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Applying Human Information Behavior & Technology Perceptions: To Enhance Library Educational Instruction
Full text linkThe poster will highlight research on human information behavior and how understanding human perceptions of technology and how the public uses it can help libraries improve public services and instruction. The poster will display data using charts and graphs so that different demographic data is represented visually and in an easy-to-digest way. The presentation will demonstrate how, by understanding library patrons\u27 perceptions of technology, librarians will be able to curate instruction to meet library users and community members where they are. It will also help librarians to better understand why patrons search the way they do and how to improve digital literacy. The presentation will also show how librarians can leverage the data and knowledge to better inform communities and institutions. Learning Objectives: Describe public perceptions of emerging and existing technology. Demonstrate how understanding public perceptions and information behavior can be harnessed to improve instruction, reference, and public services. Define different methods of applying research results to individual libraries
Doing Business on Indian Reservations: Tribal Business Owners\u27 Perspectives on Entrepreneurship
Full text linkThis paper reports insights from focus group discussions from Montana reservations, which provide insights and perspectives on the experiences of Native American entrepreneurs. The narratives from these discussions highlight the challenges the current regulatory context poses for entrepreneurial activities and their communities. The participants spoke of regulatory unpredictability and inefficiency, resulting in uncertainty faced by business owners and thus not impeding economic prosperity. The discussions also revealed a disconnect between tribal governments and community members, characterized by a lack of transparency and accountability. Judicial independence emerged as another significant concern. These insights from the focus groups suggest a path forward for more economic growth and prosperity for reservation communities
Building Fair and Inclusive AI for a Diverse World
No full textChaired by Gina Sprint, Ph.D. (Gonzaga University)
Large Language Models (LLMs) can impressively generate human-like text, translate languages, write different kinds of creative content, and answer your questions in an informative way. They are rapidly becoming integrated into every facet of our digital lives, powering our search engines, social media platforms, and even customer service interactions. But amidst the excitement and hype surrounding LLMs, we must pause and ask a critical question: do they fairly represent the diversity of human language, culture, and experiences?
This talk focuses on the intricate landscape of building truly inclusive LLMs. It’s not just about removing harmful stereotypes or ensuring equal representation across demographic categories; it’s about creating language technology that genuinely understands and respects the multifaceted nature of human communication. One of the key challenges in building inclusive LLMs lies in addressing the inherent biases that can creep into these systems. LLMs are trained on massive datasets of text and code, which often reflect the biases and prejudices prevalent in society. As a result, LLMs can inadvertently perpetuate and even amplify these biases, leading to unfair or discriminatory outcomes. Tackling this challenge requires a multi-pronged approach, encompassing technical innovations, ethical considerations, and a commitment to engaging with diverse communities and stakeholders.
Our journey towards inclusive LLMs begins with a critical examination of social media, a vibrant and dynamic space where a kaleidoscope of voices converge. Social media platforms have become fertile ground for the expression of diverse perspectives, representing a multitude of languages, dialects, and cultural backgrounds. However, this abundance of information can also be overwhelming, making it difficult to extract meaningful insights and understand the nuances of different communities. Automatic text summarization offers a potential solution, enabling us to condense large volumes of social media content while preserving salient information. But how can we ensure that these summaries truly capture the diversity of voices and perspectives present in the data? To address this question, we introduce DivSumm, a carefully crafted dataset comprising dialect-diverse tweets from African American English, Hispanic-aligned Language, and White-aligned Language communities paired with corresponding human-written summaries. An empirical analysis of these reference summaries reveals that human annotators are able to construct well-balanced and inclusive representations, whereas system-generated summaries tend to exhibit dialect bias, disproportionately amplifying certain linguistic patterns while diminishing others. A promising technique for mitigating this issue is cluster-based preprocessing, which enhances dialectal fairness without adversely impacting summarization quality, thereby presenting a scalable intervention for fairness-aware summarization.
Our exploration of dialect diversity leads us further, questioning whether simply including a variety of dialects is sufficient to ensure fair representation. We find the subtle yet pervasive influence of position bias, a phenomenon where the order in which input documents are presented to the LLM can significantly impact the fairness of summarization outputs. Even when the textual quality of the summary remains consistent, position bias can lead to disproportionate representation or omission of certain groups, depending on their position in the input sequence. This finding motivates the development of innovative methods that can effectively mitigate the influence of position bias, ensuring that all voices are heard and valued, regardless of their position in the input.
Recognizing the challenges of achieving fair and balanced representation in LLM-generated summaries, we introduce two novel methods specifically designed for fair summarization: FairExtract and FairGPT. FairExtract employs a clustering-based approach, grouping similar tweets together before extracting sentences for the summary. This method ensures that diverse perspectives are included by selecting sentences from different clusters, promoting a more balanced representation of the input data. FairGPT, on the other hand, leverages the power of LLM with carefully designed fairness constraints. Through rigorous evaluation on the DivSumm dataset, we demonstrate that both FairExtract and FairGPT achieve superior fairness compared to existing approaches while maintaining competitive summarization quality.
