The Problem with AI and Techno-Solutionism: A Case for Positive Sum Design | by Nicole Cacal | Medium

The Problem with AI and Techno-Solutionism: A Case for Positive Sum Design

Nicole Cacal

8 min read

Oct 25, 2023

Credit: Creativa Images

Are we inadvertently glorifying tech at the cost of overshadowing human values? There’s no doubt that AI is reshaping how we live and work. But with all this progress, there’s a growing mindset that’s a bit worrisome: “techno-solutionism.” It’s the idea that just by throwing technology at problems, we can solve them — overlooking the messy, human aspects that make these issues complex in the first place.

Enter the term “Ethical AI.” At first glance, it’s like a seal of approval — a promise that our smart tech will always have our best interests at heart. But when we dig a bit deeper, some cracks start to show. By framing AI as “ethical,” we risk implying that the technology itself possesses intrinsic moral values. This could lead us into a potential trap where we’re putting tech on a pedestal, all while sidelining the complex interplay of societal, cultural, and human factors that should be at the core of any ethical discussion. The exploration behind techno-solutionism and Ethical AI was inspired by my conversation with Jenny Davis, author of “How Artifacts Afford,” and numerous articles like Algorithmic Reparation.

However, there’s a silver lining: Positive Sum Design. Far from the tunnel vision of techno-solutionism, this approach champions a balance. It’s not just about making tech smarter; it’s about ensuring that AI augments human needs and values. Think of it as a collaboration where technology and humanity come together, creating an environment where everyone benefits. It pushes us to envision a world where the relationship between humans and AI is synergistic, rather than adversarial or one-sided.

Let’s untangle the pitfalls of techno-solutionism, take a closer look at the implications of “Ethical AI,” and spotlight the transformative potential of Positive Sum Design in guiding AI towards a more harmonious coexistence with humanity.

Navigating the Semantics: “Ethical AI” vs. “Responsible AI” & “AI Ethics”

The term “Ethical AI” is rapidly gaining traction in tech circles and beyond. But what does it really mean? At its core, it suggests a form of AI that operates according to ethical standards or principles. Yet, inherent in this term are techno-solutionist undertones which, rather than emphasizing the integration of human ethical deliberation into AI processes, seem to hint at AI’s capability to inherently be ethical.

It’s an alluring notion: machines that always act with moral rectitude. However, by branding AI as “ethical,” there’s an implication that technology can possess intrinsic moral values. This perspective potentially oversimplifies the intricate balance of embedding ethics into technology. Ethical decisions are often deeply contextual, nuanced, and human-centric — qualities that cannot be wholly distilled into algorithms.

As alternatives to the “Ethical AI” narrative, we have the terms “Responsible AI” and “AI Ethics.” “Responsible AI” pertains to the deliberate design and use of AI in a way that it aligns with human values and societal norms, emphasizing human oversight. On the other hand, “AI Ethics” is a broader domain, encompassing the study of the moral implications and challenges posed by AI. Both terms pivot away from the idea of inherent AI morality and underscore the importance of human involvement and reflection.

Rather than isolating AI as an independent ethical entity, it’s important to position it within a broader system that includes human values, societal norms, and ethical considerations. AI is a tool — one that’s molded, directed, and refined by humans. Its ethical dimension is a direct reflection of human intent, oversight, and continuous evaluation.

At the heart of both “Responsible AI” and “AI Ethics” is the assertion of human accountability. AI doesn’t exist in a vacuum; it’s birthed from human innovation and is constantly fed by human data and interactions. Recognizing the primacy of human responsibility ensures that AI systems are used judiciously, are under constant scrutiny, and are designed with the greater good in mind.

Unraveling Human-Centric Problems with AI

The Black Box Dilemma: AI, especially deep learning models, often operates as a “black box.” This means that while we can see the input and output of the system, understanding the computations that lead to a specific decision is often elusive. This opacity presents challenges in accountability, trust, and transparency. Without a clear understanding of how decisions are made, it becomes problematic to identify errors or biases or even to explain the logic to stakeholders.

The Bias Challenge: AI learns from data. If this data carries historical or societal biases, the AI system can inadvertently perpetuate, or even amplify these biases. From facial recognition systems that struggle to recognize certain ethnic groups to algorithms that display gender or racial bias in hiring or lending, these challenges underscore the need for rigorous oversight, diverse training datasets, and continual monitoring.

Loss of Human Agency: There’s a real concern that as AI systems become more integrated into our daily lives, human agency — the ability to make decisions, to act independently, and to exercise free will — might diminish. Over-reliance on AI recommendations in areas like healthcare or financial decision-making can sideline human judgment, leading to scenarios where critical decisions are made passively, relying solely on machines.

Ethical Ambiguities: AI doesn’t have emotions, conscience, or moral instincts. When AI’s decisions collide with human ethical boundaries, dilemmas arise. For example, consider autonomous vehicles faced with split-second decisions that could impact human lives. How should the car’s AI prioritize safety? These scenarios show the limits of programming ethics into AI and emphasize the need for human oversight.

Five colorful arrows pointing “up.”

