the work unless he could see the whole book to verify that it was ethically OK to modify the content.

    I told him, “Claude, that book is 700 pages. Even you don’t have the attention span to read that much.”

    He told me, “You’re right. In that case, I’m sorry, but I can’t help you.”

    So I took the chapter to Steve, who had no ethical qualms at all but only skimmed the chapter and lost interest about halfway through my project.

    When I do my work, I have to figure out which of my AI colleagues can help me and when to trust them. For the business model analysis, Steve answered most of my questions, but I had to get him some information from my friends who haven’t been locked in the library for the past two years. I asked both Anna and Claude. They were somewhat different from each other, both of which were well-reasoned. I had to do some of my own Googling to help me synthesize the analyses of my two friends, develop my own opinion, and bring it back to Steve so he could help me finish the work.

    For the software project, surprisingly, Steve was useless. He assumed I knew more than I did despite my asking him several times to simplify and slow down. Also, the software had changed since he last looked at it. While he tried to make up for it by saying, “Look for a menu item labeled something like ‘X’ or ‘Y’,” he just couldn’t walk me through it. Anna, on the other hand, did a superb job. She knew the latest versions of all the software. She could adjust when I had trouble and needed some extra explanation. While I wouldn’t have guessed that Anna is the better co-worker for that type of task, I am learning how to get the most out of my team.

    Generated by DALL-E 3

    For the design of the prompt at the heart of this post, I went to Claude first to think through the nuances of the competency and the task. Then, I brought the summary I created with Claude to Steve, who sharpened it up and constructed the prompt. And yet, it still could use improvement. I can ask my friends for more help, but I will need to think through what to ask them.

    My retirement portfolio analysis was 90% Anna’s work since she’s been following the market and economic conditions. I asked Steve to give me a second opinion on bits of her analytic approach. But mostly I relied on Anna.

    We often say that we must teach students how to collaborate in teams since they will probably have to collaborate in their jobs. Teaching students how to use generative AI models is an overlapping skill. And it’s only going to get more critical as models proliferate.

    I have a model called Mistral running on my laptop right now. That’s right. It’s running locally on my laptop. No internet connection is required. I don’t need to share my data with some big cloud company. And I don’t need to pay for the usage.

    My subjective experience is that Mistral is generally more competent than GPT-3 but not as smart as ChatGPT-3.5 Turbo. However, according to one calculation, Mistral is 187 times cheaper to run than GPT-4. It’s also relatively easy and affordable to fine-tune, which is a bit like sending her out to earn a MicroMasters in a particular subject.

    Let’s suppose I’m a building site engineer for net-zero buildings in Nova Scotia. I have to know all the details of the building codes at the municipal, township, provincial, and national levels that apply to any given project. Since I’m using new building technologies and techniques, I may have to think through how to get a particular approach accepted by the local building inspector. Or find an alternative approach. And very often, I’ll be out in the field without any internet connection. Mistral may not be as smart at questions about macroeconomics or software development as Steve, Claude, and Anna, but she’s smart enough to help me with my job.

    If I were running that construction company, I would hire Mistral over the others and pay for her MicroMasters. So I have to know how to evaluate her against other potential synthetic employees I could employ. Choosing Steve would be like hiring a Harvard-educated remote-working external consultant. That’s not what I need.

    Fear not

    Personally speaking, my daily use of generative AI hasn’t made me dumber or lazier. Sure, it’s saved me a lot of work. But it’s also enabled me to do work that was beyond my reach before. It feels a little like when Google first came out. If I’m curious about something, I can explore it instantly, any time I want, and go as deep as I want.

    In fact, generative AI has made me a better learner because I’m fearless now. “Can’t” isn’t a viable starting assumption anymore. “Oh, I can’t analyze tax returns.” That answer doesn’t cut it when I have an Ivy League accounting MBA student available to me at all times. I need to know which financial questions to ask and what to do with the answers. But if I don’t at least try to solve a problem that’s bugging me, I feel like I’m copping out. I almost can’t not try to figure it out. The question won’t leave me alone.

    Isn’t that what we want learning to feel like all the time?

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  • NLRB Issues Joint Employer Final Rule – CUPA-HR

    NLRB Issues Joint Employer Final Rule – CUPA-HR

    by CUPA-HR | October 27, 2023

    On October 26, 2023, the National Labor Relations Board (NLRB) released its final rule amending the standard for determining joint employer status under the National Labor Relations Act (NLRA). The rule replaces the board’s 2020 final rule on the same issue and greatly expands joint employer status under the NLRA.

    The final rule establishes joint employer status of two or more employers if they “share or co-determine those matters governing employees’ essential terms and conditions of employment,” such as wages, benefits and other compensation; work and scheduling; hiring and discharge; discipline; workplace health and safety; supervision; and assignment and work rules. Today’s final rule finds that either indirect control or reserved control may stand alone as sufficient for finding that a joint employer relationship exists. The final rule specifically states that an entity may be considered a joint employer if it possesses the authority to control one or more essential terms and conditions of employment, regardless of whether that authority is exercised, or if it exercises the power to indirectly control one or more terms and conditions of employment, regardless of whether that power is exercised directly. This is a departure from the 2020 rule, which found that an entity must exercise substantial direct and immediate control over essential terms and conditions of employment to be considered a joint employer.

    Joint employment has recently been a focal point for higher ed institutions as disputes around the worker classification of student-athletes continue. Last year, an NLRB regional office announced it would be pursuing a complaint by a student-athlete advocacy group that filed an unfair labor practice charge against that the University of Southern California, the Pac-12 Conference, and the NCAA, alleging that the three entities are joint employers who violated the NLRA by “repeatedly misclassifying employees as ‘student-athlete’ non-employees.” The case is set to be heard by an administrative law judge in November, but a final decision could take years to come to fruition.

    This final rule could have significant implications for private institutions, as they fall under the NLRB’s jurisdiction. Public institutions are not impacted by this rulemaking, as the NLRB does not have jurisdiction over public entities.

    CUPA-HR is assessing the final rule and will provide members with more information as it becomes available.



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