While fairness in text summarization is a critical concern, bias in LLM-generated narratives presents broader challenges. To investigate this, we analyse gender and ethnicity representation in AI-generated occupational narratives across 25 professional fields. Using Llama, Claude, and GPT-4.0, we evaluate how LLMs associate demographic groups with specific professions, revealing systematic disparities in representation. For instance, LLMs may disproportionately associate women with caregiving roles while attributing technical and leadership positions to men, thus perpetuating existing biases. To mitigate these biases, we propose explainability-driven fairness constraints in LLMs, aligning model transparency with demographic fairness. By integrating feedback mechanisms that adjust model outputs, we reduce demographic disparities in occupational narratives by 2%–20% across different job categories.
As AI systems increasingly mediate human interactions, professional opportunities, and knowledge dissemination, fairness in NLP applications is no longer an abstract concern – it is an ethical imperative. Throughout this journey, we have encountered complex challenges and difficult questions. How can we ensure that AI respects linguistic and cultural diversity? How do we uncover and address hidden biases in LLMs? How do we balance powerful AI capabilities with ethical responsibility? These are questions that demand a collective effort. Researchers, developers, policymakers, and everyday users must collaborate to build AI technologies that truly benefit everyone.
In conclusion, building truly inclusive AI is not just a technical challenge but a social and ethical imperative, requiring a concerted effort from all stakeholders. By addressing biases in training data, developing fairness-aware algorithms, and engaging in open and transparent dialogue about the ethical implications of LLMs, we can create language technology that empowers and benefits everyone
Value Inherence, Abductive Reasoning, and Building Machine Learning Models that Reflect Ethical Decision Making
No full textChaired by Logan Axon, Ph.D. (Gonzaga University)
Historically, businesses have had a Code of Conduct, and, in some cases, a Code of Ethics to guide employee internal and external behavior. When employees are faced with an ethical dilemma or an ambiguous situation not covered in the Code of Conduct, the principles articulated in the Code of Ethics help guide the decision maker. A classic example of ambiguity is in the use and applicability of non-disclosure agreements. A Code of Conduct rule might state “employees must not disclose confidential company information to outside parties” with the implication that this hold true even if it is related to potential unethical practices. By contrast, a Code of Ethics value might be a company is “committed to transparency and honesty in all business dealings.” To resolve the conflict between the rule and the value, an employee will need to reason to a conclusion.
The differences between Codes of Conduct and Ethics has historically not been that important when it comes to the actual creation of products, as products have tended to manifest a quality of Value Adherence or Value Adjacency. Value Adherence is the idea that the product as sold reflects a Code of Conduct or Ethics (e.g., “The customer is always right”), but itself does not contain the rule or value of a code. For example, the customer may always be right, but this does not impact the quality of the hamburger they have purchased. Value Adjacency is the idea that the product as sold and the rules or values do not overlap. Enron has a Code of Ethics, but this did not stop it from engaging in fraud.
In contrast to the concepts of Value Adherence and Value Adjacency, is the concept Value Inherence. With Value Inherence, the company’s (and its employees) values are directly incorporated into its product as part of the product itself. This is what makes ML based products different from the historical relationship between products and rules, values. The phenomena of Value Inherence raises the following question: i) Why is there Value Inherence?, and ii) How do we ensure that a company’s values are reflected in its ML based products?
To understand Value Inherence, one needs to first understand bias as a technical feature of ML. Bias is the “difference between this [an] estimators [ML Model] expected value and the true value of the parameters being estimated”. Further, “Any discussion of bias depends on the unknown true function”. Much of ML is dedicated to minimizing technical bias and finding the true function that fits the data (facts), or a desired outcome. Example strategies for solving technical bias include: a) Data preparation (e.g., Normalization Techniques, Data Labelling); or b) Model Training (e.g., Data Penalty Algorithms). However, these strategies can also facilitate bias because of the quality of Value Inherence where the ML’s expected value does not reflect the data (facts), or the desired societal outcome. A potential cause of this bias is ambiguity around the facts or the desired outcome used to resolve technical bias. In short, bias is part of the ML product itself, which is opposed to being adherent or adjacent as was historically the case with rules or values expressed as part of a Code of Conduct or Ethics.
One possible solution to bias and Value Inherence is a rules based approach in the form of updating a Code of Conduct with rules regarding Data Preparation, Model Training etc. Such an approach, however, shows the limits of rules. Specifically, rules break down and don’t generalize well given the complexities of the world. For example, the rule “All Birds Fly except Penguins, Ostriches, birds with broken wings and so on and so on” is replete with exceptions and counterfactuals. Rules based ethical systems suffer from the same problems. (See e.g., “Never tell a lie” v. the “Murder at door example; “Act to facilitate the greatest good for the greatest number” v. what is the greatest good, and for whom such that-“goods” of one group v. another).
The underlying problem is that rules based approaches don’t generalize well given the complexities of the world. Rules don’t generalize well because a rule is typically created from a previously seen example, and are meant to be applied to the same or similar factual scenarios. The complexity of the world requires that there will almost always be new, unanticipated factual scenarios. Bias may arise where facts or outcomes are not defined or well understood, hence if facts or outcomes are not known, then it may not be clear which rule (if any) applies.