Positive Sum Design: Instilling Hope Against Techno-Solutionism

Positive Sum Design isn’t just a design philosophy — it’s a fundamental shift in how we approach technology. At its heart lies the belief that we can design systems to create greater aggregate value for everyone involved, rather than winners and losers. This perspective is rooted in the idea of fostering enhanced awareness, emphasizing communication, coordination, and collaboration between all stakeholders. Its overarching goal? To produce technology that respects and understands human needs and values while ensuring broad benefits.

Positive Sum Design emphasizes creating conditions that amplify mutual value, especially in situations traditionally viewed through limited or biased perspectives. By establishing trust and enhancing communication, it paves the way for outcomes where everyone benefits, like the dynamics in the sharing economy where platforms like Airbnb and Uber employ reputation as a form of currency. Drawing inspiration from the Stag Hunt scenario widely known in Game Theory, the design illustrates that when individuals coordinate and trust one another, they can reap greater shared benefits, as opposed to settling for individual but lesser gains. Positive Sum Design advocates for shifting from a competitive zero-sum approach to one of collaborative abundance, urging us to see beyond immediate constraints and recognize the potential for collective benefit.

One of the standout features of Positive Sum Design is its insistence on viewing AI within a wider panorama of societal, ethical, and human considerations. Instead of seeing technology as an isolated entity, this approach positions AI as a component in a larger ecosystem, interconnected with diverse human experiences, values, and needs. By acknowledging this web of interactions, Positive Sum Design promotes solutions that are both technologically sound and human-centric.

The tech-first mindset — believing technology, in isolation, can be the silver bullet to societal problems — is a defining characteristic of techno-solutionism. Positive Sum Design boldly challenges this. It redirects focus from the allure of shiny new tech solutions to a more grounded, all-encompassing perspective that weighs human implications equally, if not more. By promoting designs that prioritize human values and ethical considerations, Positive Sum Design offers a fresh, balanced, and more promising path forward in the realm of AI development. More on Positive Sum Design here.

Positive Sum Design Case Studies of Impact

Communication Enhancement: The city of Reykjavik, Iceland introduced My Neighborhood, a digital platform powered by AI, allowing citizens to propose initiatives and have a direct say in city governance. By enhancing civic participation, the platform ensures that policies align more closely with residents’ needs and aspirations.

Abundance Over Scarcity: “Spoonshot” is a food & beverage intelligence platform that uses AI to analyze over 28,000 sources (social networks, research articles, consumer reviews, press articles, patent applications, distributor data, etc.) to provide the most accurate data possible on how people eat. Using this data, they aim to help considerably reduce food waste. This also enables manufacturers to make better choices when purchasing raw materials, and is also a way of reducing their costs and carbon footprint.

Empowering Underrepresented Voices: AI has the potential to democratize information and give voice to those often sidelined. A case in point is the Montreal AI Ethics Institute and Microsoft initiative that harnesses machine learning to amplify stories from marginalized communities, making their narratives a central part of the wider discourse. Such initiatives showcase how Positive Sum Design can be employed to create platforms that are both technologically advanced and socially impactful.

AI for Inclusive Healthcare: When harnessed ethically, AI can be a potent tool for societal betterment. Organizations like Cityblock Health leverage AI to tackle pressing challenges, ensuring that their solutions are ethically sound, sustainable, and considerate of diverse stakeholder needs. They “deliver medical care, behavioral health, and social services to individuals from historically underserved and marginalized communities, in particular those with complex needs.” These show how Positive Sum Design principles can be brought to life to create lasting positive impact.

AI for Climate Action: “dClimate” is a company that is building data infrastructure that powers a decentralized climate information ecosystem. They use AI to:

Bridge data voids (estimate missing or incomplete data, offering a richer understanding of climate patterns)
Unify data (satellite imagery, on-the-ground measurements, and model forecasts)
Verify data (scrutinize climate data, spotting and rectifying anomalies, deviations, and inconsistencies)
Advance climate forecasts (enhancing the precision of climate projections)

By integrating AI-centric capabilities, dClimate opens doors to heightened climate resilience and sustainable expansion.

It’s more vital than ever to strike a balance — one where technical advancements synchronize seamlessly with human values and ethics. Positive Sum Design epitomizes this philosophy. It doesn’t merely challenge the prevalent zero-sum mentality but rather champions the idea that complex problems often have ways forward that benefit all parties involved. Positive Sum Design promotes a transition from a zero-sum competitive mindset to one that embraces collaborative abundance, encouraging us to look past immediate limitations and envision a scenario of shared advantage. By reframing our approach, we not only stand to create more value for everyone, but also foster a landscape where ethics and technology coalesce, enriching one another.

Yet, this isn’t the task of technologists alone. The roadmap to a future where AI seamlessly integrates into our lives, augmenting rather than overshadowing our abilities, requires a collective effort. Policymakers, stakeholders, designers, and everyday users must come together in open dialogue, co-creating frameworks and guiding principles. It’s through this collaboration that we can steer AI development in a direction that upholds human dignity, autonomy, and values.

Let’s envision a future where AI systems are designed, developed, and deployed with a singular, unwavering focus: to enrich the human experience, to augment our capabilities, and to uphold the values that bind us together. The time to ensure a human-centric AI future is now.