Comparative book review of The ethical algorithm: the science of socially aware algorithm design by Michael Kearns and Aaron Roth and debiasing AI: rethinking the intersection of innovation and sustainability by Donghee Shin

Abstract This comparative book review examines The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Michael Kearns and Aaron Roth and Debiasing AI: Rethinking the Intersection of Innovation and Sustainability by Donghee Shin to illuminate two dominant yet divergent approaches to AI ethics: computational formalization and sociotechnical governance. Kearns and Roth conceptualize ethical challenges as bounded, technically tractable problems that can be addressed through mathematical formalization and embedded constraints. Their framework positions ethics within algorithmic systems, emphasizing measurability, trade-offs, and enforceability. In contrast, Shin conceptualizes bias and ethical harm as emergent properties of dynamic sociotechnical systems shaped by human cognition, institutional practices, and cultural contexts. His approach shifts the focus from solving discrete problems to governing evolving systems through interdisciplinary, human-centered, and sustainability-oriented strategies. The review suggests that these approaches are not mutually exclusive but represent complementary layers of ethical AI. Effective ethical intervention requires integrating technical design with sociocognitive insight and institutional accountability. Ultimately, the findings suggest that sustainable AI ethics must bridge the gap between formal algorithmic constraints and broader systems of governance, recognizing ethics as both a design challenge and an ongoing societal process.