Another possible solution for Value Inherence and bias is Abductive Reasoning. Abductive Reasoning is where one reasons to the most likely conclusion from a known set of facts. Abductive reasoning does well in complex problem spaces as it can adjust approaches to problem solving based upon observed, changing facts and provide a best guess as to an outcome (i.e., one with the highest probability). Further, Abduction allows for flexibility to approximate a true function even where facts and outcomes may be unclear which may result in bias. In abduction, one acts to facilitate the best guess as to the facts or facilitate a desired outcome as a prophylactic against bias (a “Best Judgment Approach”).
In a Best Judgment Approach, employees should be exposed to scenarios where the facts and desired outcomes are unclear. Where facts or outcomes are unclear, employees will need to develop multiple possible explanations, and use their best judgment to identify the most likely facts supporting a desired outcome. For example, “How to label an image to use to train a model where the image does (or does not) depict consensual sexual relations?” A solution is to train the labeler on a number of different scenarios that require the application of the Code of Ethics to render a labelling decision (a.k.a. using best judgment to render a decision)
Empowering Women and Broadening Participation in STEM through a Co-mentoring Community
No full textThe Women in STEM Education Network (WiSEN) is a pilot co-mentoring network that was created to provide a space for women to connect and network across multiple STEM majors at Gonzaga University and three other institutions of higher learning around the country. This co-mentoring network provides women a venue to have critical conversations about opportunities and challenges in STEM, celebrate successes, share experiences, and examine ways to broaden participation in STEM. By having a space where women can authentically share their unique experiences in STEM with others, WiSEN offers critical infrastructure to create a sense of belonging, enhance perseverance, and imbed cultural wealth into practices and identities as scientists and STEM professionals. WiSEN offers an additional model of mentoring that exists beyond the traditional top-down approach, to create a network of support where women can thrive in STEM. Our intent is to continue to grow WiSEN so that this network of support will flourish as a means to empower the future of STEM by representing and connecting a diverse network of multidisciplinary and multicultural women in STEM at Gonzaga and beyond
How to Create a Distinctly Native Journal
Full text linkIn this essay, I describe three characteristics that Indigenous Business and Public Administration should aim to develop to be a distinctly Native journal. The journal should not be contained by dominant stereotypes of Native Americans. It should reflect the adaptability and innovation that Native Americans have shown in surviving the continuing colonial endeavor to erase us. Finally, the relationships between members of this community should be prioritized at least as much as the content. I developed these characteristics by reflecting on my experiences in higher education along with the ideas of other Native scholars
Proceedings of the Value and Responsibility in AI Technologies Conference, 3-4 April 2025
Full text linkThis conference brings together scholars and thinkers from academia and industry to examine and discuss these ethical issues and to work towards solutions on how to design value-laden AI and digital technologies and ensure that they are developed responsibly. Through a diverse range of issues, participants at the conference will dive deep into the ethical aspects of AI technologies, sharing results and proposing solutions to address the ethical problems that such technologies can pose, while recognizing that these technologies have immense power to shape our lives and society for the better. In this way, AI technologies can help us to serve the common good and uphold principles of democracy, equity, fairness, social justice, human dignity, and care for the planet.
See recordings of presentations at the conference\u27s repository homepage.https://repository.gonzaga.edu/texts/1000/thumbnail.jp
The Impact of a Lack of Gender and Orientation Diversity in K-12 Schools
No full textThis poster incorporates the personal experiences of queer Gonzaga students from their K-12 education and centers these experiences in order to base our research to find solutions and improvements on the topic of queerness in schools
Prediabetes in Rural Primary Care: Screening, Diagnosis and Treatment
Full text linkBackground
Prediabetes affects one in three US adults. Over 80% of individuals with prediabetes are unaware of having the condition. Evidence-based clinical practice guidelines (CPG) recommend screening adults 35 to 70 years old with a BMI of 25 or higher. Prediabetes treatment consists of lifestyle modification, referrals to nutrition counseling, and metformin for individuals who cannot make lifestyle changes. Diagnosis and treatment of prediabetes reduces the risks or delays the progression to type 2 diabetes.
Purpose
The purpose of this DNP quality improvement project was to impact primary care providers beliefs, knowledge, and practices in identifying, diagnosing, and treating prediabetes.
Methods
The study design was retrospective and descriptive incorporating prediabetes CPG education to a convenience sample with pre- and post-education provider surveys and chart audits.
Results/Analysis
Pre- and post-education provider surveys demonstrated positive beliefs for prediabetes care with variable knowledge of recommended treatment. Six weeks of chart audits pre- and post-education found a significant increase in screening using chi-square analysis (p=0.0024). A chart review indicated percentage of missed diagnosis for prediabetes increased post-education, reaching 88.9% compared to 84.71% pre-education. The pre- and post-education treatment group did not meet the projected sample size to determine statistical significance.
Implications for practice
Prediabetes CPG education improved prediabetes screening. Despite increased screening, however, prediabetes diagnosis was frequently missed, and no documentation of treatment was discovered in chart reviews. Further research is essential to identify barriers to screening, diagnosing, and treatment of prediabetes in primary care