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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAaron Burciaga proposes five virtues for data science practitioners and the AI systems they design: resilience, humility, grit, liberal education, and empathy. His framework is a normative guide to making deliberate choices—not an empirical study showing that these virtues improve model performance or other outcomes.
Burciaga uses “quants” broadly to mean data scientists, AI and machine-learning engineers, statisticians, data miners, and people in related roles. He argues that practitioners should cultivate these qualities and consider how they are reflected in automated processes, data systems, and recommender systems. The responsibility is not something to hand off to a model: as Burciaga puts it, “A machine will not, and in fact cannot, do this of its own accord.” (O’Reilly chapter preview)
What are the five virtues?
Resilience: explore and adapt
Resilience means adapting to changing circumstances and recovering quickly when an approach falls short. In practice, Burciaga’s proposal is to explore feasible parts of the solution space, account for local constraints, and avoid stopping at the first workable idea. That means asking whether a solution remains suitable as conditions change, rather than treating an initial design as final.
Humility: take responsibility and recognize limits
Humility calls for owning the consequences of a system, continuing to learn, and acknowledging what cannot be known or controlled. It is a discipline for the people building and deploying AI. Burciaga mentions reinforcement learning in connection with continued adaptation, but this does not mean that a machine thereby possesses human humility.
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Grit: prioritize useful, accountable work
Grit is sustained attention to productive work instead of becoming absorbed in a problem’s elegance or pursuing an imagined perfect solution. Burciaga connects this focus with results that can be audited and interpreted. A practical implication is to value an approach whose decisions can be examined over one that is merely sophisticated but difficult to explain.
Liberal education: bring broad, critical judgment
Here, liberal education means welcoming complexity, diversity, and change—not relying on a single technical lens. Practitioners should examine the business problem and its data critically, consider feasible methods, and document decisions clearly. Good documentation helps others understand how a solution was reached and supports accountability.
Empathy: account for people and social impact
Empathy means recognizing that systems affect people and that those people have feelings, needs, and different circumstances. Burciaga proposes shaping objectives or constraints with understanding and compassion, while noticing how people and systems depend on one another. That makes social impact part of the design question, rather than an afterthought.
How should practitioners apply the framework?
The virtues become more useful when translated into choices in a project. Burciaga’s proposal points toward practices such as:
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- Explore feasible scenarios and account for local constraints before settling on a solution.
- Take responsibility for results and keep learning as the system and its context change.
- Prefer productive work and make results auditable and interpretable.
- Examine the problem and data critically, and document decisions clearly.
- Consider social impact and reflect human needs in objectives or constraints.
These are ethical prompts for people making design decisions. They do not, by themselves, specify a technical control, guarantee that a system is fair, or replace context-specific evaluation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the five virtues do—and do not—establish
The framework is an ethical argument about how practitioners should act and what qualities they should deliberately reflect in systems. The title-specific chapter does not report an experiment, effect size, or measured outcome validating the five-virtue framework. It should therefore be read as Burciaga’s prescriptive proposal, not as a scientifically tested method or an established industry standard.
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The exact-title contribution is chapter 87 of 97 Things About Ethics Everyone in Data Science Should Know, edited by Bill Franks and published by O’Reilly in August 2020. The book is a 344-page collection on data-science ethics, not a book devoted solely to these five virtues. (O’Reilly book record)
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