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Security for Everyone: Disability, Empathy, and Intersectionality in Cybersecurity
No full textChaired by Aaron Crandall, Ph.D. (Gonzaga University)
In this talk, I will draw on 20 years of experience in the tech industry and a decade doing novel security research. The goal of this talk is to highlight some blind spots in the broader security industry using concrete examples as well as discussion of underserved threat models. I will use both high-level discussion of at-risk parties who are underserved by the current security community and concrete examples of threats that uniquely impact these individuals. I will discuss the root cause of overlapping areas of underinvestment and how intersectionality leads to compounding of this issue. I will end with a discussion of the opportunities for AI to help remediate this issue as well as some potential pitfalls that we as a community need to be aware of when applying AI to security for these underserved communities.
I will start the talk with an overview of a key analysis technique used by security professionals, called threat modeling. Threat modeling provides a systematic path for turning abstract ideas about possible risks or concerns into detailed threats to be mitigated or accepted. This allows security practitioners to focus on the threats that are the most serious or that will provide the most efficient risk reduction for the resources invested.
Threat models, however, are not universal. They depend on subjective risk appetites, differing circumstances of subjects or organizations, and the presence of different kinds of threat actors. We summarize this observation in the industry maxim “your threat model is not my threat model”.
This means that threat models for underserved communities may not get the recognition or resources that are required to effectively mitigate them. This problem is exacerbated by the fact that often these areas of need are underfunded along multiple dimensions. Take, for instance, the needs of people who are blind or have low vision. Accessibility is an area that is already significantly underfunded in many cases. The overlap of this funding with the resources available to security further reduces the pool available to address elements of the blind person’s threat model that are unique and not shared with the wider populace.
Now consider other intersectional areas that might impact a person’s threat model. Their cultural or geo-political context, their refugee or immigration status, their ethnic background or perceived racial identity, their sexuality, etc. All of these elements increase the complexity of their threat model and reduce the pool of resources that society has available to address the intersection of these traits.
In order to solidify this point, I’ll be using one concrete example of homophone attacks. These are attacks in which “sound alike” text is used to trick users of screen readers (typically blind or low-vision folks) into taking an action that isn’t in their best interest or that they wouldn’t choose themselves. (E.g. social engineering or phishing attacks attempting to steal credentials). I’ll then explore ways that this could intersect with other aspects of identity to further target marginalized users.
After that, I’ll briefly explore a few other notable threat models that may interact with this system, including victims of intimate partner violence, children of abusive parents, sex workers, and drug addicts.
Finally I will pivot to the promise and peril of AI for all of these issues. The promise of AI lies in connecting people with resources and with helping them gather information to assess their own threat models. It can also tear down barriers that might keep users from being unable to protect themselves, such as by providing easy machine translation for security tooling, or giving tailored advice on the best mitigations for specific threats.
The peril of AI is that it might retrench existing inequities in how resources are allocated to threats. If security-conscious AIs or tools containing models are trained only on the dominant group’s threat model, it might deepen the divide that minority groups have to bridge in order to get their security needs met
Effect of Diet on Women\u27s Health
No full textResearch Question: How does nutrition affect the physical and mental health of childbearing aged women
A Collection of Greenleaf\u27s Works
No full textA collection of works written by Robert K. Greenleaf and compiled and edited by Dr. Jiying Song
Wellness for Student Leaders: Peer Health Educators’ Approach to the Eight Dimensions of Well-Being
No full textBreakout Session #1Student leaders play an important role in holding conversations around health and wellness on a college campus. Knowing about the Eight Dimensions of Well-Being—social, environmental, spiritual, financial, physical, emotional, intellectual, and occupational—and understanding how they impact people that leaders serve is imperative. It is also critical that student leaders are informed about these eight dimensions for endorsing their own wellness. Supporting and leading others can be a daunting task that demands a lot of people, especially student leaders. Peer Health Educators encourage student leaders to invest in strategies to develop self-care and resiliency. Informed student leaders take on the challenges presented to them head-on, but they also know when to take a step back, examine their own role in the task at hand, and evaluate if they need a break or turn to others for further support. We plan to share the Eight Dimensions of Well-Being with student leaders, emphasizing the importance of taking care of oneself to build resiliency and continue leading important community-building work. We also will share strategies to tend to the needs of the dimensions, facilitating conversations about getting in touch with what student leaders need in and out of their roles, ways that wellness can be realized within leadership positions
