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Technology and Ethics Cheat Sheet

The core ideas of Technology and Ethics distilled into a single, scannable reference — perfect for review or quick lookup.

PiqCue — piqcue.com/technology-and-ethics/cheatsheet

Quick Reference

Algorithmic Bias

Systematic and unfair discrimination embedded in algorithmic systems, arising from biased training data, flawed design assumptions, or the amplification of existing social inequalities. Biased algorithms can produce discriminatory outcomes in hiring, lending, criminal justice, and healthcare without explicit discriminatory intent.

Informed Consent in the Digital Age

The principle that individuals should be fully informed about and freely agree to the collection, use, and sharing of their personal data. In practice, lengthy terms of service, opaque data practices, and the near-impossibility of opting out of digital services challenge the meaningfulness of consent.

Value-Sensitive Design (VSD)

A design methodology that accounts for human values throughout the technology design process. It involves conceptual investigation of stakeholder values, empirical investigation of how values are affected by technology, and technical investigation of how design choices support or undermine those values.

The Trolley Problem and Autonomous Vehicles

The application of the classic trolley problem to autonomous vehicle programming: when a crash is unavoidable, how should the vehicle be programmed to choose between different harmful outcomes? This raises questions about the programmability of moral decisions and whose values are encoded.

Surveillance Ethics

The ethical analysis of monitoring systems, including government surveillance, corporate data tracking, facial recognition, and workplace monitoring. Central concerns include the balance between security and privacy, the chilling effect on free expression, and the disproportionate impact on marginalized communities.

Digital Privacy

The right of individuals to control their personal information in digital environments, including what data is collected, how it is used, who has access, and how long it is retained. Privacy is considered both an individual right and a societal good that supports autonomy and democratic participation.

Explainability and Transparency in AI

The principle that AI systems making consequential decisions should be interpretable and their reasoning understandable to those affected. Black-box models that cannot explain their outputs raise accountability concerns, particularly in high-stakes domains like criminal justice and healthcare.

Responsible Innovation

A framework that integrates ethical reflection, inclusive deliberation, and anticipation of social impacts into the research and development process. It aims to align innovation with societal values and needs rather than treating ethics as an afterthought.

Digital Autonomy

The capacity of individuals to make free, informed decisions about their engagement with digital technologies, including what information they consume, how their data is used, and how algorithmic systems influence their choices. Persuasive design and dark patterns can undermine digital autonomy.

Dual-Use Technology

Technology that can be used for both beneficial and harmful purposes, creating ethical dilemmas about its development and distribution. The dual-use problem is central to debates about AI, biotechnology, encryption, and drone technology.

Key Terms at a Glance

Algorithmic Bias:Systematic discrimination in the outputs of algorithmic systems resulting from biased data, flawed design, or amplification of existing social inequalities.
Autonomous Weapons:Weapon systems capable of selecting and engaging targets without human intervention, raising profound ethical and legal questions about accountability.
Care Ethics:An ethical framework that emphasizes relationships, vulnerability, and the responsibility to care for those affected by our actions, particularly the most vulnerable.
Dark Patterns:User interface design techniques that manipulate or deceive users into making choices they did not intend, undermining autonomy and informed consent.
Data Minimization:The principle of collecting only the minimum personal data necessary for a specified purpose, reducing privacy risks.
Deontological Ethics:An ethical framework that evaluates actions based on adherence to rules, duties, and rights rather than consequences.
Digital Autonomy:An individual's capacity to make free and informed decisions about their engagement with digital technologies and data.
Digital Privacy:The right to control personal information in digital contexts, including collection, use, sharing, and retention of data.
Dual-Use Technology:Technology that can serve both beneficial and harmful purposes, creating dilemmas about its development and distribution.
Ethical Impact Assessment:A systematic evaluation of the potential ethical risks and societal impacts of a technology before it is deployed.
Ethics Washing:The practice of using superficial ethics rhetoric without substantive changes to harmful practices, as a public relations strategy.
Explainability (XAI):The capability of an AI system to provide human-understandable explanations of its decisions and reasoning processes.
GDPR:The General Data Protection Regulation, an EU law establishing comprehensive data protection rights and obligations for organizations handling personal data.
Human-in-the-Loop:A design principle ensuring that humans retain meaningful decision-making authority in automated systems, especially for high-stakes decisions.
Informed Consent:The principle that individuals should understand and voluntarily agree to the terms under which their data is collected and used.

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Technology and Ethics Cheat Sheet - Quick Reference Guide | PiqCue