Professional ethics and engineering responsibility notes — Unit 1

Ethics, derived from the Greek word ethos (character), is the philosophical study of moral principles—defining what is right versus wrong, and good versus evil. In the context of technology and engineering, it shifts from abstract philosophy to highly practical guidelines governing how technical professionals behave when their decisions impact public safety, privacy, and society.

⇒1.1 Morals vs. Ethics vs. Law

• Morals: Deeply held personal or religious beliefs about what is right and wrong. (Internal, highly subjective).
• Ethics: Socially or professionally accepted codes of conduct within a specific group. (External, objective within the profession).
• Law: Rules strictly enforced by the state with legal penalties. (External, universally enforced within a jurisdiction).

When engineers face a novel moral dilemma (e.g., 'Should an autonomous car swerve and hit one pedestrian to save five passengers?'), they rely on classical ethical frameworks to reason through the decision.

Why do we treat engineering ethics similarly to medical or legal ethics? Because engineering is formally recognized as a 'Profession'.

⇒3.1 Characteristics of a Profession

• Extensive Training: Requires a formal, specialized, and lengthy academic degree.
• Public Good: The primary purpose of the work is to serve the public interest (building safe bridges, securing public data).
• Autonomy: Professionals have a high degree of independence in how they execute their work.
• Self-Regulation: The profession governs itself through professional societies (e.g., IEEE, ACM) and formal codes of conduct.

Because society places immense trust in the technical competence of engineers (society cannot verify the safety of a bridge or the security of a banking algorithm themselves), engineers bear a disproportionate ethical burden to act honestly and safely.

Professional societies have codified ethical behavior into formal rules. The two most relevant for Computer Science are the ACM (Association for Computing Machinery) Code of Ethics and the IEEE (Institute of Electrical and Electronics Engineers) Code of Ethics.

⇒4.1 The ACM Code of Ethics (Key Principles)

• Contribute to society and human well-being: Acknowledging that all people are stakeholders in computing.
• Avoid harm: Preventing injury, privacy loss, or environmental damage caused by computing artifacts.
• Be honest and trustworthy: Do not make deceptive claims about system capabilities or manipulate data.
• Respect privacy: Only collect necessary data, protect it rigorously, and allow users control over their information.
• Honor confidentiality: Keep trade secrets and client data strictly private, unless required by law to disclose.

⇒5.1 Conflict of Interest

A conflict of interest occurs when a professional has a private or personal interest sufficient to influence the objective exercise of their official duties. Example: An IT manager is tasked with selecting a cloud vendor for the company, and they own significant stock in Amazon (AWS).

Resolution: Conflicts of interest are not inherently illegal, but they must be immediately and transparently disclosed to all affected parties. The individual should recuse themselves from the decision-making process.

⇒5.2 Whistleblowing

Whistleblowing is the act of a current or former employee disclosing illegal, immoral, or dangerous practices by their employer to the public or authorities. It is considered the ultimate ethical escalation when internal channels fail.

According to Richard DeGeorge's framework, whistleblowing is morally permissible if the harm is serious, the employee has reported it to superiors, and internal channels are exhausted. It becomes morally obligatory if the employee has documented evidence that would convince an impartial observer, and the disclosure will actually prevent the harm.

The rapid acceleration of Artificial Intelligence has introduced novel ethical dilemmas that traditional codes are struggling to cover.

• Algorithmic Bias: If an AI used for hiring or criminal sentencing is trained on historically biased data, it will mathematically institutionalize discrimination. The ethical burden is on the engineer to audit the training data.
• The 'Black Box' Problem: Deep Learning models often cannot explain why they made a decision. If a medical AI denies a patient a life-saving transplant, the lack of explainability violates the patient's right to understand the decision.
• Job Displacement: While automation increases overall economic efficiency (Utilitarianism), it devastates specific working-class demographics. Engineers must engage with the socioeconomic fallout of the tools they build.

This unit focuses on the transition from abstract philosophy to applied professional rules. Expect scenario-based questions.

• Theories: Be able to explicitly define Utilitarianism vs Deontology and apply them to a hypothetical scenario (e.g., 'Analyze the ethics of Edward Snowden's leaks using Utilitarianism').
• Whistleblowing: Memorize DeGeorge's criteria for when whistleblowing shifts from permissible to strictly mandatory.
• ACM/IEEE Codes: Memorize the first three fundamental principles of the ACM code (Avoid harm, Be honest, Respect privacy). You will frequently be asked to cite them to justify an answer.