Knowing What I Know Now… a Panel of Undergraduate Students Sharing Wisdom, Stories, and Insights
No full textBreakout Session #2Some things are meant to be learned by doing it yourself. But, not all of it. In this session, you will meet seasoned students who have been involved in a variety of leadership spaces. They will engage in a conversational style panel to share with attendees lessons and experiences that might help you avoid mistakes and pitfalls to achieve success and change. You might also be inspired by the ways that these peer leaders have found and cultivated their hope in times of change and uncertainty.Presenters to be announced
Keynote: Whose Ghost in the Machine? AI, Critical Theory and Democracy
No full textChaired by Kirk Besmer, Ph.D. (Gonzaga University)
AI has quickly become synonymous in global discussions with power. AI dominance, whether in the US’s trillion-dollar tech firms, in China’s 14th Five-Year Plan, or in the EU’s AI Act is seen by global power players as the key to political dominance. China’s revelation of DeepSeek sent panics through the American AI ecosystem precisely because it disrupted carefully maintained strategies to prevent Chinese AI prominence through hardware and data embargoes.
In the midst of the power-jockeying of centi-billionaires and heads of state, AI’s presence has mainly been imposed on the masses. Aside from cases detailed by scholars like Virginia Eubanks, Ruha Benjamin and Cathy O’Neil, who emphasize how algorithms are routinely used to penalize marginalized groups merely for being marginalized, less noxious cases still indicate AI being thrust upon us regardless of its benefit. Education is one of the more apparent arenas for this: instructors have to combat abuses of LLMs facilitating disengagement while being told we need to prepare students for the future when they will be expected to use AI.
What would it be, then, to subject AI to democratic interests, to make the AI future not one dictated by technocrats but attentive to genuine human interests? In the terminology of philosopher Andrew Feenberg, this would constitute the creation and use of a “democratic” AI over the current hegemonic model. A starting place will be the participation of the general population, especially less-technologically inclined, in the development and aims of AI. Crowdsourcing, for example, offers one solution where lay persons can contribute by writing code, tagging data, creating new data sets, or just providing feedback. Other intentional “democratic” strategies like Decidim or IE University’s AI4Democracy incorporate AI into politics to better promote democratic participation.
But is this a sufficient account of democratic interests? Safiya Noble notes that algorithms trained on mass data sets do not guarantee a clear sense of “democratic” as much as they reinforce biases rampant in a given society. Thus, scholars and activists have highlighted the dangers of AI employed in government functions, including predictive policing, recidivism prediction, facial recognition, automated welfare systems, and more. Without critical voices, AI becomes a substitute for democratic action, offloading the task of political negotiation and overriding people’s rights.
Thus, a critical component in the consideration of democratic AI is the intent behind the project. Critical Theory of Technology philosopher Andrew Feenberg contends that technologies are created with underlying “technical codes,” that they have some intended aim supporting and promoting certain values. Technologies do not exist in a vacuum, but rather within societies, where they are associated with and directed toward certain aims.
The rapid growth in AI in the past decade, after a long winter, is clearly tied to large tech firms’ promotion of AI research for increased profit. The hope in AI lies in a larger trend of digital capitalism, a movement away from earlier forms of capitalism into one where digital technologies are pivotal for capital growth. Thus, consulting firms like McKinsey and PWC predict trillions of dollars in the global economy tied to AI, WEF predicts massive job restructuring because of AI, and China plans their future economic growth through AI dominance. Most AI projects, then, are contextualized by a digital capitalist technical code, even those in the semi-controlled economy of China.
Feenberg’s proposal for democratic technology entails enshrining democratic technical codes. This will entail, for example, smaller coalitions developing technologies, and promotion of individual and social needs over profit. Thus, a democratic AI would be one that is no longer directed toward a consolidation of power, maximizing profit (or minimizing costs), or promoting a singular dominant ideology.
