I am writing an essay on how an Al may be controlled to favor certain political views, what is some information you can recommend as to how this can occur?

AI can be made to favor political viewpoints through the data it learns from, the instructions it receives, and the incentives used to shape its responses. This can happen deliberately, but it can also emerge unintentionally. For your essay, it helps to distinguish political bias in an AI’s output from evidence that someone intentionally engineered that bias.

How political preferences can enter an AI system

1. Selection of training data
   AI models learn patterns from large collections of text. If certain political perspectives, languages, countries, or news sources dominate that collection, the model may reproduce their assumptions. Deliberately including or excluding particular sources can influence this—but training data does not translate predictably into a model’s political “beliefs.”

2. Human feedback and fine-tuning
   Developers often ask human evaluators to compare answers, then train the model to produce responses similar to those rated more highly. If evaluators consistently prefer one political framing, that preference can become part of the model’s behavior. Even standards such as “reasonable,” “respectful,” or “balanced” can involve contested judgments.

3. System instructions and application design
   An application can give an AI instructions that users do not ordinarily see. These might direct it to adopt a particular perspective, emphasize certain values, or avoid specified subjects. Such instructions can influence answers without changing the underlying model, although their effects are not perfectly reliable.

4. Search results and information retrieval
   Some AI systems consult search engines or selected document collections before answering. Which sources are available—and how they are ranked—can affect the response. A system that retrieves only government statements, for example, may present a different account from one that also retrieves independent reporting and criticism.

5. Moderation and refusal policies
   Rules about what an AI may discuss can affect political debate if they are applied unevenly across viewpoints. However, a restriction on threats, harassment, or demonstrably false factual claims is not, by itself, evidence of partisan bias. The important questions are whether the rules are justified, transparent, and consistently applied to comparable cases.

6. Commercial or institutional incentives
   Governments, companies, advertisers, and other institutions may influence how a system is developed or deployed. Pressure to maximize engagement, protect a brand, or comply with local restrictions can shape political coverage even without an explicit partisan directive.

What political favoritism can look like

Bias is not limited to openly endorsing a party. It can appear through:

• Framing: describing similar actions as “reform” for one group and “radical change” for another.
• Omission: leaving out relevant arguments or evidence.
• Unequal scrutiny: demanding stronger evidence from one side.
• Source selection: repeatedly treating one ideological camp as more authoritative.
• Different refusal standards: answering equivalent requests differently depending on the viewpoint.

Useful sources and research approaches

• NIST’s AI Risk Management Framework (2023): useful for discussing bias, transparency, and accountability.
• Bender et al., “On the Dangers of Stochastic Parrots” (2021): examines risks arising from large language models and their training data.
• Ouyang et al., “Training language models to follow instructions with human feedback” (2022): explains a major technique for shaping model behavior; it is not itself evidence of political manipulation.
• Model cards and system cards: document developers’ stated goals, evaluation methods, and known limitations.

For evaluating claims, look for studies using multiple prompts, repeated trials, and matched scenarios—not just a single political quiz or screenshot.

Possible thesis: “Political favoritism in AI can arise throughout development and deployment, so assessing it requires examining not only individual answers but also data selection, feedback processes, information sources, and institutional incentives.”