Chapter 1: The Quest for Understanding AI

In 2018, I presented my PhD proposal at the University of Waterloo's Philosophy Department, but it was ultimately turned down. My focus was on examining human agency in user interface design and AI technologies, aiming to empower users often constrained by machine limitations. I intended to anchor my research in philosophy, which has evolved into what we now generally refer to as Tech Ethics, all viewed through the lens of human agency in human-computer interaction (HCI).

I vividly recall a meeting with an elderly female professor who seemed unable to grasp my explanation. I suspect the rejection stemmed from a generational gap among the philosophy faculty regarding the essential role they could play in this technological era.

Shortly after, I applied to the Digital Arts Communication (DAC) program at the same university, now known as the Experimental Digital Media (XDM) program. This was the same place where I had completed my Master’s nearly two decades prior, merging my passions for technology and language. The XDM program offered an exciting blend of rhetoric, philosophy, computation, and user interface design, all centered around human agency.

Ironically, I encountered that same philosophy professor on a panel discussion later, and her lack of clarity regarding AI was striking, contrasting sharply with her previous assertions about my unsuitability for the Philosophy department.

My career journey began post-Master’s with my project titled "Screen-based Controls for GUI." With a strong passion for storytelling, I once dreamed of writing for games, though I ended up contributing to Autodesk's Maya — a pivotal moment that allowed me to delve deeper into design.

While raising my two sons, I reflected on my career and decided to return to academia, merging my professional journey with personal growth.

This first video, "Decoding AI: A Go Programmer's Perspective," offers insights into how AI-powered solutions make decisions that significantly affect humans.

Chapter 2: The Intersection of AI and Design

As a Design leader for Maya, I discovered the intriguing realm of Artificial Intelligence (AI) and Machine Learning (ML), particularly within procedural design generation — now commonly referred to as Generative AI. My team taught Maya to differentiate between a range of materials, enabling the software to create designs based on specific prompts and innovative coding.

The dynamics of this process remind me of a humorous scene from "Silicon Valley," where a character develops an app to identify hot dogs. This comedic take resonates with broader themes of startup culture and technology innovation.

Our design challenges paralleled this innovation. We conceptualized a dynamic library of materials, allowing automatic population and randomization in our designs. Imagine the efficiency of a bookshelf filled with books of varying appearances without the tedious task of modeling each individually.

We also launched Maya's first cloud instance in 2016, marking a significant step in our design evolution.

The second video, "The Quick AI Decode: What You Need to Know," explores the implications of AI in design and how it reshapes user experiences.

Chapter 3: The Evolving Role of Design

The shift towards modular UI components raises questions about the necessity of human designers. While software can generate components automatically, this view overlooks the essence of design as a fundamentally human-centered process. It's about crafting experiences, understanding user needs, and infusing creativity into interactions.

My contemplation of leaving design was compounded by the draining commute from my quiet town to Toronto, pushing me to reflect on work-life balance. This theme resonated deeply with many, as I shared personal anecdotes in my most-read posts on Medium.

My interest in design transcends aesthetics; I am driven to understand how automated systems impact our lives. I investigate what inputs lead to certain outcomes and how these processes influence humanity. This curiosity pushes me to explore the relationship between technology and user experience, questioning the algorithms that shape our digital landscape.

As I transitioned into computational rhetoric, I aimed to examine AI systems critically. I posed the question: "What is in the AI stew?" from both technical and rhetorical angles.

Chapter 4: The Challenges of AI Ethics

AI presents capabilities once seen only in science fiction, but it also necessitates addressing the societal implications of these technologies. Embedded bias in AI systems, often drawn from historical data, can perpetuate existing societal inequalities, particularly gender bias.

Digital profiling risks create a framework for discrimination, often affecting marginalized communities. AI can act as a gatekeeper, maintaining entrenched power dynamics rather than promoting equitable change.

The legal and ethical implications of AI in judicial contexts demand constant vigilance to ensure justice remains balanced. Transparency and accountability are crucial for demystifying AI operations and preventing undue influence over users' lives.

AI's societal impact is profound; its deployment necessitates thoughtful management of its influence on cultural norms. Inclusive design must be prioritized in AI development to ensure diverse human experiences are respected and represented.

Ultimately, AI must align with the core values of fairness, transparency, and inclusivity to foster a just society.

Chapter 5: The Future of AI and Society

The increasing reliance on algorithms in daily life introduces the issue of bias amplification. Historical data often underpins these algorithms, risking the continuation of outdated biases.

Without deliberate efforts to integrate gender equity into their design, algorithms may systematically disadvantage marginalized genders. Transparency in algorithmic processes is essential for identifying and rectifying these biases.

The digital landscape must not only promote technological advancements but also uphold the principles of justice and equality. Ensuring that women and marginalized communities are represented in algorithmic development is crucial to prevent regression into digital oppression.

In the broader context of digital evolution, understanding AI’s "recipe" is vital for safeguarding our collective future. As AI increasingly shapes human progress, we must engage actively with these technologies to ensure they enhance rather than harm society.

By fostering an environment that values equity and accountability, we can embrace the benefits of AI without regretting its implications. Let us ensure that the innovations emerging from AI are nourishing and equitable for our diverse global community.

References

Robert-Houdin, J. E., & Hoffmann, L. (2011). Secrets of Conjuring and Magic: Or How to Become a Wizard (p. 43). Cambridge University Press.

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