Feenberg’s approach lines up surprisingly well with liberation theology in the Catholic tradition. A parallel between Feenberg’s hegemonic and democratic technical codes can be found in what Jon Sobrino calls the civilizations of wealth and poverty. The civilization of wealth is directed to profit and individual liberty; it allows free market exploitation of labor and nature for the sake of capitalist notions of “progress.” The civilization of poverty, on the other hand, promotes human rights and collective well-being over technological or economic dominance.
Thus, a possibly more radical, though surprisingly more Christian, approach to democratic AI would be to adopt the “option for the poor (and marginalized).” Rather than negotiating between disparate participant interests, especially insofar as some in the hegemony will inevitably only ever act on bad faith, the guarantee of the worst off will be a way to ensure that all have (some) interests met.
Inevitably, this option leads to some significant challenges. First, there is the concern of why anyone would prefer the option for the poor rather than the logic of domination. From a philosophical perspective, one can suggest a number of arguments, such as Rawls’s maximin position, Habermas’s discourse ethics, or various critical theory approaches. But, in true Nietzschean fashion, the powerful will likely not cede their own vested position. Among Christians, as is true for most religions, there is strong doctrinal justification for prioritizing the weak, but ultimately, like all movements for liberation of the past century, the truth of this perspective will be a hard fight.
Second, there is the challenge of implementing this in the design of AI. Tech firms demand returns on investments to justify their projects. Designing AI to redistribute resources and power to benefit the worst off is unlikely to meet the approval of boards of directors. Government grants may be a preferable alternative then, but these often are directed toward particular aims of those who hold governmental purse strings.
Thus, the third and final challenge is whether AI itself can be adapted to a non-capitalistic framework. The mathematical model all AI are built on requires transforming all values into quantities that can be calculated, leading easily to what Weber called instrumental rationality, wherein everything becomes estimated according to its use value.
Ultimately, the question of democratic AI remains open. Perhaps it will never be truly democratic, but the above parameters can serve as indicators of the democratic-compatibility of AI technologies
The Gonzaga Jewish Student Union Oneg Aviv and Living in your marginalized Identity becomes an Act of Resistance
No full textGiven the Prompt: Examine an overlooked story lead me and Spencer Rittenhouse to the Jewish Student Union on the Gonzaga University campus. We met with their President Tessa Divergilio and Vice President Peri Abrahm and attended the Oneg Aviv event held on campus. While attending, we where educated on the idea of how powerful it is for students to live in their marginalized identity on a campus where their culture is unintentionally silenced by the majority
Democracy Only Works for the Wealthy: Homelessness and Voting in Washington State
No full textAmerican representative democracy is a system of government in which elected officials represent the interests of the people, with authority derived from popular consent (Dahl, 2003). Financial standing should not disqualify American citizens from voting, nor should it heighten their influence over elections. Yet, in American society today, the wealthy’s votes, in the form of financial contributions, far outweigh those of others (Montanaro, 2016). Indeed, some of America’s poorest citizens may have no voice at all. As a result of a voter registration system that requires a mailing address, most of America’s unhoused citizens have difficulty registering to vote (VoteRiders, n.d.). In fact, only 1 in 10 unhoused people vote in elections (National Health Care for the Homeless Council, 2024). This project will explain the process of voter registration in the state of Washington showing how it excludes many of our poorest citizens and why this is consequential. Upholding democratic principles of representation and equality requires the full participation of eligible voters who are directly affected by law and policies. The project will conclude by 1) noting how Washington state has made positive changes to enable unhoused people to more easily vote, and 2) draw attention to the ways in which Washington State should continue to improve voting access for homeless and impoverished people to participate
Seasonal Affective Disorder: Interventions in the Digital Age
No full textResearch question: How does living farther from the equator impact mental and physical wellbeing
On Robert K. Greenleaf\u27s Archival Writings and Selected Published Works
No full textIJSL Associate Editor Jenny Song’s essay that follows, along with Robert K. Greenleaf’s four rare writings that are included here, provides an opportunity for me to share some thoughts on the Robert K. Greenleaf Archives, and on two of the five books of Greenleaf’s writings that I helped to curate and bring to the public some thirty years ago